Search

Simple Summary: There is limited evidence on the mortality reduction effect of colorectal cancer (CRC) screening using a fecal immunochemical test (FIT) from long-term follow-up nationwide cohort data. This nested case-control study demonstrated that the annual FIT conducted through the Korean National Cancer Screening Program reduced CRC-specific mortality in Koreans aged 50 to 74. Additionally, screening frequency and time interval also affected CRC-specific mortality reduction. Therefore, it is proposed that countries planning to implement national CRC screening programs consider the appropriate age for FIT. Background: Whether colorectal cancer (CRC) screening with a fecal immunochemical test (FIT) reduces mortality remains unclear. In South Korea, CRC screening with a FIT for individuals aged ≥ 50 years has been part of the Korean National Cancer Screening Program (KNCSP) since 2004. The aim of this study was to evaluate the effectiveness of the KNCSP in reducing CRC-specific mortality. Methods: We conducted a nested case-control study using cohort-based data derived from the KNCSP database. The cohort included 5,944,540 colorectal cancer-free individuals aged ≥ 50 years as of 2004. Individuals who died after CRC diagnosis were defined as cases (n = 29,992) and their sociodemographic characteristics were matched to those of the selected controls. The effects of screening exposure, frequency, and time interval on CRC-specific mortality were analyzed according to age group. Conditional logistic regression analysis was performed. Results: Compared with individuals who had never been screened, those who had ever been screened showed an OR of 0.74 (95% CI, 0.71–0.76) for CRC-specific mortality. CRC-specific mortality decreased as the number of screenings increased. Similar results were observed for those aged 50–79 years; however, the results for those aged 75–79 years were not statistically significant. Moreover, those aged ≥ 80 years had the opposite results. Conclusions: CRC mass screening using FIT is effective for individuals aged 50–74 years; therefore, this study suggests that countries considering introducing national CRC screening implement FIT for those within this age range. [ABSTRACT FROM AUTHOR]

Simple Summary: Lip and oral cavity cancer caused over 177,000 deaths globally in 2020, but patient survival increases with earlier diagnosis. One barrier to early diagnosis is the invasive nature of biopsies needed for diagnosis. Automated diagnosis systems have the potential to perform non-invasive diagnosis by pairing novel imaging data with deep learning models. Given the variability between patients, access to a sufficiently large training database from human subjects limits deep learning applications. We propose a model that maps non-invasive images of oral tissue to a diagnosis by encouraging the model to group normal samples close together (reducing variability between patients). Our model improves non-invasive oral cancer diagnosis through a robust training process that only requires a small amount of data. This work shows how we can address small data challenges through model architecture and training, rather than through the collection of larger databases or manual corrections and normalizations. Background: Multispectral autofluorescence lifetime imaging systems have recently been developed to quickly and non-invasively assess tissue properties for applications in oral cancer diagnosis. As a non-traditional imaging modality, the autofluorescence signal collected from the system cannot be directly visually assessed by a clinician and a model is needed to generate a diagnosis for each image. However, training a deep learning model from scratch on small multispectral autofluorescence datasets can fail due to inter-patient variability, poor initialization, and overfitting. Methods: We propose a contrastive-based pre-training approach that teaches the network to perform patient normalization without requiring a direct comparison to a reference sample. We then use the contrastive pre-trained encoder as a favorable initialization for classification. To train the classifiers, we efficiently use available data and reduce overfitting through a multitask framework with margin delineation and cancer diagnosis tasks. We evaluate the model over 67 patients using 10-fold cross-validation and evaluate significance using paired, one-tailed t-tests. Results: The proposed approach achieves a sensitivity of 82.08% and specificity of 75.92% on the cancer diagnosis task with a sensitivity of 91.83% and specificity of 79.31% for margin delineation as an auxiliary task. In comparison to existing approaches, our method significantly outperforms a support vector machine (SVM) implemented with either sequential feature selection (SFS) (p = 0.0261) or L1 loss (p = 0.0452) when considering the average of sensitivity and specificity. Specifically, the proposed approach increases performance by 2.75% compared to the L1 model and 4.87% compared to the SFS model. In addition, there is a significant increase in specificity of 8.34% compared to the baseline autoencoder model (p = 0.0070). Conclusions: Our method effectively trains deep learning models for small data applications when existing, large pre-trained models are not suitable for fine-tuning. While we designed the network for a specific imaging modality, we report the development process so that the insights gained can be applied to address similar challenges in other non-traditional imaging modalities. A key contribution of this paper is a neural network framework for multi-spectral fluorescence lifetime-based tissue discrimination that performs patient normalization without requiring a reference (healthy) sample from each patient at test time. [ABSTRACT FROM AUTHOR]

Simple Summary: This article discusses the potential benefits and limitations of oral cancer screening. While early detection can be beneficial, there is limited evidence that screening programs significantly reduce deaths from oral cancer. The issue lies partly in how the effectiveness of screening programs is measured, with studies showing that higher detection rates do not necessarily translate to fewer deaths. The review highlights the importance of critical evaluation of screening programs and the need for improved dentist education and technology to reduce the burden of oral cancer. The authors advocate for a balanced, evidence-based approach to integrate screening into broader oral health strategies. Screening is widely believed to facilitate early detection and improve health outcomes; however, recent evidence questions its overall benefits, highlighting the need for a critical assessment of cancer screening programs. The simplicity of clinical oral examination and the easy accessibility of the oral cavity make oral cancer a suitable candidate for opportunistic screening. Despite this uniqueness, studies that solidly assess the efficacy and cost-effectiveness of oral cancer screening are scanty, and the only large randomized trial undertaken so far shows limited evidence of mortality reduction through population screening. Research indicates that while early detection rates following screening may increase, they do not necessarily translate into decreased oral cancer deaths. This article emphasizes the need for a nuanced understanding of the benefits of screening, awareness of deceptive metrics and inherent biases in the context of early detection, and the importance of improving dentist education and introducing new technologies to enhance clinical decision-making. The review calls for a balanced and evidence-based approach to oral cancer screening, integrating it with broader oral health initiatives to maximize public health outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Many breast cancers do not respond to immune checkpoint inhibitor therapy, even when combined with the standard of care chemotherapy. Here, we sought to identify compounds that enhance T-cell-mediated toxicity in tumor cells using a high-throughput screen of known anti-cancer drugs. Beginning with a library of over 400 FDA-approved or investigational compounds, we chose four chemotherapy agents for detailed mechanistic follow-up. We describe potential immunostimulatory properties of paclitaxel, bleomycin sulfate, ispinisib, and etoposide. Ultimately, these studies provide insight into the potential immunostimulatory properties of the chosen follow-up compounds and suggest avenues for future investigations into optimizing chemotherapy partners to use with ICI. Background: Triple-negative breast cancers (TNBCs) typically have a greater immune cell infiltrate and are more likely to respond to immune checkpoint inhibition (ICI) than ER+ or HER2+ breast cancers. However, there is a crucial need to optimize combining chemotherapy strategies with ICI to enhance overall survival in TNBC. Methods: Therefore, we developed a high-throughput co-culture screening assay to identify compounds that enhance CD8+ T-cell-mediated tumor cell cytotoxicity. Over 400 FDA-approved compounds or agents under investigation for oncology indications were included in the screening library. Results: Four chemotherapy agents were chosen as priority hits for mechanistic follow-up due to their ability to enhance T-cell-mediated cytotoxicity at multiple doses and multiple time points: paclitaxel, bleomycin sulfate, ispinesib, and etoposide. Lead compounds affected the expression of MHCI, MHCII, and PD-L1 and induced markers of immunogenic cell death (extracellular ATP or HMGB1). Conclusions: Based on the ability to increase tumor cell susceptibility to T-cell-mediated cytotoxicity while minimizing T-cell toxicity, bleomycin was identified as the most promising lead candidate. Overall, the results of these studies provide mechanistic insight into potential new chemotherapy partners to enhance anti-PD-1 efficacy in TNBC patients. [ABSTRACT FROM AUTHOR]

Simple Summary: Self-sampling for HPV primary screening is recognized as a viable alternative to clinician-sampling. Its use in an Italian cervical cancer prevention program, focusing on acceptance, ease of use, and follow-up adherence, was evaluated. Self-sampling was offered to 19,327 women aged 30–64, overdue for screening due to the COVID-19 pandemic; they had never or irregularly attended. Results showed 11.5% of women chose self-sampling, more than doubling the participation rate compared to clinician-sampling alone (<5%), showing high acceptance. The return rate of self-samples was high (79.5%), with 1.1% being inadequate, indicating ease of use. HPV positivity was higher in the self-sampled group (13.1%) compared to the 2019 ordinary screening population (9.9%). Compliance with follow-up procedures exceeded 90%, and cervical cancer detection rates were higher (0.9‰) than in the routine screening population (0.4‰). Self-sampling was therefore effective in reaching non-attenders, but clinician-sampling remains important, as 6% of women opted for a clinician appointment. Background/Objectives: Self-sampling is recognized as a viable alternative to clinician-sampling for HPV primary screening. This study aimed to assess, within an Italian organized cervical cancer screening program, the acceptance and ease of use of self-sampling and the adherence to follow-up. The prevalences of HPV infection, cervical dysplasia, and cancer were contextually evaluated. Methods: Electronic records of 19,327 women, 30–64 years-old, residing within the Bologna Local Health Authority territory, who were offered self-sampling as an alternative to clinician-sampling, were retrospectively reviewed. They had never or irregularly attended and were overdue for a screening invitation due to the COVID-19 pandemic. An opt-in approach was adopted, involving local pharmacies for kit delivery and sample collection. Initially, HPV-positive results led to direct referral to colposcopy; later, cytological triage on clinician-samples was provided. Results: Self-sampling reached over twice as many women (11.5%) compared to historical clinician-sampling alone (<5%), showing high acceptance. Additionally, a high screening completion level was observed, with 79.5% of self-samples returned to pharmacies. A low percentage of self-samples resulted in inadequate results (1.1%), suggesting the method's ease of use. HPV-positivity was 13.1%, higher than the 9.9% recorded in the ordinary screening population in 2019 (p < 0.001), the last year before the pandemic. Compliance to both immediate colposcopy and cytology triage exceeded 90% (p = 0.675). The rate of cervical adenocarcinoma was twice as high as in the routinely screened population in 2019 (0.9‰ versus 0.4‰). Finally, 6% of women opted for clinician appointments. Conclusions: Self-sampling proved to be an easy-to-use and effective tool for reaching non-attenders, who are at high risk of cancer. Cytology triage on clinician-samples did not negatively impact follow-up adherence. It seems appropriate to maintain a clinician-collection option even among non-attenders. [ABSTRACT FROM AUTHOR]

Simple Summary: This article explores the molecular pathways involved in lung cancer, alongside genomic alterations and genetic syndromes associated with the disease. It also reviews testing methods and their role in advancing personalized treatment approaches, offering insights into current therapeutic strategies. This article provides a comprehensive overview of lung cancer from a genetic perspective, emphasizing the molecular pathways involved in its pathogenesis. This review presents the genetic syndromes and key genomic variations, including alterations and instabilities, contributing to tumor development and progression. Furthermore, advancements in genomic testing, such as liquid biopsy, are explored, highlighting their utility in early diagnosis, prognostic evaluation, and personalized treatment strategies. The role of biomarkers in informing therapeutic decisions and optimizing patient outcomes is also examined, offering insights into their potential to refine clinical interventions. This review aims to enrich the understanding of the genetic landscape of lung cancer, providing insights that may lead to improved diagnostic and therapeutic approaches. [ABSTRACT FROM AUTHOR]

Simple Summary: Mucinous epithelial pancreatic cystic lesions (PCLs) are premalignant lesions detectable through imaging techniques; however, distinguishing them from other PCLs with lower malignancy potential is challenging. Current methods like biochemical markers and genomic studies are not always reliable. Thermal liquid biopsy (TLB) is an innovative tool that analyzes the thermal profile of biological samples to detect disease-related alterations. In a retrospective study of 35 intracystic fluid samples obtained via fine needle aspiration, predictive models were developed using machine learning algorithms. Two classification models were created: TLB1, which differentiates mucinous from non-mucinous PCLs, demonstrating 92% sensitivity and 86% negative predictive value, and TLB2, which identifies benign and malignant mucinous lesions, achieving an area under the curve of 1.00. TLB shows promise in improving the differential diagnosis of PCLs and in detecting malignant transformations. Background/Objectives: Mucinous epithelial pancreatic cystic lesions (PCLs) are premalignant lesions readily detectable through imaging techniques such as multidetector computed tomography, magnetic resonance imaging, and endoscopic ultrasound (EUS). However, distinguishing these from other PCLs with lower or no malignant potential, and the early identification of those undergoing malignant transformation, remains a diagnostic challenge. Current methods, including biochemical markers in intracystic fluid (ICF) and genomic studies, offer some assistance but are not always reliable or accessible. Thermal liquid biopsy (TLB) is a novel diagnostic tool that examines the thermal profile (thermogram) of biological samples, reflecting their response to heat and thereby revealing characteristics of their overall composition or disease-induced alterations. Methods: In this retrospective study, a total of 35 ICF samples (49% mucinous) obtained via EUS-FNA (fine needle aspiration) were analyzed using TLB. Thermogram data were utilized to develop predictive models for differential diagnosis between mucinous and non-mucinous PCLs or malignancy detection through machine learning algorithms. Results: Two classification models were developed: TLB1 ("mucinous" vs. "non-mucinous" PCLs) and TLB2 ("benign mucinous" vs. "malignant mucinous" PCLs). The TLB1 model demonstrated a sensitivity of 92% and a negative predictive value of 86%, with an area under the curve (AUC) of 0.79 (0.59–0.99), indicating good discriminative ability between the two groups. The TLB2 model exhibited excellent predictive capability, with an AUC of 1.00. Conclusions: TLB analysis of PCLs is a promising tool that could significantly enhance the differential diagnosis of PCLs, enabling the efficient identification of mucinous lesions and even those undergoing malignant transformation. [ABSTRACT FROM AUTHOR]

Simple Summary: Individuals with Lynch syndrome (LS) are at increased risk of colorectal cancer and undergo regular lower endoscopic procedures to detect and remove precancerous lesions. However, the frequency of colonic neoplasia detection in LS is not well characterized. In this study, we analyzed over 1200 colonoscopies and sigmoidoscopies from 352 individuals with LS. Detection rates for adenomas and other colorectal lesions were influenced by factors such as age and clinical history, while genotype, sex, and race showed no association. Our findings may provide benchmarks for endoscopists caring for individuals with LS and offer insight into lesion detection patterns. Further research is needed to assess how these rates affect colorectal cancer risk and outcomes, with the goal of optimizing surveillance strategies for this high-risk population. Background: The expected and optimal adenoma detection rate (ADR) is not well characterized in Lynch syndrome (LS). The aim of this study is to determine the ADR, the overall colorectal neoplasia detection rate (CNDR), proximal serrated detection rate (PSDR), and CRC detection rate (CRCDR) in an LS cohort. Methods: A retrospective study was performed of individuals with LS who were evaluated at a single tertiary care center from May 2001 to September 2023 (n = 542). Data from procedure and pathology reports were collected along with relevant demographic, clinical history, and family history data. Fisher's exact test and the Kruskal–Wallis test were used to assess factors associated with colorectal neoplasia. Results: Amongst 542 individuals with LS, 352 met the inclusion criteria, and their 1296 colonoscopies/sigmoidoscopies were used for analysis. The cohort was primarily female (64.5%), white (87.5%), and privately insured (76.1%), with a near even distribution across genotypes. CNDR was 27.9%, ADR was 21.4%, PSDR was 7.7%, and CRCDR was 1.5%. Advanced age, Medicare insurance, prior colonic resection, and prior history of non-CRC were significantly associated with an increased CNDR and ADR (p < 0.05). PSDR remained constant with age. There was no association with genotype, biological sex, race, smoking, BMI, aspirin use, nor family history. Conclusions: Despite frequent colonoscopies/sigmoidoscopies, individuals with LS maintain a high rate of colorectal neoplasia, primarily driven by increased detection of adenomas with advancing age. Neoplasia rates may serve as helpful "ballpark rates" for endoscopists performing colonoscopies/sigmoidoscopies in LS. However, further studies need to determine whether neoplasia rates are predictive of CRC risk and outcomes in LS. [ABSTRACT FROM AUTHOR]

Simple Summary: In the assessment of pigmented lesions, it is crucial to avoid unnecessary procedures while ensuring that melanomas are not missed. This study aims to understand how the number needed to treat (NNT) in melanoma detection varies with a patient's age and the type of referring physician. By analyzing data from the University Hospital Regensburg, we found that the NNT decreased with advancing age. The type of referring physician was also significant: dermatologists from university settings had the most accurate referrals, requiring fewer procedures compared to office-based dermatologists and non-dermatologists. Additionally, the introduction of Germany's skin cancer screening program in 2008 significantly reduced the NNT. These findings may help refine melanoma detection strategies and improve screening practices in the future. Background/Objectives: With regard to excision of pigmented lesions for detection of malignant melanoma (MM), the number needed to treat (NNT) describes the number of melanocytic nevi that need to be biopsied/excised to detect one MM. The aim should be a low NNT. Methods: Single-center data analysis, including dermatohistopathological records of all nevi and MM cases during 2004–2013 at the Department of Dermatology, University Hospital Regensburg (UKR), was performed. We calculated the NNT, correlating it with the patient's age and referring physician. The MM to MM in situ ratio was calculated to quantify early detection. As a secondary objective, we stratified into a pre- and post-2008 dataset, coinciding with the introduction of statutory skin cancer screening in Germany. Results: The overall NNT of 118,668 pigmented lesions was 17.2. We found a linear decrease in NNT towards older patients (R2 = 62%; p < 0.001). The impact of skin cancer screening in 2008 was marked by a reduction in biopsies/excisions, a shift in age distribution, and a decrease in the NNT from 20.3 to 14.7. Office-based dermatologists had an NNT of 22.3, UKR-based dermatologists had an NNT of 8.0, and non-dermatologists had an NNT of 16.5. Conclusions: The age-related decrease in the NNT emphasizes the importance of age stratification for pigmented lesions. The NNT differed between professional settings. The implementation of skin cancer screening in 2008 was associated with a reduced NNT. [ABSTRACT FROM AUTHOR]

Simple Summary: The COVID-19 pandemic disrupted timely diagnosis of many cancers, including lung cancer, the leading cause of cancer-related death. In this study, we analyzed national data to understand how lung cancer detection was affected during the first two years of the pandemic, focusing on differences across demographic groups and communities. We found that lung cancer detection dropped significantly in 2020, particularly for rural residents, non-Hispanic Black and Asian individuals, and women. Although detection rates started to recover in 2021, disparities persisted, particularly in rural areas. Our findings highlight a hidden pool of undiagnosed cases and the need for targeted interventions to reach underserved populations. These insights could help guide future public health strategies to reduce disparities and improve early lung cancer detection for all groups. Objective: To quantify the extent of the disruption and recovery of lung cancer detection during the first two years of the pandemic, focusing on disparities across demographic and community factors. Methods: This retrospective cohort study used the SEER database to identify lung cancer cases from 2001 to 2021. Expected incidence rates for 2020 and 2021 were projected based on pre-pandemic trends (2000–2019) using the NCI's Joinpoint Regression program. Percent differences between expected and observed incidence rates were calculated. Multivariate and propensity score analyses were conducted to quantify changes in the odds of being diagnosed with metastatic disease during the pandemic. Results: Lung cancer incidence fell by 10% in 2020 compared to pre-pandemic projections, with rural populations, non-Hispanic Black and Asian patients, and females disproportionately affected. By 2021, detection rates partially recovered but remained 5% below expected levels. Localized disease detection improved significantly, while rural communities not adjacent to metropolitan areas faced further declines. Adjusted analyses showed that patients with small-cell lung cancer (SCLC) and those of Hispanic, non-Hispanic Black, or Asian/Pacific Islander ethnicity had persistently higher rates of distant disease presentation through 2021. Conclusions: The pandemic significantly reduced lung cancer detection, with only partial recovery by 2021. Persistent gaps, particularly in rural and minoritized populations, highlight the need for targeted interventions to reengage these communities. [ABSTRACT FROM AUTHOR]

Simple Summary: This retrospective analysis of selected patients who underwent guideline-based, point of care, endoscopy in tandem with guideline-based, screening/surveillance colonoscopy reveals potentially significant foregut pathology (not necessarily precancerous). Potentially precancerous foregut pathology is as prevalent as precancerous colon polyps found at colonoscopy. Benign foregut endoscopic and histologic findings may be amenable to dietary and pharmacologic interventions. Tandem EGD combines Barrett's esophagus and gastric cancer screening in one examination and should be considered in all subjects referred for screening or surveillance colonoscopy. Introduction: Performing a tandem endoscopy and colonoscopy in selected individuals has advantages, such as the early detection of benign and/or precancerous foregut diseases; it is efficient, and it may allow added therapies. It may also have disadvantages, such as generating anxiety from false-positive screening, possible harm from further testing, and unproven cost-effectiveness. Aims: We aimed to examine the prevalence of foregut endoscopic and histologic abnormalities in subjects referred for screening/surveillance colonoscopy who also underwent a tandem endoscopy. We wanted to (1) assess implications for cancer detection, intervention, and surveillance of precancerous foregut abnormalities, (2) identify benign foregut lesions, and (3) generate data on the utilities of this tandem approach. Patients and Methods: A retrospective cohort study of consecutive subjects referred for screening or surveillance colonoscopy who also underwent an endoscopy. Based on national screening guidelines, responses to prompting questions, personal or family history, or other risk factors, subjects were assigned to tandem endoscopy with biopsies (modified Seattle and Sydney protocols), under one anesthesia. Results: Of the 1004 patients referred for colonoscopy, 317 (32%) underwent tandem endoscopy. There were 214 women and 103 men. There were 237 Whites, 16 Asians, 40 Blacks, and 24 Hispanics. Median age was 59 (range 19–85). At endoscopy, we identified actionable benign (45%) peptic, inflammatory, and H. pylori-related abnormalities, and premalignant findings (i.e., intestinal metaplasia, 27%, dysplasia, 2%, and cancer 0.9%), comparable to the premalignant (40.3%) and malignant (0.6%) colonoscopy yield. Conclusions: When implemented based on national screening guidelines, tandem EGD and colonoscopy combines Barrett's esophagus and gastric cancer screening in one examination, and it has a high yield in a diverse US population. [ABSTRACT FROM AUTHOR]

Simple Summary: This study developed and evaluated Bayesian Network models for lung cancer risk prediction using a decade of data from 38,944 high-risk individuals in Denmark. The models were trained and validated on datasets with varying sizes and levels of missing data to reflect real-world screening scenarios. The results showed that a model trained on a small, complete dataset (AUC 0.78) performed similarly on a larger dataset with 21% missing data (AUC 0.78), but performance decreased when 39% of data were missing (AUC 0.67). The laboratory results and smoking data were the most informative variables, significantly outperforming models based only on age and smoking status (AUC 0.70). These findings suggest that BN models can maintain strong predictive performance despite incomplete data and highlight the value of including standard laboratory results in future LC screening programs. Background/Objectives: Lung cancer (LC) is the leading cause of cancer mortality, making early diagnosis essential. While LC screening trials are underway globally, optimal prediction models and inclusion criteria are still lacking. This study aimed to develop and evaluate Bayesian Network (BN) models for LC risk prediction using a decade of data from Denmark. The primary goal was to assess BN performance on datasets varying in size and completeness, simulate real-world screening scenarios, and identify the most valuable data sources for LC screening. Methods: The study included 38,944 patients evaluated for LC, with 11,284 (29%) diagnosed. Data on comorbidities, medications, and general practice were available for the entire cohort, while laboratory results, smoking habits, and other variables were only available for subsets. The cohort was divided into four subsets based on data availability, and BNs were trained and validated across these subsets using cross-validation and external validation. To determine the optimal combination of variables, all possible data combinations were evaluated on the samples that contained all the variables (n = 5587). Results: A model trained on the small, complete dataset (AUC 0.78) performed similarly on a larger dataset with 21% missing data (AUC 0.78). Performance dropped when 39% of data were missing (AUC 0.67), resulting in informative variables missing completely in the dataset. Laboratory results and smoking data were the most informative, significantly outperforming models based only on age and smoking status (AUC 0.70). Conclusions: BN models demonstrated moderate to strong predictive performance, even with incomplete data, highlighting the potential value of incorporating laboratory results in LC screening programs. [ABSTRACT FROM AUTHOR]

Simple Summary: The large-scale colorectal cancer screening program (CRCSP) in Flanders, Belgium, was suspended twice in 2020 due to the COVID-19 pandemic. The aim of this retrospective study was to estimate the influence of the COVID-19 pandemic on CRCSP uptake and to identify the disproportionate changes in uptake rate and screening interval in areas with different socioeconomic status (SES). We found that the COVID-19 pandemic had a short-lived influence on uptake rate and screening interval. Areas with lower SES led to a greater decrease in uptake rate and areas with higher SES showed a greater increase in screening interval during the COVID-19 pandemic. These findings suggest that tailored invitations on CRCSP during the pandemic are needed for low SES areas. Timely warnings help people living in high SES areas, who might delay participation, to adhere to screening intervals. Objectives: To assess the association of the COVID-19 pandemic with an uptake rate and screening interval between two screening rounds in colorectal cancer screening program (CRCSP) and identify the disproportionate correlation of socioeconomic status (SES) factors. Methods: An analysis was performed on aggregated screening and SES data at the area level in Flanders, Belgium, during 2018–2022. The screening uptake rate was the percentage of people returning self-test results within 40 days after invitation, and the screening interval was the number of days between current and previous screening. Differences in uptake rate and screening interval before and during COVID-19 were categorized into 10 quantiles, and determinants were evaluated using quantile regression models. Results: Significant change was seen from March to August 2020. The areas with the greatest decrease in uptake rate and screening interval had low population density, and areas with the greatest increase in screening interval had the highest income and percentage of home ownership. In regression analysis, more people living alone (β = −0.09), lower income (β = 0.10), and a higher percentage of home ownership (β = −0.06) were associated with a greater decrease in uptake rate. Areas with lower population density (β = −0.75), fewer people of Belgian nationality (β = −0.11), and higher income (β = 0.42) showed greater increases in screening interval. Conclusions: During the COVID-19 pandemic, people in areas with low SES were less likely to participate in screening, whereas people in areas with high SES were more likely to delay participation. A tailored invitation highlighting benefits of CRCSP is needed for people with low SES to improve uptake. Timely warnings could help people who delay participation adhere to screening intervals. [ABSTRACT FROM AUTHOR]

Simple Summary: This review explores the evolving landscape of lung cancer surgery, particularly focusing on the effectiveness and precision of sublobar resections such as segmentectomy. With advancements in screening and surgical techniques, there is a growing interest in more conservative approaches that spare lung tissue while ensuring complete cancer removal. The findings discussed in this review highlight the benefits and potential of sublobar resections, especially for early-stage non-small cell lung cancer, offering a less invasive option with comparable outcomes to lobectomy. This information is essential for the thoracic surgery community as they refine patient-specific treatment strategies in the era of precision medicine. Lung cancer remains the leading cause of cancer-related deaths worldwide. The introduction of targeted treatments and immunotherapy in lung cancer has transformed patient care by offering "precision medicine" focused on the characteristics of the disease. The same concept has emerged in lung cancer surgery. This "precision surgery" aims to determine the best surgical approach based on the characteristics of the cancer, the patient's cardiorespiratory status, and technological advances in thoracic surgery. While lobectomy continues to be the gold standard for managing early-stage lung cancer, the implementation of screening programs has enabled the earlier detection of smaller tumors. In this context, sublobar resections, particularly segmentectomy, have emerged as valuable options in the treatment of early-stage lung cancer. Recent studies suggest that sublobar resections, including segmentectomy and wedge resection, provide a viable alternative to lobectomy. This review explores the various resection strategies available, tailored to patient and tumor characteristics, and highlights modern preoperative techniques aimed at advancing precision surgery. [ABSTRACT FROM AUTHOR]

Simple Summary: Penile cancer is aggressive and rapidly progressive. Early detection is crucial for survival. Many men delay seeking help due to a lack of awareness and fear of embarrassment. This study explored the use of artificial intelligence (AI) in detection of penile cancer. AI was trained on 136 penile lesions images from scientific publications. This included 65 penile cancer, 44 precancerous, and 27 benign images. It performed well in distinguishing between benign lesions and penile cancer with high accuracy. However, it faced challenges in accurately identifying precancerous lesions. These findings suggest that AI has potential in early detection of penile cancer, but more research is needed to refine and validate the AI software with real-life data. The goal is to complement and not replace clinicians. The AI algorithm may allow patients to evaluate any concerning penile lesion from the comfort and privacy of their home, potentially encouraging earlier medical consultation. Background/Objective: Penile cancer is aggressive and rapidly progressive. Early recognition is paramount for overall survival. However, many men delay presentation due to a lack of awareness and social stigma. This pilot study aims to develop a convolutional neural network (CNN) model to differentiate penile cancer from precancerous and benign penile lesions. Methods: The CNN was developed using 136 penile lesion images sourced from peer-reviewed open access publications. These images included 65 penile squamous cell carcinoma (SCC), 44 precancerous lesions, and 27 benign lesions. The dataset was partitioned using a stratified split into training (64%), validation (16%), and test (20%) sets. The model was evaluated using ten trials of 10-fold internal cross-validation to ensure robust performance assessment. Results: When distinguishing between benign penile lesions and penile SCC, the CNN achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.94, with a sensitivity of 0.82, specificity of 0.87, positive predictive value of 0.95, and negative predictive value of 0.72. The CNN showed reduced discriminative capability in differentiating precancerous lesions from penile SCC, with an AUROC of 0.74, sensitivity of 0.75, specificity of 0.65, PPV of 0.45, and NPV of 0.88. Conclusion: These findings demonstrate the potential of artificial intelligence in identifying penile SCC. Limitations of this study include the small sample size and reliance on photographs from publications. Further refinement and validation of the CNN using real-life data are needed. [ABSTRACT FROM AUTHOR]

Simple Summary: Adrenocortical tumors (ACTs) are rare in children and can be benign (adrenocortical adenoma or ACA) or malignant (adrenocortical carcinoma or ACC). Children with Beckwith–Wiedemann spectrum (BWSp) are at an increased risk of developing ACTs, but there is uncertainty around the prognosis, management, and molecular characteristics associated with these tumors. In this study, we provide a literature review of data from 54 published patients with BWSp-ACT and report on one additional new patient, totaling 55 cases: 19 ACA, 33 ACC, and 3 uncertain malignant potential (umACT). Almost half of the ACC patients were clinically diagnosed with BWSp after ACC onset, suggesting that the BWSp clinical diagnostic score has limited value for early diagnosis in this setting. Two patients with metastatic ACC had a low histopathological Wieneke score, confirming limitations of the current histopathological classification, as previously documented. Ultrasound screening failed to identify ACC before metastasis in two cases, indicating a need for new screening strategies for ACTs in children with BWSp. Notably, in some cases, metastatic ACC exhibited unexpectedly indolent behavior, despite their malignancy. Background/Objectives: Adrenocortical tumors (ACTs), including adrenocortical adenoma (ACA) and carcinoma (ACC), represent 0.3–0.4% of pediatric tumors. Beckwith–Wiedemann spectrum (BWSp) confer an increased risk of ACTs, but prognosis, management, and associated molecular characteristics are unclear. Methods: This paper combines a literature review of 54 published cases of BWSp-ACT with a report of one newly identified patient, totaling 55 cases with a confirmed BWSp clinical and/or molecular diagnosis. Results: Nineteen patients with ACA, 33 with ACC, and 3 with ACT of uncertain malignant potential (umACT) were included. Twenty patients had uniparental disomy of chromosome 11p15.5 (patUPD11), 11imprinting Center 2 Loss-of-methylation (IC2-LoM), and had 2 11p15 locus duplication. Eleven patients were diagnosed during cancer screening procedures, including two metastatic at diagnosis ACC. Conclusions: Almost half of ACC patients reached the minimum score for clinical BWSp diagnosis only after ACC onset, suggesting that the BWSp score has limited value for the early diagnosis in such a setting. Two patients with metastatic ACC had a histopathological Wieneke score ≤2, not correlating with clinical malignancy and confirming limitations of the current histopathological classification, as previously documented. Ultrasound screening failed identifying the ACC before metastasis in two cases, indicating an urgent need to develop new strategies for screening of ACTs in BWSp. Furthermore, some cases of metastatic ACC exhibited unexpectedly indolent behavior despite being malignant. [ABSTRACT FROM AUTHOR]

Simple Summary: This study uses an individual socioeconomic status index to analyse social inequalities in the Valencia Region Colorectal Cancer Screening Programme. Analysing the social determinants of cancer and ensuring health equity are some of the main challenges faced by developed countries in recent years, and these aspects are also the main focus of policies in many territories. Furthermore, given that the European Commission has highlighted the importance of evaluating inequalities in colorectal cancer (CRC) screening programmes, this study is highly relevant. This paper contributes to the existing literature on inequalities in cancer screening, and the insights provided herein could potentially influence policies and interventions to promote equity in CRC screening programmes. Background/Objectives: An individual socioeconomic status index (ISESI) was used to analyse inequalities in participation and colonoscopy acceptance in the Valencia Region Colorectal Cancer Screening Programme (VR-CRCSP). Methods: This is a cross-sectional study of men and women aged 50–69 who had been invited to participate in the VR-CRCSP as of February 2020 (N = 1,066,763). The variables included in the ISESI were nationality, employment status, disability, healthcare coverage, risk of vulnerability, and family size. The ISESI was categorised into quartiles (Qs), with Q4 corresponding to the lowest socioeconomic status (SES). Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using mixed logistic regression models. Results: The results showed that Q2 (OR = 1.30, CI = 1.28–1.33) and Q3 (OR = 1.07, CI = 1.05–1.09) are more likely to participate in the CRCSP than Q1 (the highest SES), and that Q4 (OR = 0.77, CI = 0.76–0.78) is less likely to participate than Q1. In addition, Q2 (OR = 2.03, CI = 1.78–2.32), Q3 (OR = 1.90, CI = 1.67–2.16), and Q4 (OR = 1.55, CI = 1.36–1.76) are more likely to accept a colonoscopy than Q1. The following socioeconomic characteristics were related to both non-participation and colonoscopy refusal: not Spanish, disabled, no family unit, at risk of social vulnerability, and private mutual health insurance. Conclusions: Inequalities were observed in VR-CRCSP participation and colonoscopy acceptance. [ABSTRACT FROM AUTHOR]

Simple Summary: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, and early detection plays a crucial role in improving patient survival. However, the current surveillance guidelines may not fully address the growing number of HCC cases in individuals with non-alcoholic fatty liver disease (NAFLD), who often develop cancer without liver cirrhosis. Our paper aims to review and compare major international guidelines on HCC surveillance, highlighting key gaps in their criteria such as the exclusion of non-cirrhotic NAFLD patients. By exploring advancements in screening technologies, including imaging and biomarkers, we aim to provide insights that could lead to better detection methods for high-risk populations. These findings have the potential to shape future research and updates to clinical guidelines, ensuring that surveillance strategies become more inclusive and effective. Background/Objectives: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths globally, with prognosis and treatment outcomes that are significantly influenced by the stage at diagnosis. Early detection through regular surveillance is crucial for improving patient outcomes, especially in high-risk groups such as those with cirrhosis or chronic hepatitis B. Geographic variations in HCC risk factors, including viral hepatitis and non-alcoholic fatty liver disease (NAFLD), have led to the development of different international surveillance guidelines. This review aims to compare and evaluate the surveillance strategies proposed by the Asian Pacific Association for the Study of the Liver (APASL), the American Association for the Study of Liver Diseases (AASLD), and the European Association for the Study of the Liver and European Organization for Research and Treatment of Cancer (EASL–EORTC). Methods: The review examined and compared major international guidelines on HCC surveillance, focusing on patient selection, imaging modalities, and the integration of biomarkers. We also explored recent advancements in screening techniques, including artificial intelligence and emerging biomarkers, to identify future directions for improving surveillance strategies. Results: Our analysis identified key differences in the guidelines, particularly in imaging modality preferences and the use of biomarkers for early detection. While all guidelines place emphasis on high-risk populations, the inclusion criteria and surveillance intervals vary. Additionally, novel technologies such as artificial intelligence show potential to enhance the accuracy and efficiency of HCC detection. Conclusions: This review highlights the need to harmonize the international guidelines, particularly in regard to patients with non-cirrhotic NAFLD who remain under-represented in current surveillance protocols. Future research should focus on integrating emerging technologies and biomarkers to improve early detection and overall patient outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: One way cancer develops is through gene amplification, where copies of certain genes increase. Traditionally, this has been detected using a technique called fluorescence in situ hybridization (FISH). However, newer methods like amplicon-based next-generation sequencing (NGS) are increasingly being used to detect DNA mutations and can also identify changes in gene copy numbers. However, NGS has not yet been validated for routine clinical use for detecting gene amplification. This study evaluated NGS as a less resource-intensive alternative to traditional methods like FISH. The findings highlight the strengths and limitations of using amplicon-based NGS assays for detecting gene amplification in preserved pathology tissue samples. By modifying the criteria for measuring gene copy numbers and considering tumor characteristics, this study aimed to enhance the accuracy of amplicon-based NGS in detecting gene amplifications with a specific focus on ERBB2 gene amplification. This could potentially decrease the reliance on FISH testing and help improve testing strategies in clinical settings, ultimately guiding treatment decisions. Background: Targeted next-generation sequencing (NGS) panels are increasingly being utilized to identify actionable gene amplifications (copy number > 4) among solid tumors. Methods: This study validated the analytical performance of two amplicon-based NGS assays, the Oncomine Comprehensive Panel (OCAv3) and the Oncomine Focus Assay (OFA), for detecting gene amplification in formalin-fixed paraffin-embedded (FFPE) tumors of varying cellularity. OCAv3 was assessed for amplification detection in 756 FFPE samples comprising various tumor types. Results: We demonstrated that with standardized quality control metrics, including median absolute pairwise difference score, these assays can achieve a near-perfect positive predictive value, although their sensitivity for detecting amplifications significantly decreased in tumors with cellularity below 30%. Stratifying tumor cellularity into 10–30%, 31–60%, and 61–95% groups revealed significantly higher gene amplification detection rates in the 31–60% and 61–95% groups versus the 10–30% group (20.6% and 26.7% vs. 9.2%, p < 0.0001). When considering all detected gene amplifications, the average amplification calling per sample was nearly five-fold lower in the 10–30% group versus the 61–95% group (0.11 vs. 0.52; p < 0.0001). To further investigate the analytic performance of OCAv3 in detecting ERBB2 amplification, we analyzed a cohort of 121 uterine carcinomas with confirmed ERBB2 status by HER2 IHC or FISH, in which a threshold incorporating amplifications and tumor cellularity achieved 79% sensitivity and 100% specificity, potentially eliminating the need for FISH analysis in 34% of equivocal cases. In a separate validation cohort, similar analytical performance was observed, with the threshold demonstrating consistent sensitivity and specificity. Conclusions: This study highlights the strengths and limitations of amplicon-based NGS assays in detecting amplifications using real-world data. [ABSTRACT FROM AUTHOR]

Simple Summary: Liquid biopsy, particularly involving the detection of circulating tumor DNA (ctDNA), has emerged as a promising tool in breast cancer management. Unlike traditional tissue biopsies, ctDNA provides a non-invasive method by detecting DNA fragments released by tumor cells into the bloodstream. Detection techniques include PCR-based methods, targeted panels for known mutations, and personalized assays based on the individual tumor profile. In early-stage breast cancer, ctDNA shows potential for assessing response to treatments like chemotherapy and identifying patients at elevated risk of recurrence. However, ctDNA detection in early-stage disease remains challenging due to low tumor DNA concentrations in blood. In metastatic breast cancer, ctDNA is utilized to monitor disease progression, evaluate treatment response, and detect emerging resistance mutations, enabling timely adjustments in therapy. Although ctDNA holds significant potential for enhancing personalized care, further research is necessary to validate its role in routine clinical practice for comprehensive breast cancer management. The progress that has been made in recent years in relation to liquid biopsies in general and circulating tumor DNA (ctDNA) in particular can be seen as groundbreaking for the future of breast cancer treatment, monitoring and early detection. Cell-free DNA (cfDNA) consists of circulating DNA fragments released by various cell types into the bloodstream. A portion of this cfDNA, known as ctDNA, originates from malignant cells and carries specific genetic mutations. Analysis of ctDNA provides a minimally invasive method for diagnosis, monitoring response to therapy, and detecting the emergence of resistance. Several methods are available for the analysis of ctDNA, each with distinct advantages and limitations. Quantitative polymerase chain reaction is a well-established technique widely used due to its high sensitivity and specificity, particularly for detecting known mutations. In addition to the detection of individual mutations, multigene analyses were developed that could detect several mutations at once, including rarer mutations. These methods are complementary and can be used strategically depending on the clinical question. In the context of metastatic breast cancer, ctDNA holds particular promise as it allows for the dynamic monitoring of tumor evolution. Through ctDNA analysis, mutations in the ESR1 or PIK3CA genes, which are associated with therapy resistance, can be identified. This enables the early adjustment of treatment and has the potential to significantly enhance clinical outcome. The application of ctDNA in early breast cancer is an ongoing investigation. In (neo)adjuvant settings, there is preliminary data indicating that ctDNA can be used for therapy monitoring and risk stratification to decide on post-neoadjuvant strategies. In the monitoring of aftercare, the detection of ctDNA appears to be several months ahead of routine imaging. However, the feasibility of implementing this approach in a clinical setting remains to be seen. While the use of ctDNA as a screening method for the asymptomatic population would be highly advantageous due to its minimally invasive nature, the available data on its clinical benefit are still insufficient. Nevertheless, ctDNA represents the most promising avenue for fulfilling this potential future need. [ABSTRACT FROM AUTHOR]

Simple Summary: To improve cancer classification performance for high-dimensional microarray datasets, this work proposes combining filter and differential evolutionary (DE) algorithm feature selection techniques. By scoring genes or features of high-dimensional microarray datasets by some common filter methods, we keep only the highest-ranked features and eliminate superfluous and irrelevant ones to decrease the dimensionality of the microarray datasets. Then, the genes or features of the microarray datasets are optimized further by DE, producing noticeably better classification results. This could lead to outstanding improvement in the cancer classification using only less features of the microarray datasets. Background: In recent years, microarray datasets have been used to store information about human genes and methods used to express the genes in order to successfully diagnose cancer disease in the early stages. However, most of the microarray datasets typically contain thousands of redundant, irrelevant, and noisy genes, which raises a great challenge for effectively applying the machine learning algorithms to these high-dimensional microarray datasets. Methods: To address this challenge, this paper introduces a proposed hybrid filter and differential evolution-based feature selection to choose only the most influential genes or features of high-dimensional microarray datasets to improve cancer diagnoses and classification. The proposed approach is a two-phase hybrid feature selection model constructed using selecting the top-ranked features by some popular filter feature selection methods and then further identifying the most optimal features conducted by differential evolution (DE) optimization. Accordingly, some popular machine learning algorithms are trained using the final training microarray datasets with only the best features in order to produce outstanding cancer classification results. Four high-dimensional cancerous microarray datasets were used in this study to evaluate the proposed method, which are Breast, Lung, Central Nervous System (CNS), and Brain cancer datasets. Results: The experimental results demonstrate that the classification accuracy results achieved by the proposed hybrid filter-DE over filter methods increased to 100%, 100%, 93%, and 98% on Brain, CNS, Breast and Lung, respectively. Furthermore, applying the suggested DE-based feature selection contributed to removing around 50% of the features selected by using the filter methods for these four cancerous microarray datasets. The average improvement percentages of accuracy achieved by the proposed methods were up to 42.47%, 57.45%, 16.28% and 43.57% compared to the previous works that are 41.43%, 53.66%, 17.53%, 61.70% on Brain, CNS, Lung and Breast datasets, respectively. Conclusions: Compared to the previous works, the proposed methods accomplished better improvement percentages on Brain and CNS datasets, comparable improvement percentages on Lung dataset, and less improvement percentages on Breast dataset. [ABSTRACT FROM AUTHOR]

Simple Summary: Lung and colon cancer are among the leading causes of cancer-related deaths globally, emphasizing the critical need for early and precise detection to improve treatment and patient outcomes. This research introduces a novel deep learning model for efficient lung and colon cancer detection, aiming to address the limitations of existing computationally intensive models. This study proposes a lightweight, parameter-efficient model suitable for mobile devices, utilizing a 1D convolutional neural network enhanced with Squeeze-and-Excitation layers. By achieving 100% accuracy on a large dataset of histopathological images, this research provides a significant advancement in cancer detection technology. The findings hold the potential to revolutionize medical diagnostics by enabling faster, more accessible, and reliable cancer screening, potentially leading to earlier interventions and improved survival rates. Lung and colon cancers are among the leading causes of cancer-related mortality worldwide. Early and accurate detection of these cancers is crucial for effective treatment and improved patient outcomes. False or incorrect detection is harmful. Accurately detecting cancer in a patient's tissue is crucial to their effective treatment. While analyzing tissue samples is complicated and time-consuming, deep learning techniques have made it possible to complete this process more efficiently and accurately. As a result, researchers can study more patients in a shorter amount of time and at a lower cost. Much research has been conducted to investigate deep learning models that require great computational ability and resources. However, none of these have had a 100 % accurate detection rate for these life-threatening malignancies. Misclassified or falsely detecting cancer can have very harmful consequences. This research proposes a new lightweight, parameter-efficient, and mobile-embedded deep learning model based on a 1D convolutional neural network with squeeze-and-excitation layers for efficient lung and colon cancer detection. This proposed model diagnoses and classifies lung squamous cell carcinomas and adenocarcinoma of the lung and colon from digital pathology images. Extensive experiment demonstrates that our proposed model achieves 100 % accuracy for detecting lung, colon, and lung and colon cancers from the histopathological (LC25000) lung and colon datasets, which is considered the best accuracy for around 0.35 million trainable parameters and around 6.4 million flops. Compared with the existing results, our proposed architecture shows state-of-the-art performance in lung, colon, and lung and colon cancer detection. [ABSTRACT FROM AUTHOR]

Simple Summary: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death world-wide. Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) is a novel imaging modality used in prostate cancer that appears to demonstrate uptake in HCC. The aim of this systematic review and meta-analysis was to determine the sensitivity and specificity of PSMA PET for HCC. On systematic review, 68Ga-PSMA-11 demonstrated a high sensitivity on per-patient analysis for HCC, 89.9% (95% CI 78.5–95.5), and on a per-lesion analysis, this increased to 94.5% (95% CI 82.9–98.4). Specificity, however, was poorly reported, with insufficient data for analysis. The meta-analysis of studies with sufficient data demonstrated a sensitivity of 97.1% (95% CI 87.8–99.4) and specificity of 42.2% (95% CI 0.3–99.4) on per-lesion level for HCC. Specificity has been poorly reported, and diagnostic accuracy remains unclear. Further prospective studies are required to define the true specificity of PSMA PET for HCC. Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) is widely used in prostate cancer. Recent studies indicate hepatocellular carcinoma (HCC) demonstrates PSMA PET uptake. The diagnostic accuracy of PSMA PET for HCC is not known. We conducted a systematic review and meta-analysis of studies assessing 68Ga-PSMA-11 in HCC. Nine studies were included, with 196 patients and a total of 491 HCC lesions. Per-patient analysis yielded a pooled sensitivity of 89.8% (95% CI 78.5–95.5). Specificity was poorly reported, with insufficient data. When per-lesion level analysis was performed on seven studies, the pooled sensitivity was 94.5% (95% CI 82.9–98.4), and specificity was again poorly reported with insufficient data. Among the three studies with adequate data for full per-lesion meta-analysis, 115 lesions in 41 patients demonstrated sensitivity of 97.1% (95% CI 87.8–99.4), while specificity was 42.2% (95% CI 0.3–99.4). Two studies provided sufficient data for meta-analysis on a per-patient level (n = 50 patients), demonstrating a sensitivity of 92.5% (95% CI 64.0–98.9) and specificity of 72.4% (95% CI 1.3–99.8). PSMA PET demonstrates a high sensitivity for HCC and shows promise as an imaging modality for diagnosis and staging of HCC. However, the existing literature does not provide enough data to confidently evaluate its specificity and, therefore, accuracy. Further prospective studies are necessary, with a focus on the accurate reporting of benign lesions and inclusion of patients with an intermediate probability of HCC. [ABSTRACT FROM AUTHOR]

Simple Summary: Hepatocellular carcinoma (HCC) is a common and deadly cancer, almost always arising from a background of chronic liver disease. Patients at high risk for developing HCC are recommended to undergo semiannual screening for HCC with serum alpha-fetoprotein and liver ultrasound. However, for patients of lower socioeconomic status, adherence to this recommended screening program can be challenging. Here, we describe outcomes of patients diagnosed with HCC seen primarily in a county safety-net healthcare system. Patients were analyzed by whether or not they were screened for HCC or diagnosed incidentally. We find that patients diagnosed as part of a screening regimen have improved overall survival and that this improvement persists after adjustment for lead-time bias. Dedicating resources to HCC screening can meaningfully address existing disparities for patients at risk for development of HCC. Purpose: We describe the impact of screening on outcomes of patients diagnosed with hepatocellular carcinoma (HCC) in an urban safety-net healthcare system compared to a non-screened cohort diagnosed with HCC. Methods: Patients diagnosed with HCC at John Peter Smith Health Network were identified by querying the hospital tumor registry and allocated to the screened cohort if they had undergone any liver imaging within one year prior to HCC diagnosis, while the remainder were allocated to the non-screened cohort. Kaplan–Meier methods and log-rank tests were used to compare 3-year survival curves from an index date of HCC diagnosis. Cox proportional hazard models were used to calculate unadjusted and adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). The Duffy adjustment was used to address lead-time bias. Results: A total of 158 patients were included (n = 53 screened, n = 105 non-screened). The median overall survival (OS) for the screened cohort was 19.0 months (95% CI: 9.9–NA) and that for the non-screened cohort was 5.4 months (95% CI: 3.7–8.5) [HR death (non-screened vs. screened) = 2.4, 95% CI: 1.6-3.6; log rank p < 0.0001]. The benefit of screening remained after adjusting for lead-time bias (HR 2.19, 95% CI 1.4–3.3, p = 0.0002). Conclusions: In an urban safety-net population, screening for HCC was associated with improved outcomes compared to patients diagnosed with HCC outside of a screening protocol. [ABSTRACT FROM AUTHOR]

Simple Summary: This study looked at different ways to predict the risk of colorectal cancer using blood tests. Sixteen studies from 2015 to 2024 were reviewed, focusing on people with an average risk of cancer who were undergoing a colonoscopy for screening. The most common test was a complete blood count, which showed good accuracy. Other tests included tests of proteins, metabolites, enzymes, and markers related to insulin and anemia. The best results came from a test using a group of plasma metabolites. While these tests could make cancer screening more accurate, many studies did not show how these models could be used in real-life settings. More research is needed to understand how these models can be effectively used in current screening programs. Background/Objectives: Risk prediction models (RPMs) for colorectal cancer (CRC) could facilitate risk-based screening. Models incorporating biomarkers may improve the utility of current RPMs. We performed a systematic review of studies reporting RPMs for CRC that evaluated the impact of blood-based biomarkers on clinical outcome prediction at the time of screening colonoscopy in average-risk populations. Methods: We conducted a search of MEDLINE, Web of Science, and PubMed databases from inception through April 2024. Studies that developed or validated a model to predict risk of CRC or its precursors were included. Studies were limited to those including patients undergoing average-risk CRC screening. Results: Sixteen studies published between 2015 and 2024 were included. Outcomes included CRC (16 studies) and high-risk adenomas (1 study). Using a complete blood count was the most common biomarker and was able to achieve an AUC of 0.82 and a specificity of 0.88. Other blood-based biomarkers included were various serum proteins/metabolites/enzymes, plasma metabolites, insulin-related factors, and anemia markers. The highest-performing model, with an AUC of 0.99, involved the use of a plasma metabolite panel. Conclusions: The evidence base of RPMs for CRC screening is expanding and incorporating biomarkers, which remain a prominent aspect of model discovery. Most RPMs included a lack of internal/external validation or discussion as to how the model could be implemented clinically. As biomarkers improve the discriminatory potential of RPMs, more research is needed for the evaluation and implementation of RPMs within existing CRC screening frameworks. [ABSTRACT FROM AUTHOR]

Simple Summary: Tumors in soft tissue and bones are rare, and there is limited knowledge about how they occur. Better knowledge about inherited predisposition to tumor syndromes increases the chance for the medical community to detect cancer early through targeted screening programs and to choose the most appropriate cancer treatment. In our study, we show that some inherited mutations can increase the risk for these tumors. We applied a novel approach, in which we analyzed a blood sample in tandem with a tumor sample from participants, and we found especially interesting inherited mutations. Some of the mutations we found were already known to increase the risk of cancer, although not proven to be connected to soft tissue and bone tumors before. Other mutations we present have not been shown to be connected to tumors at all before. Our findings can guide further genetic investigations of soft tissue and bone tumors. Background: The etiology of most mesenchymal tumors is unknown, and knowledge about syndromes with an increased risk of tumors in bone or soft tissue is sparse. Methods: We present a prospective germline analysis of 312 patients with tumors suspected of being sarcomas at a tertiary sarcoma center. Germline and tumor whole genome sequencing, tumor transcriptome, and methylome analyses were performed. Results: Germline pathogenic or likely pathogenic variants associated with an increased risk of tumors were detected in 24 patients (8%), of which 11 (4%) harbored a detectable second hit in the tumor. Second hits were confirmed in genes with (NF1, RB1, TP53, EXT2, and SDHC) and without (ATM, CDC73, MLH1, MSH6, POLG, and KCNQ1) known association with mesenchymal tumor predisposition. Sarcomas from two Lynch syndrome patients showed mismatch repair deficiency, predicting a treatment response to immune checkpoint inhibitors (Level 1 biomarker according to the FDA (Federal Drug Administration) and ESMO (European Society for Medical Oncology)). None of the three CHEK2 carriers had a second hit in the tumor, suggesting a weak link to sarcoma. Conclusions: We conclude that second-hit analyses can be used in standard of care to identify syndrome-related tumors. This approach can help distinguish true manifestations of tumor syndromes from unrelated germline findings and enhance the understanding of germline predisposition in soft tissue tumors. Prospective screening using germline whole genome sequencing should be considered when comprehensive somatic sequencing is introduced into clinical practice. [ABSTRACT FROM AUTHOR]

Simple Summary: Pancreatic cancer (PC) is one of the most common causes of cancer-related deaths. Unfortunately, the number of deaths caused by this cancer is expected to increase in the future. Early diagnosis of pancreatic cancer is essential for improving treatment efficacy. MicroRNAs are endogenous non-coding RNAs and play a role in the post-transcriptional regulation of expression of many genes—they can also be useful as diagnostic and prognostic biomarkers in various cancers, including pancreatic cancer. Using microRNA detection in patients' blood is becoming more and more popular. We review the clinical value of microRNAs in the screening, diagnosis, prognosis, and monitoring of pancreatic cancer therapy. Pancreatic cancer (PC) is a serious malignant tumor with a high mortality rate, mainly due to late diagnosis and a lack of effective therapeutic interventions. The possibility of recognizing this cancer with reliable biomarkers using minimally invasive methods is of great importance for improving early detection, prognostic assessment, and targeted treatment methods. In recent years, small non-coding RNAs, especially microRNAs, have emerged as promising candidates for biomarkers of pancreatic cancer. Despite the methodological and analytical limitations of microRNA determination and a lack of available automated and standardized tests, a prospective systematic assessment of this new parameter's use in the detection of pancreatic cancer is warranted. This review provides general information on the diagnostic and prognostic utility of microRNAs, which appear to be well-established in many studies. In summary, microRNAs are promising non-invasive biomarkers of pancreatic cancer, offering potential opportunities for early detection, prognosis, and treatment monitoring. As research in this field progresses, microRNAs should become valuable tools in the fight against pancreatic cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: Head and neck squamous cell carcinoma (HNSCC) consists of a group of heterogeneous tumors that are diagnosed comparatively late and often have a poor prognosis. Current head and neck research is motivated to find biomarkers for early detection, diagnosis, and therapy monitoring. Extravesicular (EV)-microRNA obtained by liquid biopsy of blood offers a promising approach here since the respective target mRNAs and their function in tumor neogenesis are partly already known. In this study, blood sera from 50 HNSCC patients and 16 controls treated between 2020–2023 at the Department of Otorhinolaryngology, Head and Neck Surgery of the University Hospital of Münster, were obtained by liquid biopsy. A specific EV-miRNA panel consisting of EV-miR-21, -1246, -200c, -let-7a, -11a, and -26a was determined. The expression of the analyzed EV-microRNA of the patients was then examined for correlation with clinical parameters regarding the diagnostic and prognostic value and suitability for therapy monitoring. Background: Biomarkers for HNSCC are still lacking. Biomolecules obtained via liquid biopsy are being investigated for diagnosis, prognosis, and therapy monitoring, including extracellular vesicles (EVs) and EV-cargo, e.g., proteins, RNA, and microRNA. This study aims to understand localization-dependent EV-microRNA expression in blood sera, their dynamics over time (12 months FU), and insights into their potential in diagnostics and therapy monitoring. Methods: Via liquid biopsy, blood serum was taken from 50 patients with HNSCC and 16 controls. Extracellular vesicles were isolated from serum by precipitation, and the contained microRNA-21, -1246, -200c, -let-7a, -181a, and -26a were amplified by reverse transcription and determined with real-time PCR. Expression ratios (HNSCC to healthy controls) were collated with the patients' clinical parameters. A second liquid biopsy was carried out avg. 12 months later in the tumor aftercare. A sub-analysis with the Oropharynx subsite was implemented. Results: EV-mir-21, -let-7a, and -181a were 2.5–3-fold higher expressed in HPV/p16+ than in HPV/p16- HNSCC. Different expressions of EV-mir-181a and -26a could be demonstrated depending on the therapy modality. Conclusions: EV-microRNA could be a promising biomarker in the diagnosis and therapy monitoring of HNSCC. A systematic comparison of EV- and tissue microRNA expression in different HNSCC-subsites is needed. [ABSTRACT FROM AUTHOR]

Simple Summary: Cholangiocarcinoma (CCA), a rare cancer that affects the bile ducts, is often diagnosed at a late stage, making treatment difficult and survival rates low. This research aims to improve the accuracy of diagnosing distal cholangiocarcinoma (dCCA) by using advanced artificial intelligence (AI) models to analyze endoscopic ultrasound (EUS) images. By developing a user-friendly tool, this study hopes to assist doctors in making quicker and more reliable diagnoses, potentially leading to better outcomes for patients with this challenging disease. Introduction: Cholangiocarcinoma (CCA) is a highly lethal malignancy originating in the bile ducts, often diagnosed late with poor prognosis. Differentiating benign from malignant biliary tumors remains challenging, necessitating advanced diagnostic techniques. Objective: This study aims to enhance the diagnostic accuracy of endoscopic ultrasound (EUS) for distal cholangiocarcinoma (dCCA) using advanced convolutional neural networks (CCNs) for the classification and segmentation of EUS images, specifically targeting dCCAs, the pancreas, and the bile duct. Materials and Methods: In this retrospective study, EUS images from patients diagnosed with dCCA via biopsy and an EUS-identified bile duct tumor were evaluated. A custom CNN was developed for classification, trained on 156 EUS images. To enhance the model's robustness, image augmentation techniques were applied, generating a total of 1248 images. For tumor and organ segmentation, the DeepLabv3+ network with ResNet50 architecture was utilized, employing Tversky loss to manage unbalanced classes. Performance evaluation included metrics such as accuracy, sensitivity, specificity, and Intersection over Union (IoU). These methods were implemented in collaboration with the ADAPTED Research Group at the Technical University of Cluj-Napoca. Results: The classification model achieved a high accuracy of 97.82%, with precision and specificity both at 100% and sensitivity at 94.44%. The segmentation models for the pancreas and bile duct demonstrated global accuracies of 84% and 90%, respectively, with robust IoU scores indicating good overlap between predicted and actual contours. The application performed better than the UNet model, particularly in generalization and boundary delineation. Conclusions: This study demonstrates the significant potential of AI in EUS imaging for dCCA, presenting a robust tool that enhances diagnostic accuracy and efficiency. The developed MATLAB application serves as a valuable aid for medical professionals, facilitating informed decision-making and improving patient outcomes in the diagnosis of cholangiocarcinoma and related pathologies. [ABSTRACT FROM AUTHOR]

Simple Summary: Lung and colon cancers are among the leading causes of death globally. Accurate and early detection is essential for improving patient outcomes. However, the existing dataset used in many studies for cancer diagnosis is limited by augmentation methods, reducing its real-world applicability. This study addresses the limitations by introducing new real-world histopathological images to enhance the dataset and applying advanced contrast enhancement techniques. Our proposed model integrates feature extraction from MobileNetV2 and EfficientNetB3 with Grey Wolf Optimization for feature selection and classification. This approach improves the model's generalizability, offering better support for pathologists in real-time cancer diagnosis. Background: Lung and colon cancers are among the most prevalent and lethal malignancies worldwide, underscoring the urgent need for advanced diagnostic methodologies. This study aims to develop a hybrid deep learning and machine learning framework for the classification of Colon Adenocarcinoma, Colon Benign Tissue, Lung Adenocarcinoma, Lung Benign Tissue, and Lung Squamous Cell Carcinoma from histopathological images. Methods: Current approaches primarily rely on the LC25000 dataset, which, due to image augmentation, lacks the generalizability required for real-time clinical applications. To address this, Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied to enhance image quality, and 1000 new images from the National Cancer Institute GDC Data Portal were introduced into the Colon Adenocarcinoma, Lung Adenocarcinoma, and Lung Squamous Cell Carcinoma classes, replacing augmented images to increase dataset diversity. A hybrid feature extraction model combining MobileNetV2 and EfficientNetB3 was optimized using the Grey Wolf Optimizer (GWO), resulting in the Lung and Colon histopathological classification technique (MEGWO-LCCHC). Cross-validation and hyperparameter tuning with Optuna were performed on various machine learning models, including XGBoost, LightGBM, and CatBoost. Results: The MEGWO-LCCHC technique achieved high classification accuracy, with the lightweight DNN model reaching 94.8%, LightGBM at 93.9%, XGBoost at 93.5%, and CatBoost at 93.3% on the test set. Conclusions: The findings suggest that our approach enhances classification performance and offers improved generalizability for real-world clinical applications. The proposed MEGWO-LCCHC framework shows promise as a robust tool in cancer diagnostics, advancing the application of AI in oncology. [ABSTRACT FROM AUTHOR]

Simple Summary: Current ultrasound imaging methods for detecting breast cancer often face challenges with image quality, making it tough to accurately spot tumors. This paper introduces UCapsNet, a new model that combines two advanced techniques to enhance breast cancer detection in ultrasound images. By improving how tumors are segmented and classified, UCapsNet aims to deliver clearer and more accurate results compared to traditional methods. Our findings indicate that UCapsNet can greatly improve diagnostic precision, enabling earlier detection and better treatment options for patients. This research could significantly impact future studies and lead to improved breast cancer detection practices in the medical community. Background/Objectives: Breast cancer remains one of the biggest health challenges for women worldwide, and early detection can be truly lifesaving. Although ultrasound imaging is commonly used to detect tumors, the images are not always of sufficient quality, and, thus, traditional U-Net models often miss the finer details needed for accurate detection. This outcome can result in lower accuracy, making early and precise diagnosis more difficult. Methods: This study presents an enhanced U-Net model integrated with a Capsule Network (called UCapsNet) to overcome the limitations of conventional techniques. Our approach improves segmentation by leveraging higher filter counts and skip connections, while the capsule network enhances classification by preserving spatial hierarchies through dynamic routing. The proposed UCapsNet model operates in two stages: first, it segments tumor regions using an enhanced U-Net, followed by a classification of the segmented images with the capsule network. Results: We have tested our model against well-known pre-trained models, including VGG-19, DenseNet, MobileNet, ResNet-50, and Xception. By properly addressing the limitations found in previous studies and using a capsule network trained on the Breast Ultrasound Image (BUSI) dataset, our model resulted in top-achieving impressive precision, recall, and accuracy rates of 98.12%, 99.52%, and 99.22%, respectively. Conclusions: By combining the U-Net's powerful segmentation capabilities with the capsule network's high classification accuracy, UCapsNet boosts diagnostic precision and addresses key weaknesses in existing methods. The findings demonstrate that the proposed model is not only more effective in detecting tumors but also more reliable for practical applications in clinical settings. [ABSTRACT FROM AUTHOR]

Simple Summary: Lung nodules detected by computed tomography (CT) often require invasive procedures for definitive diagnosis. With the increasing use of CT, incidental lung nodules have increased significantly. An adjunctive blood-based biomarker test that predicts lung cancer risk could reduce unnecessary interventions and focus diagnostic efforts on high-risk patients. This study introduces a blood-based biomarker test that predicts lung cancer risk in CT-detected nodules with a sensitivity of 78.4% (95% CI: 75.7–81.1) and a specificity of 93.1% (95% CI: 90.0–96.3). Background and Objectives: Lung nodules detected by chest computed tomography (CT) often require invasive biopsies for definitive diagnosis, leading to unnecessary procedures for benign lesions. A blood-based biomarker test that predicts lung cancer risk in CT-detected nodules could help stratify patients and direct invasive diagnostics toward high-risk individuals. Methods: In this multicenter, single-blinded clinical trial, we evaluated a test measuring plasma levels of p53, anti-p53 autoantibodies, CYFRA 21-1, and anti-CYFRA 21-1 autoantibodies in patients with CT-detected lung nodules. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated, and subgroup analyses by gender, age, and smoking status were performed. A total of 1132 patients who had CT-detected lung nodules, including 885 lung cancer cases and 247 benign lesions, were enrolled from two academic hospitals in South Korea. Results: The test demonstrated a sensitivity of 78.4% (95% CI: 75.7–81.1) and specificity of 93.1% (95% CI: 90.0–96.3) in predicting lung cancer in CT-detected nodules. The PPV was 97.6%, and the NPV was 54.6%. Performance was consistent across gender (sensitivity 79.3% in men and 76.8% in women) and age groups, with a specificity of 93.4% in men and 92.7% in women. Stage I lung cancer was detected with a sensitivity of 80.6%. Conclusions: The Lung Cancer test based on 9G technology presented here offers a non-invasive method for stratifying lung cancer risk in patients with CT-detected nodules. Its integration into clinical practice could reduce unnecessary interventions and foster earlier detection. [ABSTRACT FROM AUTHOR]

Simple Summary: Prognosis, early detection, and relapse identification are jointly crucial factors for effective lung cancer treatment. Blood-based liquid biopsy, a non-invasive method, can provide solutions to the deadlocks in the management of this widespread cancer type by eliciting biomarkers. In this research, an integrated approach is developed, which combines the use of databases and experimental validation to find adequate biomarkers in non-small-cell lung cancer. The arising results, concerning miR-29a-3p in exosomes and lncRNA H19 in cfRNA, widen the horizons for identifying and exploiting promising biomarkers in non-small-cell lung cancer. Background and Objective: Lung cancer, the second most prevalent cancer globally, poses significant challenges in early detection and prognostic assessment. Despite advancements in targeted therapies and immunotherapy, the timely identification of relapse remains elusive. Blood-based liquid biopsy biomarkers, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating-free RNAs (cfRNAs), and extracellular vesicles (EVs)/exosomes, offer promise for non-invasive monitoring. Methods: We employ a comprehensive approach integrating miRNA/lncRNA/metabolomic datasets, following a mixed-methods content analysis, to identify candidate biomarkers in NSCLC. NSCLC-associated miRNA/gene/lncRNA associations were linked to in silico-derived molecular pathways. Results: For data validation, mass spectrometry-based untargeted metabolomics of plasma EVs highlighted miRNA/lncRNA/metabotypes, linking "glycerophospholipid metabolism" to lncRNA H19 and "alanine, aspartate and glutamate metabolism" to miR-29a-3p. Prognostic significance was established for miR-29a-3p, showing lower expression in NSCLC patients with disease progression compared to stable disease (p = 0.004). Kaplan–Meier survival analysis indicated that patients with miR-29a-3p under-expression had significantly shorter overall survival (OS) (p = 0.038). Despite the expression of lncRNA H19 in plasma EVs being undetected, its expression in plasma cfRNAs correlated significantly with disease progression (p = 0.035). Conclusions: Herein, we showcase the potential of plasma EV-derived miR-29a-3p as a prognostic biomarker and underscore the intricate interplay of miRNAs, lncRNAs, and metabolites in NSCLC biology. Our findings offer new insights and avenues for further exploration, contributing to the ongoing quest for effective biomarkers in early-stage NSCLC. [ABSTRACT FROM AUTHOR]

Simple Summary: Since the U.S. National Lung Screening Trial (NLST) demonstrated the efficacy of low-dose computed tomography (LDCT) for early lung cancer detection, LDCT has emerged as a critical tool for identifying lung cancer in high-risk populations. This retrospective study evaluates trends in non-small cell lung cancer (NSCLC) staging at Kaohsiung Medical University Hospital (KMUH) from 2011 to 2020, with a particular focus on the effects of LDCT screening, which was introduced in 2013. We examined correlations between the number of LDCT screenings and NSCLC stage distribution, emphasizing early-stage (stages 0 and I) and late-stage (stage IV) diagnoses. Additionally, we assessed histopathological differences between adenocarcinoma and squamous cell carcinoma identified via LDCT and evaluated the impact of early diagnosis on five-year survival rates. Background: Low-dose computed tomography (LDCT) has been widely adopted for lung cancer screening due to its proven ability to reduce lung cancer mortality, especially among high-risk populations. Methods: This retrospective study aims to evaluate the impact of LDCT screening on non-small cell lung cancer (NSCLC) staging at Kaohsiung Medical University Hospital (KMUH) from 2011 to 2020, following the introduction of LDCT in 2013. The study examines the correlation between LDCT screening volume and changes in the distribution of NSCLC stages, particularly early-stage (stages 0 and I) and late-stage (stage IV) diagnoses. Additionally, it explores the differences in histopathological subtypes, focusing on adenocarcinoma and squamous cell carcinoma, and assesses the impact of early detection on five-year survival rates. Results: The results show a significant increase in early-stage NSCLC diagnoses, particularly in adenocarcinoma cases, where early-stage diagnoses rose from 10.4% in 2010 to 38.7% in 2019. However, the number of stage IV cases remained stable, indicating that LDCT may not substantially reduce late-stage diagnoses. Pearson's correlation analysis demonstrated a strong positive correlation between LDCT screening and early-stage NSCLC detection, particularly for adenocarcinoma (p < 0.001), though the early detection of squamous cell carcinoma and small cell carcinoma remained limited. Conclusions: The study concludes that LDCT screening plays a crucial role in improving early NSCLC detection and five-year survival rates. Future research should focus on optimizing screening strategies to capture more at-risk populations and enhance the detection of harder-to-diagnose subtypes like squamous cell carcinoma. [ABSTRACT FROM AUTHOR]

Simple Summary: Hepatitis B virus (HBV)—hepatitis delta virus (HDV) coinfection accounts for an estimated 8–10% of cases of hepatocellular carcinoma (HCC) worldwide. Recent meta-analyses suggest that HDV increases the risk of HCC associated with HBV, but its pathogenic role remains poorly elucidated. HCC related to HBV–HDV coinfection tends to be diagnosed by surveillance but in the background of severe liver dysfunction, which has therapeutic implications, with a high rate of liver transplantation, and high mortality in patients not eligible for liver transplantation. Hepatitis delta virus (HDV) infection requires the presence of hepatitis B virus (HBV), and chronic HBV–HDV coinfection is considered the most severe form of viral hepatitis. When compared with HBV mono-infection, HBV–HDV coinfection is associated with higher rates of liver cirrhosis and hepatocellular carcinoma (HCC). In this review, we aim to elucidate the complex relationship between HDV infection and the development of HCC. The exact mechanisms underlying the carcinogenic potential of HDV remain to be fully elucidated. Evidence suggests that HDV has both indirect and direct oncogenic effects. Indirect effects promote accelerated progression to liver cirrhosis, which results in a different tumor microenvironment. Direct oncogenic effects are suggested by a distinct molecular signature. The recent epidemiological data regarding HBV–HDV coinfection should make us reconsider the HCC screening strategy, with special focus in younger non-cirrhotic patients. Finally, treating HCC in patients with chronic HDV poses unique challenges due to the complex interplay between HBV and HDV and the severity of liver disease. An in-depth understanding of the epidemiology and pathophysiology of HDV infection and carcinogenesis is essential to improve disease management in this high-risk population. [ABSTRACT FROM AUTHOR]

Simple Summary: This study focused on the identification of rare variants that are associated with high-risk colorectal cancer (CRC) from the Middle Eastern region. This study analyzed DNA samples from 146 patients with CRC and from 1395 healthy individuals. We identified rare inactivating variants in the APC gene that are strongly linked to CRC, increasing the risk approximately 60-fold. Other significant genes harboring rare damaging variants were also identified. These results may have implications for genetic counseling and the early detection of CRC in the Middle Eastern population. Background: Colorectal cancer (CRC) displays a complex pattern of inheritance. It is postulated that much of the missing heritability of CRC is enriched in high-impact rare alleles, which might play a crucial role in the etiology and susceptibility of CRC. Methods: In this study, an exome-wide association analysis was performed in 146 patients with high-risk CRC in the Middle East and 1395 healthy controls. The aim was to identify rare germline variants in coding regions and their splicing sites associated with high-risk CRC in the Middle Eastern population. Results: Rare inactivating variants (RIVs) in APC had the strongest association with high-risk CRC (6/146 in cases vs. 1/1395 in controls, OR = 59.7, p = 5.13 × 10−12), whereas RIVs in RIMS1, an RAS superfamily member, were significantly associated with high-risk CRC (5/146 case vs. 2/1395 controls, OR = 24.7, p = 2.03 × 10−8). Rare damaging variants in 17 genes were associated with high-risk CRC at the exome-wide threshold (p < 2.5 × 10−6). Based on the sequence kernel association test, nonsynonymous variants in six genes (TNXB, TAP2, GPSM3, ADGRG4, TMEM229A, and ANKRD33B) had a significant association with high-risk CRC. RIVs in APC—the most common high-penetrance genetic factor—were associated with patients with high-risk CRC in the Middle East. Individuals who inherited APC RIVs had an approximate 60-fold increased risk of developing CRC and were likely to develop the disease earlier. Conclusions: We identified new potential CRC predisposition variants in other genes that could play a role in CRC inheritance. However, large collaborative studies are needed to confirm the association of these variants with high-risk CRC. These results provide information for counseling patients with high-risk CRC and their families in our population. [ABSTRACT FROM AUTHOR]

Simple Summary: Environmental pollution caused by nano- and microplastics (MPs) is widespread and has become a global issue. There is a confirmed accumulation of MPs in animal and human tissues, raising concerns about potential health effects. The accumulation of NMPs in human tissues, as well as their genotoxicity, mutagenicity, and impact on cancer development, is a relatively new area of research that presents several challenges, mainly related to instrumental limitations and ensuring quality assurance and quality control (QA/QC) in studies of both exposure and subsequent fate in the body, such as translocation and possible accumulation. Background: Humans cannot avoid plastic exposure due to its ubiquitous presence in the natural environment. The waste generated is poorly biodegradable and exists in the form of MPs, which can enter the human body primarily through the digestive tract, respiratory tract, or damaged skin and accumulate in various tissues by crossing biological membrane barriers. There is an increasing amount of research on the health effects of MPs. Most literature reports focus on the impact of plastics on the respiratory, digestive, reproductive, hormonal, nervous, and immune systems, as well as the metabolic effects of MPs accumulation leading to epidemics of obesity, diabetes, hypertension, and non-alcoholic fatty liver disease. MPs, as xenobiotics, undergo ADMET processes in the body, i.e., absorption, distribution, metabolism, and excretion, which are not fully understood. Of particular concern are the carcinogenic chemicals added to plastics during manufacturing or adsorbed from the environment, such as chlorinated paraffins, phthalates, phenols, and bisphenols, which can be released when absorbed by the body. The continuous increase in NMP exposure has accelerated during the SARS-CoV-2 pandemic when there was a need to use single-use plastic products in daily life. Therefore, there is an urgent need to diagnose problems related to the health effects of MP exposure and detection. Methods: We collected eligible publications mainly from PubMed published between 2017 and 2024. Results: In this review, we summarize the current knowledge on potential sources and routes of exposure, translocation pathways, identification methods, and carcinogenic potential confirmed by in vitro and in vivo studies. Additionally, we discuss the limitations of studies such as contamination during sample preparation and instrumental limitations constraints affecting imaging quality and MPs detection sensitivity. Conclusions: The assessment of MP content in samples should be performed according to the appropriate procedure and analytical technique to ensure Quality and Control (QA/QC). It was confirmed that MPs can be absorbed and accumulated in distant tissues, leading to an inflammatory response and initiation of signaling pathways responsible for malignant transformation. [ABSTRACT FROM AUTHOR]

Simple Summary: Patients with advanced stages of breast cancer are at high risk of the cancer metastasizing or spreading to areas of the central nervous system, including the brain, cerebrospinal fluid, and the protective tissue layers of the brain, termed leptomeninges. Although guidelines recommend screening for central nervous system disease in other cancers, such as non-small cell lung cancer, there are no such recommendations for patients with breast cancer unless they have symptoms such as headaches, weakness, or vomiting. This review discusses the evidence behind screening for breast cancer that has spread to the central nervous system, as well as new methods of diagnosis, including specialized imaging, serum testing, and cerebrospinal fluid analysis. Brain and leptomeningeal metastases are complications of breast cancer with high rates of morbidity and mortality and have an estimated incidence of up to 30%. While National Comprehensive Cancer Network (NCCN) guidelines recommend screening for central nervous system metastasis in other neurotropic cancers such as non-small cell lung cancer, there are no such recommendations for asymptomatic breast cancer patients at any stage of disease. This review highlights ongoing studies into screening and diagnostics for breast cancer with brain and leptomeningeal metastasis (BCBLM) as they relate to patient outcomes and prognostication. These include imaging methods such as MRI with novel contrast agents with or without PET/CT, as well as 'liquid biopsy' testing of the cerebrospinal fluid and serum to analyze circulating tumor cells, genomic material, proteins, and metabolites. Given recent advances in radiation, neurosurgery, and systemic treatments for BCBLM, screening for CNS involvement should be considered in patients with advanced breast cancer as it may impact treatment decisions and overall survival. [ABSTRACT FROM AUTHOR]

Simple Summary: A CRISPR knockout screen was performed targeting all genes of the human genome with about 12 knockout sgRNAs each. By comparing pancreatic cancer cells with high or low metastatic capacity, genes were identified that affect the viability of metastatic cells while not affecting cell viability of non-metastatic cells. Further functional studies looked at some of the identified candidate genes. For one of the metastasis-related genes—MYBL2—a change in its interaction with another regulator gene was found to be implicated in metastasis. Background/Objectives: Early and aggressive metastasis is a major feature of pancreatic ductal adenocarcinoma. Understanding the processes underlying metastasis is crucial for making a difference to disease outcome. Towards these ends, we looked in a comprehensive manner for genes that are metastasis-specific. Methods: A genome-wide CRISPR-Cas9 gene knockout screen with 259,900 single guide RNA constructs was performed on pancreatic cancer cell lines with very high or very low metastatic capacity, respectively. Functional aspects of some of the identified genes were analysed in vitro. The injection of tumour cells with or without a gene knockout into mice was used to confirm the effect on metastasis. Results: The knockout of 590 genes—and, with higher analysis stringency, 67 genes—affected the viability of metastatic cells substantially, while these genes were not vital to non-metastasizing cells. Further evaluations identified different molecular processes related to this observation. One of the genes was MYBL2, encoding for a well-known transcription factor involved in the regulation of cell survival, proliferation, and differentiation in cancer tissues. In our metastasis-focussed study, no novel functional activity was detected for MYBL2, however. Instead, a metastasis-specific transformation of its genetic interaction with FOXM1 was observed. The interaction was synergistic in cells of low metastatic capacity, while there was a strong switch to a buffering mode in metastatic cells. In vivo analyses confirmed the strong effect of MYBL2 on metastasis. Conclusions: The genes found to be critical for the viability of metastatic cells form a basis for further investigations of the processes responsible for triggering and driving metastasis. As shown for MYBL2, unexpected processes of regulating metastasis might also be involved. [ABSTRACT FROM AUTHOR]

Simple Summary: Lynch syndrome (LS) is an autosomal dominant disorder linked to increased risks of colorectal and endometrial cancers, caused by pathogenic variants in MMR genes (MLH1, MSH2, MSH6). In a cohort of 42,828 participants from the Taiwan Precision Medicine Initiative (TPMI), 89 individuals carried MMR gene variants: 25% MLH1, 53% MSH2, and 22% MSH6, with a prevalence of 1 in 481. Cancer incidence rates were 40.9% for MLH1, 29.8% for MSH2, and 40% for MSH6 carriers. Colonoscopy screening revealed no significant differences in polyp prevalence compared to controls. The study underscores the need for improved LS diagnosis and surveillance in the Taiwanese population. Lynch syndrome (LS) is an autosomal dominant disorder characterized by increased risks of colorectal and endometrial cancers. LS is defined by pathogenic variants in mismatch repair (MMR) genes, including MLH1, MSH2, and MSH6. Data on the prevalence and associated cancer risks of LS in the Han Chinese population remain limited. In this study, using a broad biobank approach through the Taiwan Precision Medicine Initiative (TPMI), we identified LS-associated MMR gene variants within a cohort of 42,828 participants from a Taiwanese medical center. A total of 89 individuals were found to carry pathogenic MMR variants: MLH1 (n = 22, 25%), MSH2 (n = 47, 53%), and MSH6 (n = 20, 22%). The overall prevalence of MMR variants was calculated, and cancer incidence rates among carriers were determined. The prevalence of MMR variants in the study population was 1 in 481. The distribution of MLH1, MSH2, and MSH6 variants were 24.7%, 52.8%, and 22.5%, respectively. Cumulative cancer incidence rates of carriers were 40.9% for MLH1 carriers, 29.8% for MSH2, and 40% for MSH6. Among the 19 individuals who underwent colonoscopy screening, the prevalence of polyps was similar to that of the control group (adenoma detection rate: 32% vs 26%, p = 0.585). A meticulous analysis of the detected polyps in seven participants, considering factors such as location, size, morphology, and pathological features, showed no significant differences from controls. A significant cancer risk is associated with LS-related MMR variants in the Taiwanese population. The apparent under diagnosis of LS highlights the urgent need for enhanced surveillance and genetic counseling in this demographic. Our findings suggest that adjustments in the current screening protocols may be warranted to better identify and manage at-risk individuals. [ABSTRACT FROM AUTHOR]

Simple Summary: Prostate cancer (PCa) is a heterogeneous disease and the second most diagnosticated cancer in men. Epidemiological information is essential to adopt the strategy of early detection. In Mexico there is paucity of epidemiological data. We observed that Mexican men present aggressive disease at diagnosis with metastatic symptoms. Seems the Mexican race is a risk factor for PCa, as African men. "OPUS program " is the actual screening protocol for Mexican men. We describe the concernment of early detection of PCa in the Mexican population to reduce the rate of metastases. Introduction: Epidemiological data are crucial for adopting primary and secondary prevention strategies and to develop screening protocols against prostate cancer (PCa). Despite the comprehensive characterization of PCa across White and Black men, there is a lack of data from the Mexican population. This manuscript presents data from the Can.Prost registry that captures PCa trends over the past two decades in Mexico City; furthermore, we aimed to compare clinical differences and oncological outcomes before and after the promotion of early detection actions through a campaign against PCa that occurred in 2014. Methods: A retrospective observational study on newly diagnosed Mexican PCa patients was carried out at the Instituto Nacional de Cancerología (INCan) in Mexico City. During 2014 and 2015, a project for the early diagnosis of PCa ("OPUS program") was launched in the aforementioned tertiary hospital. Starting at the age of 45 years, all men were invited for a PSA measurement and a specialist urologist consultation. All individuals with clinical or biochemical suspicion of PCa (PSA > 4 ng/mL), in the context of age and prostate volume, underwent ultrasound-guided transrectal prostate biopsy. Then, patients with pathologically confirmed prostate cancer were stratified according to the year of diagnosis: Group A accounted for those diagnosed between 2000 and 2014 and Group B for those patients diagnosed in the timeframe of 2015–2021. Comparisons of PCa characteristics, treatment modalities and oncologic outcomes between Group A and B were performed. Results: Overall, we collected data from 2759 PCa patients from 2000 to 2021. The median PSA at baseline was 32 ng/mL, and 25% had a family history of PCa. Overall, 25.8% were asymptomatic and 46% had a non-metastatic presentation. After the OPUS campaign, PSA at diagnosis was significantly lower across all age groups. The incidence of PCa diagnosis in asymptomatic men was higher (31.4% vs. 19.9%) and a higher proportion of men were diagnosed with organ-confined, palpable disease (46% vs. 28%) (p < 0.001). The rate of patients eligible for active/radical treatment was higher after the OPUS campaign (patients who received surgery increased from 12.78% to 32.41%; patients who underwent radiation increased from 28.38% to 49.61%). The proportion of patients diagnosed with non-clinically significant disease was negligible and remained stable across time. Conclusions: PCa in Mexican patients displays aggressive features at diagnosis, whereas the rate of non-significant disease is negligible. The introduction of early detection strategies may lead to lower symptomatic and metastatic PCa and higher opportunities for radical treatment. This emphasizes the need for public awareness and for adjustment of screening strategies to the peculiarities of the Mexican population. [ABSTRACT FROM AUTHOR]

Simple Summary: Prostate cancer is the second most common cancer diagnosed in men and yet is the second leading cause of cancer-related deaths worldwide. This is often due to the cancer being misdiagnosed as benign prostatic hyperplasia or missed entirely due to the limited nature of the most popular detection method, prostate-specific antigen levels. The aim of this study was to assess the potential of G-protein-coupled receptor-associated sorting protein 1 (GASP-1) as a valid prostate cancer biomarker. Prostate tissue samples from healthy, benign prostatic hyperplasia, and prostate cancer patients were subjected to anti-GASP-1 polyclonal antibodies in immunohistochemical staining and enzyme-linked immunosorbent assay analyses, showing that GASP-1 levels were significantly greater in prostate cancer patients when compared to benign prostatic hyperplasia and healthy patients. Specifically in prostate cancer patients, there was a positive correlation between GASP-1 overexpression and the severity of the prostate cancer. Background/Objectives: Prostate cancer (PCa) is a prevalent malignancy, necessitating accurate diagnostic methods to distinguish it from benign conditions such as benign prostatic hyperplasia (BPH). Current diagnostic tools, relying primarily on serum prostate-specific antigen (PSA) levels, lack specificity, leading to an over-diagnosis and unnecessary treatment of patients with benign conditions. This study explores G-protein-coupled receptor-associated sorting protein 1 (GASP-1) as a more sensitive biomarker for PCa detection. Methods: Prostate tissue microarrays of healthy, BPH, and prostate cancer patients with different Gleason scores were studied. Polyclonal antibodies targeted against GASP-1 were used for routine immunohistochemistry (IHC) and enzyme-linked immunosorbent assay (ELISA) analyses. Results: The results indicated a 5-fold difference in serum GASP-1 levels between BPH and PCa, which was validated through GASP-1 IHC. Furthermore, a novel scoring system, the H-score, assesses GASP-1 granules' intensity and size, revealing a clear distinction between BPH and PCa. An additional analysis of GASP-1 expression between PCa cases with different Gleason scores reveals that GASP-1 overexpression correlates with PCa severity, providing insights into disease progression. Conclusions: The study supports GASP-1′s role as a promising diagnostic marker, supplementing PSA testing, and offering improved risk stratification for PCa. Additionally, an open-source software system is introduced for an efficient GASP-1 granule color analysis, enhancing diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Simple Summary: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer that is challenging to diagnose and treat. Current methods to measure disease burden, monitor treatment response, and predict disease progression have significant limitations. This study explores the utility of circulating neoplastic-immune hybrid cells (CHCs) as a novel blood-based biomarker. We find that pre-operative CHC levels can predict the presence of metastatic disease that may only be detected during surgical exploration, as well as identify patients who are likely to experience rapid metastatic progression after surgery. Further, CHC levels and protein expression patterns are reflective of disease response to chemotherapy. Collectively, these results suggest that CHCs could serve as crucial indicators of disease status and treatment efficacy, offering great potential to enhance survival outcomes for patients with PDAC. Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) presents significant diagnostic and prognostic challenges, as current biomarkers frequently fail to accurately stage disease, predict rapid metastatic recurrence (rPDAC), or assess response to neoadjuvant therapy (NAT). We investigated the potential for circulating neoplastic-immune hybrid cells (CHCs) as a non-invasive, multifunctional biomarker for PDAC. Methods: Peripheral blood specimens were obtained from patients diagnosed with PDAC. CHCs were detected by co-expression of pan-cytokeratin and CD45, normalized to 50,000 peripheral blood mononuclear cells. rPDAC was defined as metastatic recurrence within six months of margin-negative pancreatectomy. Cyclic immunofluorescence (CyCIF) analyses compared hybrid phenotypes in blood and tumors. Results: Blood samples were collected from 42 patients with PDAC prior to resection. Those with radiographically occult metastatic disease and rPDAC had higher preoperative CHC numbers compared to patients who did not (65.0 and 74.4, vs. 11.52 CHCs; p < 0.001). Patients with complete or near-complete pathologic responses to NAT had lower preoperative CHC numbers than partial and/or non-responders (1.7 vs. 13.1 CHCs; p = 0.008). When assessed longitudinally, those with partial pathologic response saw CHC levels become undetectable while on treatment but increase in the interval between NAT completion and resection. In contrast, patients with poor responses or development of metastatic disease experienced persistent CHC detection during therapy or rising levels prior to radiographic evidence of metastases. Further, in metastatic PDAC patients, treatment-induced phenotypic changes in hybrid cells mirrored those in paired metastatic tumor samples. Conclusions: CHC enumeration and phenotyping display promise as a real-time indicator of disease burden, recurrence risk, and treatment response in PDAC. CHCs have great potential as tumor-derived biomarkers to optimize therapeutic strategies and improve survival in patients with PDAC. [ABSTRACT FROM AUTHOR]

Simple Summary: Despite the benefits of stool-based tests for colorectal cancer screening, the return rate is low. In this study, we tested if adding a tailored, one-page educational flyer to the mailed stool kits targeted at rural Midwestern residents in the USA improved the kit return rates. Overall, there was no significant difference in return rate between the groups that did and did not receive the flyer. However, females and older participants were more likely to return the kits compared to males and younger participants. To improve the kit return rates of male and younger participants, future programs should consider how the content and design of the flyer can be more closely tailored to the audience's needs and preferences as well as how the flyer can be combined with other approaches (e.g., reminders). Background/Objectives: Stool-based tests, such as the fecal immunochemical test (FIT), have been widely used for increasing colorectal cancer (CRC) screening. Small media, such as printed materials or flyers, are known to be an effective intervention to increase CRC screening by fecal tests. However, more evidence is needed to determine whether such small media are effective in improving screening uptake of a mailed FIT intervention targeted at rural populations in the USA. Methods: In this randomized study, 1230 FIT kits were mailed from July to December 2022 to rural Nebraskans aged 45–74 who were not up to date on CRC screening. Half of the participants (n = 608) also received a tailored, one-page, gender-specific educational flyer created based on focus groups with rural residents. Logistic regression was used to determine predictors of returning the FIT. Results: Study participants were predominantly female (76%), non-Hispanic White (83%), and within the age group of 55–64 (43%). Overall, 192 (15.6%) kits were returned (16.1% from the flyer group; 15.1% from the no-flyer group). However, we found no significant differences between the flyer and no-flyer groups (Adjusted Odds Ratio [AOR]: 1.21; 95% CI: 0.88–1.66). Females (AOR: 1.78; 95% CI: 1.19–6.14) and the oldest (65–74) age group (AOR: 5.03; 95% CI: 2.78–8.47) were more likely to return FIT kits than males and the youngest (45–54) age group. Conclusions: A tailored educational flyer was not effective in improving the CRC-screening-rate-by-mailed-FIT approach for rural populations. Future research should explore the content, timing, and mode of delivery of educational interventions as well as other multi-component strategies to improve screening rates. Public health officials might also consider developing strategies targeted at males and younger (45–54) age groups. [ABSTRACT FROM AUTHOR]

Simple Summary: Currently, pathologists use ultrastaging to detect whether cancer has spread to the lymph nodes. This process is time-consuming and expensive. Our pilot study explored the use of a deep learning algorithm to help detect cancer spread to lymph nodes of early-stage cervical cancer patients. Using this technology could make the detection process faster, more efficient, and less costly. We evaluated an algorithm that was originally designed to identify cancer spread to lymph nodes in breast and colon cancer in cervical cancer patients. The study included 21 women with different types of early-stage cervical cancer. The algorithm was used to analyze 47 lymph node samples and successfully identified all cases where cancer had spread, showing 100% accuracy. Although the algorithm was initially developed for other cancers, it proved highly effective in this new population. More prospective research in a larger group of patients is needed to confirm its cost-effectiveness. Background/objectives: Pathological ultrastaging, an essential part of sentinel lymph node (SLN) mapping, involves serial sectioning and immunohistochemical (IHC) staining in order to reliably detect clinically relevant metastases. However, ultrastaging is labor-intensive, time-consuming, and costly. Deep learning algorithms offer a potential solution by assisting pathologists in efficiently assessing serial sections for metastases, reducing workload and costs while enhancing accuracy. This proof-of-principle study evaluated the effectiveness of a deep learning algorithm for SLN metastasis detection in early-stage cervical cancer. Methods: We retrospectively analyzed whole slide images (WSIs) of hematoxylin and eosin (H&E)-stained SLNs from early-stage cervical cancer patients diagnosed with an SLN metastasis with either H&E or IHC. A CE-IVD certified commercially available deep learning algorithm, initially developed for detection of breast and colon cancer lymph node metastases, was employed off-label to assess its sensitivity in cervical cancer. Results: This study included 21 patients with early-stage cervical cancer, comprising 15 with squamous cell carcinoma, five with adenocarcinoma, and one with clear cell carcinoma. Among these patients, 10 had macrometastases and 11 had micrometastases in at least one SLN. The algorithm was applied to evaluate H&E WSIs of 47 SLN specimens, including 22 that were negative for metastasis, 13 with macrometastases, and 12 with micrometastases in the H&E slides. The algorithm detected all H&E macro- and micrometastases with 100% sensitivity. Conclusions: This proof-of-principle study demonstrated high sensitivity of a deep learning algorithm for detection of clinically relevant SLN metastasis in early-stage cervical cancer, despite being originally developed for adenocarcinomas of the breast and colon. Our findings highlight the potential of leveraging an existing algorithm for use in cervical cancer, warranting further prospective validation in a larger population. [ABSTRACT FROM AUTHOR]

Simple Summary: Tubo-ovarian cancers are associated with high mortality as they are often not detected until a late stage. Early diagnosis is associated with better patient outcomes, but there are currently no effective screening measures. We explore whether an optical imaging catheter can detect early or occult lesions in the fallopian tubes. This device collects three-dimensional structural images of tissue through optical coherence tomography (OCT) simultaneously with functional imaging through autofluorescence imaging (AFI). We image ex vivo fallopian tubes from n = 28 patients (n = 7 cancer patients) and explore eleven imaging biomarkers for their ability to distinguish early or occult disease. We find that high-grade serous ovarian carcinomas can be visually distinguished through this approach, and that there are several quantitative changes within the area of lesion and throughout the specimen that can be measured through these imaging biomarkers. We conclude that this approach shows promise and merits further investigation of its diagnostic potential. Background: There are currently no effective screening measures to detect early or occult tubo-ovarian cancers, resulting in late-stage detection and high mortality. This work explores whether an optical imaging catheter can detect early-stage tubo-ovarian cancers or precursor lesions where they originate in the fallopian tubes. Methods: This device collects co-registered optical coherence tomography (OCT) and autofluorescence imaging (AFI). OCT provides three-dimensional assessment of underlying tissue structures; autofluorescence imaging provides functional contrast of endogenous fluorophores. Ex vivo fallopian tubes (n = 28; n = 7 cancer patients) are imaged; we present methods for the calculation of and analyze eleven imaging biomarkers related to fluorescence, optical attenuation, and OCT texture for their potential to detect tubo-ovarian cancers and other lesions of interest. Results: We visualize folded plicae, vessel-like structures, tissue layering, hemosiderin deposits, and regions of fibrotic change. High-grade serous ovarian carcinoma appears as reduced autofluorescence paired with homogenous OCT and reduced mean optical attenuation. Specimens containing cancerous lesions demonstrate a significant increase in median autofluorescence intensity and decrease in Shannon entropy compared to specimens with no lesion. Non-cancerous specimens demonstrate an increase in optical attenuation in the fimbriae when compared to the isthmus or the ampulla. Conclusions: We conclude that this approach shows promise and merits further investigation of its diagnostic potential. [ABSTRACT FROM AUTHOR]

Simple Summary: Receipt of an abnormal Papanicolaou result often leads to a psychosocial burden. This study reported independent predictors of higher psychosocial burden prior to diagnostic procedures (colposcopy/biopsy/endocervical curettage) in women who received an abnormal Papanicolaou screening result. Worry in women with a positive Papanicolaou screening test before diagnostics is associated with oral contraceptives use, alcohol use, and low knowledge of the meaning of term precancerous, while satisfaction with information/support is linked to psychological distress. Providing uniform and explicit information about cervical cancer screening to better understand the meaning of the term dysplasia/precancerous and identifying women at risk of psychosocial burden may help protect against this potential harm in women who receive a positive cervical-cancer-screening result and may facilitate their intention to undergo further diagnostic procedures. Background/Objectives: This study aimed to evaluate psychosocial burden and its associated factors in women who were referred for additional diagnostic procedures following receipt of a positive cervical-cancer-screening smear result. Methods: A cross-sectional study was performed in a consecutive cohort of only women who received an abnormal Papanicolaou screening result and therefore presented to a gynecologist for additional diagnostic examinations (colposcopy/biopsy/endocervical curettage) at the Clinic for Gynecology and Obstetrics of the Clinical Center. Multivariate linear regression was used for data analysis, with Bonferroni correction applied for multiple comparisons. Results: Significant independent predictors for the occurrence of psychosocial burden–worry in women with a positive Papanicolaou screening test before diagnostic procedures were the use of oral contraceptives (β = −0.174, p < 0.001), alcohol consumption (β = 0.188, p < 0.001), anxiety (β = −0.189, p = 0.001), high burden of depressive symptoms (β = 0.191, p = 0.001) and insufficient knowledge of the meaning of the term dysplasia/precancerous (β = −0.187, p < 0.001), according to the multivariate linear regression. The significant independent predictor for the occurrence of psychosocial burden–satisfaction with information/support in women with a positive Papanicolaou screening test before diagnostic procedures was psychological distress (β = −0.210, p = 0.001). Conclusions: Providing information in order to improve understanding of the term dysplasia/precancerous, as well as identifying which women are at risk of psychosocial burden, may help protect against this potential harm among women who receive a positive cervical-cancer-screening result and may facilitate their intention to undergo further diagnostic procedures. [ABSTRACT FROM AUTHOR]

Simple Summary: A colposcopy provides a comprehensive examination of the female genital tract, including the cervix, vagina, and vulva. However, its diagnostic accuracy, especially for vaginal lesions, remains suboptimal due to the challenge in detecting subtle alterations. Integrating artificial intelligence (AI) into colposcopy holds potential to enhance the detection rates of clinically important lesions. Our study pioneered the development of an AI algorithm capable of differentiating low-grade (LSILs) and high-grade (HSILs) squamous intraepithelial lesions in the vagina. The promising results we achieve in differentiating HPV-associated dysplastic lesions demonstrate that AI can significantly address the current challenges in medical practice. There are already promising results when using AI to detect HPV lesions in the cervix and anus, suggesting that in the future, this ubiquitous tool for lesion detection across different anatomical regions could be a reality. The future integration of these technologies into the colposcopy process could revolutionize healthcare, making early detection a reality for women worldwide. Background/Objectives: While human papillomavirus (HPV) is well known for its role in cervical cancer, it also affects vaginal cancers. Although colposcopy offers a comprehensive examination of the female genital tract, its diagnostic accuracy remains suboptimal. Integrating artificial intelligence (AI) could enhance the cost-effectiveness of colposcopy, but no AI models specifically differentiate low-grade (LSILs) and high-grade (HSILs) squamous intraepithelial lesions in the vagina. This study aims to develop and validate an AI model for the differentiation of HPV-associated dysplastic lesions in this region. Methods: A convolutional neural network (CNN) model was developed to differentiate HSILs from LSILs in vaginoscopy (during colposcopy) still images. The AI model was developed on a dataset of 57,250 frames (90% training/validation [including a 5-fold cross-validation] and 10% testing) obtained from 71 procedures. The model was evaluated based on its sensitivity, specificity, accuracy and area under the receiver operating curve (AUROC). Results: For HSIL/LSIL differentiation in the vagina, during the training/validation phase, the CNN demonstrated a mean sensitivity, specificity and accuracy of 98.7% (IC95% 96.7–100.0%), 99.1% (IC95% 98.1–100.0%), and 98.9% (IC95% 97.9–99.8%), respectively. The mean AUROC was 0.990 ± 0.004. During testing phase, the sensitivity was 99.6% and 99.7% for both specificity and accuracy. Conclusions: This is the first globally developed AI model capable of HSIL/LSIL differentiation in the vaginal region, demonstrating high and robust performance metrics. Its effective application paves the way for AI-powered colposcopic assessment across the entire female genital tract, offering a significant advancement in women's healthcare worldwide. [ABSTRACT FROM AUTHOR]

Simple Summary: Anal human papilloma virus (HPV) is the leading etiology of anal and cervical cancer. While cervical HPV screening for women is well established, anal HPV screening is restrained to a limited population and specific indications. Our group performed concomitant anal and cervical screening in 275 women at a single gynecologic appointment and revealed high-risk anal HPV in 91 women (33%). The present follow-up study intended to analyze the outcomes of these women during a specialized follow-up in a dedicated high-resolution anoscopy (HRA) outpatient clinic. Independent risk factors for anal HPV were anal intercourse and the presence of cervical HR-HPV with a three- and fourfold increased risk, respectively. Background: This study represents a follow-up analysis of the AnusGynecology (ANGY) study. Methods: This prospective, cross-sectional, single-center study recruited women for concomitant cervical and anal screening of HPV genotypes and cytology during a single appointment. All women with findings of either HPV or any type of dysplastic lesions on anal smears were offered follow-up in a specialized high-resolution anoscopy (HRA) outpatient clinic, representing the study cohort for this follow-up study. Results: Overall, 275 patients (mean age 42 ± 12) were included. Among them, 102 (37%) had cervical high-risk (HR) HPV. In total, HPV was (incidentally) revealed in 91 patients (33%) on anal smears, while any degree of anal squamous intraepithelial lesion (SIL) was found in 30 patients (11%), 6 if which were high-grade SIL (H-SIL). Furthermore, 10 out of 19 biopsies were positive (3 H-SIL lesions). Only half (48/91, 53%) of the women agreed to undergo the recommended specialized follow-up evaluation. Of them, 18 (38%) were diagnosed with dysplastic lesions (9 low grade (L-SIL) and 9 H-SIL, respectively) on biopsies, while the remaining visits revealed no abnormalities. Multivariable analysis revealed cervical HR-HPV infection (OR 4, 95% CI 2.2–7.5) and anal intercourse (OR 3.1, 95% CI 1.7–5.9) as independent risk factors for anal HR-HPV infection. Conclusions: Close follow-up of these women is hence strongly recommended. [ABSTRACT FROM AUTHOR]

Simple Summary: Radiologists often call back women whose screening mammograms show features that are suggestive of breast cancer for additional testing. Imaging modalities used for the additional testing of these women vary across countries and screening services; however, we do not know the imaging modality that best distinguishes between women who have and those who do not have breast cancer. We examined published studies to identify the best imaging modality that can be used for the testing of women who are recalled following mammography screening. We collected and analysed all the results in the studies published to date. Our analysis showed that contrast-enhanced mammography, digital breast tomosynthesis, magnetic resonance imaging, and ultrasound perform better than digital mammography in correctly identifying and classifying cancer lesions referred for additional testing; however, digital breast tomosynthesis and digital mammography perform better in correctly dismissing women who do not have cancer. There are variations in the assessment pathways for women recalled at screening, and the imaging assessment pathway with the best diagnostic outcome is poorly understood. This paper examines the efficacy of five imaging modalities for the assessment of screen-recalled breast lesions. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) strategy was employed to identify studies that assessed the efficacy of imaging modalities in the assessment of lesions recalled at screening from the following eight databases: Medline, Web of Science, Embase, Scopus, Science Direct, PubMed, CINAHL, and Global Health. Search terms included "Breast assessment" AND "Diagnostic Workup" OR "Mammography" AND "Digital Breast tomosynthesis" AND "contrast enhanced mammography and Magnetic Resonance imaging" AND "breast ultrasound". Studies that examined the performance of digital mammography (DM), digital breast tomosynthesis (DBT), handheld ultrasound (HHUS), contrast-enhanced mammography (CEM), and magnetic resonance imaging (MRI) in screen-recalled lesions were reviewed. Meta-analyses of these studies were conducted using the MetaDisc 2.0 software package. Results: Fifty-four studies met the inclusion criteria and examined between one and three imaging modalities. Pooled results of each imaging modality demonstrated that CEM has the highest sensitivity (95; 95% CI: 90–97) followed by MRI (93; 95% CI: 88–96), DBT (91; 95% CI: 87–94), HHUS (90; 95% CI: 86–93), and DM (85; 95% CI: 78–90). The DBT demonstrated the highest specificity (85; 95% CI: 75–91) followed by DM (77; 95% CI: 66–85), CEM (73; 95% CI: 63–81), MRI (69; 95% CI: 55–81), and HHUS (65; 95% CI: 46–80). Conclusions: The CEM, MRI, DBT, and HHUS demonstrate excellent performance in correctly identifying and classifying cancer lesions referred for diagnostic work-up, but HHUS, MRI, and CEM have a more limited ability to discriminate benign lesions than DBT and DM. [ABSTRACT FROM AUTHOR]