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Prostate cancer (PCa) is a prevalent male malignancy globally. Prolonged diagnostic intervals are associated with poorer outcomes, emphasizing the need to optimize this process. This study aimed to evaluate the doctor and primary care interval, research their impact on patient survival and explore opportunities to improve PCa diagnostic pathway in primary care. A retrospective cohort study using cancer patients' anonymised primary care data and data of the Slovenian Cancer Registry. The study found that the doctor interval had a median duration of 0 days (interquartile range ([IQR] 0–6) and primary care interval a median duration of 5 days (IQR 0–58). Longer intervals were observed in patients with more than two comorbidities, where general practitioners didn't have access to laboratory diagnostic tests within their primary health care centre and when patients first presented with symptoms (reported symptoms at first presentation: dysuria, lower urinary tract symptoms [LUTS], abdominal pain). The analysis also revealed a statistically significant association between lower 5-year survival rate and the accessibility of laboratory and ultrasound diagnostics in primary healthcare centres and a shorter 5-year survival of symptomatic patients in comparison to patients who were identified by elevated levels of prostate specific antigen (PSA). This study shows that treating suspected PCa in primary care has a significant impact on 5-year survival. Several factors contribute to better survival, including easy access to laboratory and abdominal ultrasound in primary care centres. The study highlights the complex array of factors shaping PCa diagnosis, beyond individual clinicians' skills, encompassing test and service availability. [ABSTRACT FROM AUTHOR]

Lung cancer, the leading cause of cancer-related deaths globally, remains a pressing health issue despite significant medical advances. The New York Lung Cancer Foundation brought together experts from academia, the pharmaceutical and biotech industries as well as organizational leaders and patient advocates, to thoroughly examine the current state of lung cancer diagnosis, treatment, and research. The goal was to identify areas where our understanding is incomplete and to develop collaborative public health and scientific strategies to generate better patient outcomes, as highlighted in our "Calls to Action." The consortium prioritized 8 different calls to action. These include (1) develop strategies to cure more patients with early-stage lung cancer, (2) investigate carcinogenesis leading to lung cancers in patients without a history of smoking, (3) harness precision medicine for disease interception and prevention, (4) implement solutions to deliver prevention measures and effective therapies to individuals in under-resourced countries, (5) facilitate collaborations with industry to collect and share data and samples, (6) create and maintain open access to big data repositories, (7) develop new immunotherapeutic agents for lung cancer treatment and prevention, and (8) invest in research in both the academic and community settings. These calls to action provide guidance to representatives from academia, the pharmaceutical and biotech industries, organizational and regulatory leaders, and patient advocates to guide ongoing and planned initiatives. [ABSTRACT FROM AUTHOR]

Purpose: To assess the association between food insecurity and colorectal cancer screening uptake in screening eligible participants in New York State. Methods: We analyzed 28,154 adults who participated in New York State Behavioral Risk Factor Surveillance System (BRFSS) from 2018 to 2021, were age-eligible for colorectal cancer screening based on the USPSTF guidelines at the time of survey administration and answered a version of the administered survey that included the module on food insecurity. Participants were defined as food insecure if they self-reported being always, usually, or sometimes stressed about having enough money to buy nutritious meals in the past 12 months. We compared demographic, healthcare access, overall health status, food insecurity by colorectal cancer screening status. Multivariable analyses were performed to assess the association of food insecurity and colorectal cancer screening status after adjusting for relevant covariates. Weighted analyses were performed using survey procedures to obtain population estimates. Results: Food insecurity was statistically significantly associated a decreased likelihood of being up to date on colorectal cancer screening (ORadj 0.83, 95% CI [0.72, 0.94]) and being ever screened for colorectal cancer (ORadj 0.74, 95% CI [0.64, 0.87]) after adjusting for overall health status, healthcare coverage, interview year, age, race/ethnicity, sex, educational attainment, and income. Health status, health coverage, age, and Non-Hispanic Black race/ethnicity showed positive, statistically significant association with ever being screened and with being up to date for colorectal cancer screening. Lower income, lower educational attainment, and non-Hispanic Asian race/ethnicity were statistically significant inverse predictors of ever being screened and being up to date on screening. Conclusion: This project assessed the association between food insecurity and colorectal cancer screening uptake using the BRFSS survey. Food insecurity may be an important predictor for colorectal cancer screening uptake in eligible adults in the United States. The results from the study can inform future interventions and policies designed to improve participation in routine colorectal cancer screening. [ABSTRACT FROM AUTHOR]

Purpose: There is limited evidence regarding body mass index (BMI) as an early marker of high-risk adenoma (HRA) at the time of screening colonoscopy. Because high-risk adenomas (HRA) can develop into colorectal cancer (CRC), BMI could serve as an important clinical predictor of future risk of CRC. Methods: We examined data from 1831 adults undergoing screening colonoscopy at the Forzani & MacPhail Colon Cancer Screening Center in Alberta, Canada. We fit multivariable logistic regression models to examine the association between BMI and HRA. Non-linear relationships for BMI on HRA were also evaluated using restricted cubic splines. Results: The mean BMI in patients with HRA was 28.2 kg/m2 compared to 27.4 kg/m2 in patients without adenomas (t test: p = 0.003). In the adjusted models, those with a BMI over 30 kg/m2 had 1.45 (95% CI 1.05–2.00) times the odds of HRA detected during colonoscopy compared to those with a BMI below 25 kg/m2. Examining BMI as continuous, the odds of HRA were 1.20 (95% CI 1.04–1.37) times higher for every 5 kg/m2 increase in BMI. Conclusion: The findings of this study suggest that excess body mass is associated with higher risk of HRA among a screening population and may be useful an early marker of future disease. [ABSTRACT FROM AUTHOR]

In blood smear images, there are difficulties in diagnosing blood cancer diseases like leukemia and lymphoma because of their various forms that appear in the human body. In this paper, a method for automatic detection of blood cancer is suggested that uses the EfficientNet-B3 architecture along with transfer learning techniques to improve accuracy and efficiency. We first fine-tuned the EfficientNet-B3 model, which was pre-trained on a large dataset consisting of annotated blood smear images, to capture pertinent features linked with blood malignant cells. To expedite the training process and adapt the model to our task, we use transfer learning. The proposed approach's results from our experiments show that it outperforms traditional deep learning models and state-of-the-art methods in blood cancer detection. Additionally, with high precision and recall rates, this model also detects different types of blood cancers with robustness in its performance since its accuracy is over 99%. This means that when used together with the EfficientNet-B3 architecture, transfer learning can help the developed methods generalize among different types of blood cancers and conditions. [ABSTRACT FROM AUTHOR]

Early detection of skin cancer ensures the survival of many cases. There are still challenges in segmenting dermoscopic skin lesion images. Artifacts in the lesion images, such as various dirt, hairs, low contrast, and unclear boundaries, are challenges that affect segmentation accuracy. Convolutional neural networks have brought success in skin lesion segmentation. U‐shaped and V‐shaped deep learning‐based segmentation architectures learn boundary information in the first layers. However, this information becomes weaker in the following layers. Herein, the Edge‐aTtention module is added to the V‐Net architecture to move edge information to the last layer, and the spatial and channel squeeze‐excitation module is added to emphasize high‐level features by recalibrating the channel information to learn lesion boundaries better. The scSEETV‐Net is supported by fusing the binary cross‐entropy, which calculates the loss on a pixel‐based, and the focal Twersky loss function, which has significant success in class imbalances. The proposed architecture achieves 0.9212 Jaccard and 0.9552 Dice in the ISIC2016 dataset, 0.8273 Jaccard and 0.8949 Dice in the ISIC2017 dataset, and 0.8070 Jaccard and 0.8831 Dice in the ISIC2018 dataset. Comparative analyses show that the proposed methodology outperforms the state‐of‐the‐art techniques in the literature. [ABSTRACT FROM AUTHOR]

Background and Aims: Despite its prioritization by the World Health Organization, improving access to cervical cancer screening remains a challenge in Ethiopia. Educated individuals, particularly teachers, are viewed as key influencers in promoting healthy lifestyles among youth and can significantly contribute to cervical cancer prevention. However, there is a notable gap in research regarding cervical cancer screening practices among female educators in the studied region. This study aimed to assess the cervical cancer screening practices and associated factors among female school teachers in Bahir Dar, Ethiopia, 2024. Methods: A school‐based cross‐sectional study was conducted from May 27 to June 28, 2024, involving 561 female school teachers in Bahir Dar City, using a stratified multistage sampling method. Data were gathered through a pretested structured questionnaire, entered in Epi‐data version‐4.6, and analyzed with SPSS version‐23. Bi‐variable and multivariable binary logistic regression analyses were performed, considering variables with p values < 0.05 as statistically significant. Multicollinearity was assessed, and the Hosmer–Lemeshow test evaluated the model's fit. Results: The proportion of female school teachers practicing cervical cancer screening was 14.6% (95% CI: 11.57%–17.66%). Significant factors associated with screening included having more than one lifetime sexual partner (AOR = 2.96, 95% CI: 1.48–4.92), a history of gynecological examinations (AOR = 2.59, 95% CI: 1.28–5.23), a fair understanding of cervical cancer (AOR = 2.16, 95% CI: 1.12–4.17), a positive attitude toward screening (AOR = 3.23, 95% CI: 1.65–6.33), and a high perceived susceptibility to cervical cancer (AOR = 2.57, 95% CI: 1.34–4.93). Conclusions: Cervical cancer screening rates among female school teachers in Bahir Dar City fall short of the HSTP‐II target. Independent predictors of screening include having multiple sexual partners, a history of gynecological exams, knowledge of cervical cancer, attitudes toward screening, and perceived susceptibility. To improve these rates, initiatives should focus on raising awareness and fostering positive attitudes among teachers about cervical cancer and health‐seeking behavior. [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]

Human Papillomavirus (HPV) infection is a significant global health concern linked to various cancers, particularly cervical cancer. Timely and accurate detection of HPV is crucial for effective management and prevention strategies. Traditional laboratory-based HPV testing methods often suffer from limitations such as long turnaround times, restricted accessibility, and the need for trained personnel, especially in resource-limited settings. Consequently, there is a growing demand for point-of-care (POC) HPV testing solutions that offer rapid, easy-to-use, and convenient screening at the primary care level. This review provides a comprehensive overview of recent advancements and emerging technologies utilized in POC HPV testing using isothermal amplification methods, in addition to evaluating their diagnostic performance, sensitivity, specificity, and clinical utility compared to conventional laboratory-based assays, particularly in low-resource settings, where access to centralized laboratory facilities is limited. We provide insights into the potential of isothermal nucleic acid amplification to revolutionize cervical cancer screening and prevention efforts worldwide, with emphasis on the need for continued research, innovation, and collaboration to optimize the performance, accessibility, and affordability of POC HPV testing solutions, ultimately contributing to the worldwide effort towards the elimination of this disease. [ABSTRACT FROM AUTHOR]

AbstractEarly cancer detection using T-cell receptor sequencing (TCR-seq) and multiple instances learning methods has shown significant effectiveness. We introduce a multiple instance learning method based on convolutional neural networks and self-attention (MICA). First, MICA preprocesses TCR-seq using word vectors and then extracts features using convolutional neural networks. Second, MICA uses an enhanced self-attention mechanism to extract relational features of instances. Finally, MICA can extract the crucial TCR-seq. After cross-validation, MICA achieves an area under the curve (AUC) of 0.911 and 0.946 on the lung and thyroid cancer datasets, which are 7.1% and 2.1% higher than other methods, respectively. [ABSTRACT FROM AUTHOR]

It is becoming increasingly common for researchers to consider leveraging information from external sources to enhance the analysis of small‐scale studies. While much attention has focused on univariate survival data, correlated survival data are prevalent in epidemiological investigations. In this article, we propose a unified framework to improve the estimation of the marginal accelerated failure time model with correlated survival data by integrating additional information given in the form of covariate effects evaluated in a reduced accelerated failure time model. Such auxiliary information can be summarized by using valid estimating equations and hence can then be combined with the internal linear rank‐estimating equations via the generalized method of moments. We investigate the asymptotic properties of the proposed estimator and show that it is more efficient than the conventional estimator using internal data only. When population heterogeneity exists, we revise the proposed estimation procedure and present a shrinkage estimator to protect against bias and loss of efficiency. Moreover, the proposed estimation procedure can be further refined to accommodate the non‐negligible uncertainty in the auxiliary information, leading to more trustable inference conclusions. Simulation results demonstrate the finite sample performance of the proposed methods, and empirical application on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial substantiates its practical relevance. [ABSTRACT FROM AUTHOR]

Background: There is a strong evidence showing that women who start menstruation early are at a greater risk of developing breast cancer. Recognizing that women will seek breast cancer screening when they have a high perceived risk, we hypothesized that women who experienced early menarche will be more likely to utilize clinical breast examination (CBE). Hence, we aimed to investigate the association between age at first menstruation and women's utilization of CBE in Côte d'Ivoire. Methods: We used data from the 2021 Côte d'Ivoire demographic and health survey. A sample of 14,685 women was used for the analysis. A descriptive analysis, as well as bivariate and multivariate logistic regression models were computed in STATA version 18. Adjusted odds ratio (AOR) and a 95% confidence interval was used to present the result. Results: CBE utilization was 17.4%. Women who had their first menstruation before attaining 15 years were significantly less likely to utilize CBE services [AOR = 0.89; 95% CI 0.81–0.99]. A significantly higher utilization of CBE was found among those with primary [AOR = 1.48, 95% CI 1.29–1.70], secondary [AOR = 2.96, 95% CI 2.59–3.38], and higher education [AOR = 4.35, 95% CI 3.50–5.40] compared to those with no formal education. Increasing likelihood of CBE utilization was observed as age increased. Rural residence was associated with lower odds of CBE utilization (AOR = 0.84, 95% CI 0.74–0.95]. Increasing wealth status was associated with higher odds of CBE utilization with those in the richest households having the highest odds compared to women in the poorest household [AOR = 2.11, 95% CI 1.69–2.64]. Conclusion: Utilization of CBE is low among women of reproductive age in Côte d'Ivoire. We conclude that even though existing literature has established early age at first menstruation as a strong risk factor for breast cancer, CBE utilization is significantly low among those who had early menarche. Going forward, it is necessary for Côte d'Ivoire's health Ministry to intensify breast cancer awareness in the country. Such awareness campaigns must emphasize age at menarche as a risk factor so as to motivate women with a history of early menstruation to utilize CBE. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is the most common diagnosed disease, and the second leading cause of death among women. Thus, due to its importance, the current research is aimed at identifying the preferences of individuals for improving breast cancer screening programs and the related policies. Method: A systematic search was applied on databases including - PubMed, Scopus, the Web of Science, Embase, Cochrane, SID- up to October 2022. The including articles were original or review papers that assessed individuals' willingness to pay. Also, articles including the effective variables or attributes for breast cancer screening program were included. Meta-analysis was applied to calculate Willingness to Pay (WTP) as a mean for breast cancer screening followed by vote-counting for identifying the variables and attributes correlated with screening. Results: A total of 721 articles were identified during the first phase. After the screening process, thirteen papers were chosen, out of which, nine assessed mammography as a breast cancer screening program. The results of random effect meta-analysis on the including studies indicated that the rate of willingness to pay for screening was 0.28% of GDP per capita (95%CI: 0.14–0.43), which was found to be statistically significant. The result of stratified meta- analysis indicated that the rate of willingness to pay for screening was 0.22% of GDP per capita (95%CI: 0.07–0.37), which was found to be statistically significant. Generally, income was the basic factor for receiving screening services, and cost was an effective attribute for participating in screening programs. Conclusions: To increase women's participation in breast cancer screening programs; it is essential to provide legitimate information and eliminate the barriers to women's non-participation. Offering rapid tests at low costs in healthcare centers (both in terms of travel and screening time) delivered by female staff can lead to an increase in women's willingness to participate in breast cancer screening programs. [ABSTRACT FROM AUTHOR]

Although medical mistrust is associated with lower cancer screening uptake among racial minorities, such as African Americans, potential impacts on cancer screening among White Americans are generally understudied. In this study, we examined links from medical mistrust to lung cancer screening among African American (N = 203) and White American (N = 201) smokers. Participants completed the Group-Based Medical Mistrust Scale and viewed a brief online educational module about lung cancer risks, etiology, and screening. Thereafter, participants reported their receptivity to lung cancer screening using a Theory of Planned Behavior (TPB) measurement framework (attitudes, normative beliefs, perceived control, and intentions). Medical mistrust predicted lower screening receptivity across all TPB measures for both racial groups. Although medical mistrust was higher among African Americans, there were no race differences in screening receptivity. However, there was some evidence that race moderates the relationship between medical mistrust and screening attitudes. While greater mistrust predicted more negative attitudes among both races, this effect was stronger among White Americans than African Americans. Findings suggest that group-based medical mistrust is a barrier to lung cancer screening for both African Americans and White Americans and illustrates the need to address medical mistrust as a barrier to screening for both racial minority and nonminority populations. [ABSTRACT FROM AUTHOR]

Alpha-fetoprotein (AFP) is a characteristic marker of liver cancer, and its accurate detection is of great significance for the early detection of liver cancer. This study utilizes visible light as the excitation light source, employs electrochemical measurement, and incorporates the AFP aptamer as the biorecognition element to construct a photoelectrochemical (PEC) biosensor for the highly sensitive detection of AFP. A Z-scheme heterostructure is constructed via a two-step hydrothermal method, and CdS nanoparticles are anchored on TiO2NRA and BiOI nanosheets with high specific surface areas. The photocurrent response of the BiOI/CdS/TiO2NRA composite material is 4.5 times that of TiO2NRA. By analyzing the energy band positions, the electron transfer mechanisms of Z-scheme heterojunctions in CdS/TiO2NRA and BiOI/CdS photoactive materials were elucidated. Subsequently, the AFP aptamer was immobilized on the base electrode surface via the C＝N structure to fabricate a self-supporting sensing platform. The PEC biosensor demonstrates excellent detection performance within the linear range of 1 nM ∼ 250 nM, exhibiting high selectivity and the detection limit is 0.59 nM (S/N = 3). It has been effectively utilized for the detection of AFP in blood, providing a practical method for early detection and prevention of liver cancer, and also offering new insights for the design of other similar characteristic marker biosensors. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer screening remains pivotal in early detection and intervention. However, disparities persist, particularly among women with disabilities, necessitating a comprehensive understanding of their screening practices. This study aims to investigate breast cancer screening behaviours in Saudi women with disabilities. Methods: A cross-sectional study conducted in Saudi Arabia surveyed 307 women with disabilities, evaluating their screening frequency, knowledge of mammography, disability types, and duration. The Statistical Package for the Social Sciences (SPSS) was employed for data analysis. Results: The study found that 70.4% of participants had irregular breast cancer screenings, and 92.5% lacked tailored information on breast cancer screening. The primary sources of information were support groups (59.3%) and healthcare professionals (25.4%). Significant associations were observed between education levels and awareness of the importance of mammography and the increased risk of breast cancer in individuals with disabilities. Notably, participants with higher education levels demonstrated greater awareness. Conclusion: The findings highlight substantial gaps in breast cancer screening practices and knowledge among Saudi women with disabilities. There is a critical need for tailored educational programs, accessible information, and targeted awareness campaigns to address these disparities. Enhancing the accessibility of screening services and information for this demographic is essential for improving healthcare equity and outcomes. [ABSTRACT FROM AUTHOR]

Cervical cancer is a significant public health issue in Mexico and many developing countries. Early detection is crucial for combating this disease. The official screening test for cervical cancer is cytology, but this technique faces several barriers, including methodological, educational, and sociocultural challenges. Liquid-based cytology is an improved version of this test, however it does not address the aforementioned complications. Biomarkers for cervical precursor lesions and cervical cancer can improve timely detection of the disease. A previous study from our group identified four circulating human proteins as potential biomarkers for these conditions. For molecular screening, we selected GAPDH as the biomarker for cervical precursor lesions and HNRNPA1 as the biomarker for cervical cancer -chosen from the three previously identified options based on antibody availability- to be detected in sera. Participants underwent a comprehensive panel of tests, including liquid-based cytology, PCR detection of Human papillomavirus (HPV), colposcopy, and histopathology -when applicable-. The last two tests were used as references for determining sensitivity and specificity, with histopathology being the gold standard for cervical cancer diagnosis. All the participants successfully received colposcopies (n = 99) and only those women with visible or suspected cervical lesions/malignancies were biopsied (n = 62). A subset of randomly selected biopsies underwent p16INK4a immunohistochemistry (n = 36). This study compares the performance of liquid-based cytology with the molecular screening. With colposcopy as reference, liquid-based cytology showed 30% sensitivity and 96% specificity, while the molecular screening showed 90% sensitivity and 43% specificity. With histopathology as reference, liquid-based cytology showed 21% sensitivity and 93% specificity, while the molecular screening showed 85% sensitivity and 61% specificity. The molecular screening outperformed the liquid-based cytology in several areas, including detecting true-positive cases, reducing false-negative cases by 34.62%, application time, simplicity of result´s categories, and acceptance among participants. An ideal screening test requires high sensitivity, maintains moderate specificity, and minimizes false negatives. Our proposed screening test meets these criteria, making it an ideal complement -or alternative- for cervical cancer screening. [ABSTRACT FROM AUTHOR]

The human lungs, crucial for supplying oxygen, are vulnerable to diseases such as lung cancer, a leading cause of mortality. Timely prediction of lung cancer is essential to enable early intervention by healthcare professionals, enhancing patient outcomes and saving lives. This study introduces a comprehensive Machine Learning (ML) model designed to predict lung cancer at an early stage, utilizing a dataset sourced from Kaggle. Built on the Random Forest algorithm, the model assesses a diverse set of characteristics and variables, including gender, age, and exposure to various environments and lifestyles. It accurately identifies individuals at a higher risk of developing early-stage lung cancer, facilitating prompt intervention and personalized treatment strategies. Key evaluation metrics demonstrating the model's effectiveness include precision, F1 score, recall, and accuracy. The findings indicate a model accuracy of approximately 97.9%, underscoring its potential as a valuable tool for enhancing the early detection of lung cancer. [ABSTRACT FROM AUTHOR]

Skin lesion datasets provide essential information for understanding various skin conditions and developing effective diagnostic tools. They aid the artificial intelligence-based early detection of skin cancer, facilitate treatment planning, and contribute to medical education and research. Published large datasets have partially coverage the subclassifications of the skin lesions. This limitation highlights the need for more expansive and varied datasets to reduce false predictions and help improve the failure analysis for skin lesions. This study presents a diverse dataset comprising 12,345 dermatoscopic images with 40 subclasses of skin lesions, collected in Turkiye, which comprises different skin types in the transition zone between Europe and Asia. Each subgroup contains high-resolution images and expert annotations, providing a strong and reliable basis for future research. The detailed analysis of each subgroup provided in this study facilitates targeted research endeavors and enhances the depth of understanding regarding the skin lesions. This dataset distinguishes itself through a diverse structure with its 5 super classes, 15 main classes, 40 subclasses and 12,345 high-resolution dermatoscopic images. [ABSTRACT FROM AUTHOR]

Background: Our previous small-sample study indicated that serum levels of interleukin enhancer binding factor 2 (ILF2) may have the potential for gastric cancer (GC) detection. The present study was conducted to further validate the diagnostic value of serum ILF2 protein for GC. Methods: Serum specimens and clinical data were collected from patients with GC (n = 99) or benign gastric disease (BGD) (n = 49) and healthy controls (HC) (n = 51). Serum ILF2 levels were measured using enzyme-linked immunosorbent assay. The diagnostic performance of ILF2 was evaluated using the area under the receiver operating characteristic curve (AUC). The independence and synergy of ILF2 in GC diagnosis were analyzed by modeling with conventional blood indicators. Results: The median serum ILF2 level was higher in the GC group (227.8ng/mL) than in the BGD group (72.0ng/mL) and the HC group (56.8ng/mL) (p < 0.001), and no significant difference across GC subgroups. The AUCs of ILF2 were 0.915 (95%CI 0.873–0.957) for GC vs. HC, 0.854 (95%CI 0.793–0.915) for GC vs. BGD, 0.885 (95%CI 0.841–0.929) for GC vs. BGD + HC, and 0.888 (95% CI 0.830–0.945) for TNM I stage GC vs. BGD + HC, outperforming conventional blood indicators (corresponding AUCs ranging from 0.641 to 0.782). ILF2 was independent of and synergistic with conventional blood indicators in GC diagnosis, and a simple diagnostic model based on ILF2 and red blood cell count improved the diagnostic performance, with positive rates of approximately 90% in various subgroups of GC. Conclusions: Serum ILF2 protein is a novel and potential serum biomarker for the detection of GC, especially for early GC. [ABSTRACT FROM AUTHOR]

ABSTRACT Objectives Methods Results Conclusion Introduce a new intrapapillary capillary loop (IPCL) classification using narrow‐band imaging (NBI) to diagnose early malignant transformation in oral potentially malignant disorders (OPMDs).In the exploratory phase, NBI images from 241 patients with OPMDs and oral squamous cell carcinoma (OSCC) were assessed for three basic IPCL features—dilation, unevenness, and tortuosity—to identify patterns associated with OPMDs malignant transformation. Chi‐square analysis and logistic regression differentiated these patterns between low‐risk (no/mild dysplasia) and high‐risk (moderate/severe dysplasia, invasive carcinoma) lesions, establishing a new classification system. In the validation phase, the system was applied to 90 patients with histopathological results confirming diagnostic accuracy.Four distinct IPCL types were identified: normal, dilated, irregular, and chaotic, with increasing odds ratios for high‐risk lesions. The ROC curve had an AUC of 0.913, with irregular and chaotic types providing the best diagnostic performance, achieving sensitivities and specificities of 84.2% and 88.4%, respectively. In the validation phase, the new classification achieved an overall diagnostic accuracy of 84.4% and substantial inter‐observer consistency (κ = 0.694).The new IPCL classification effectively identifies high‐risk oral lesions, enhancing the diagnostic utility of NBI for early detection of OPMDs malignant transformations. [ABSTRACT FROM AUTHOR]

Background: Breast cancer poses a significant public health challenge in Tanzania. Limited knowledge about breast cancer and negative attitudes toward screening practices contributes to delayed diagnoses and poorer patient outcomes. Catholic nuns, who are often nulliparous, represent a population with an increased risk of developing breast cancer. Despite this risk, they remain an understudied group regarding breast cancer awareness and screening practices. Objective: This study aimed to assess breast cancer knowledge and attitudes toward screening practices among Catholic nuns residing in Tanzania's Lake Zone. Study design: The study was a cross-sectional design. Methods: A total of 385 Catholic nuns participated in the study. To ensure a representative sample, nuns were chosen through simple random sampling, giving each Catholic nun an equal probability of being selected. Data were collected using a self-administered questionnaire and then analyzed using STATA version 18.0. Both descriptive and inferential statistics were used to draw conclusions. In inferential statistics, logistic regression was used to test for associations between categorical variables. The test statistics were considered significant when the P -value was less than.05 at a 95% confidence interval (CI). Results: This study enrolled 385 catholic nuns. 57.1% (95% CI, 52.0%-62.1%) of all surveyed catholic nuns had inadequate knowledge of breast cancer. Misconceptions also emerged as significant risk factors for inadequate knowledge. Thus, lack of awareness of breast cancer risk factors increased the odds by 5.57 times (adjusted odds ratio [AOR]: 5.57; 95% CI: 2.84-10.92; P <.001). In addition, believing cancer was not inheritable (AOR: 2.65; 95% CI: 1.14-6.15; P =.024), misperceiving oneself as being in a low-risk group (AOR: 1.65; 95% CI: 1.03-2.66; P =.039), and underestimating the vulnerable age group (believing it is not above 40 years) (AOR: 2.60; 95% CI: 1.49-4.51; P =.001) were all significantly associated with higher odds of inadequate knowledge. Regarding the attitude toward breast cancer screening practices, 62.3% (95% CI, 57.3%-67.2%) of the catholic nuns had negative attitudes. Conclusion: These findings highlight the need for breast health intervention educational programs to improve breast cancer awareness among Catholic nuns. Such programs should address risk factors, symptoms, screening methods, and treatment options, dispelling misconceptions. By empowering nuns with knowledge, they can make informed decisions about their health and take charge of their well-being. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion This study aimed to assess the value of a HPV E6/E7 mRNA assay (Aptima® HPV [AHPV]) for primary cervical cancer screening combined with menopausal status.A total of 16 917 women underwent AHPV testing and had complete histopathological results at the Affiliated Hospital of Jining Medical University China between January 1, 2017 and March 31, 2022. We evaluated the performance of different screening strategies and combined strategies, as well as evaluations of different menopausal states.When identifying LSIL+ (includes low‐ and high‐grade squamous intraepithelial lesions and invasive cervical cancer [ICC]), the sensitivity (91.2%) and negative predictive value (NPV; 96.6%) were significantly higher for AHPV than for liquid‐based cytology assay (LBC; 33.2% and 84.7% for sensitivity and NPV, respectively). Furthermore, the co‐testing strategy (cytology combined with AHPV), when compared with AHPV, achieved a slightly higher sensitivity (93.6% vs. 91.2%, respectively, P < 0.001), a similar specificity (61.3% vs. 62.7%, respectively, P = 0.014), a similar positive predictive value (PPV; 37.5% vs. 37.8%, respectively, P = 0.709) and a similar NPV (97.5% vs. 96.6%, respectively, P = 0.001). Moreover, AHPV (when compared with menopausal women) achieved a higher sensitivity (93.5% vs. 77.7%, respectively, P < 0.001), a higher NPV (97.3% vs. 93.9%, respectively, P < 0.001), a similar PPV (37.8% vs. 37.0%, respectively, P = 0.618) and a slightly lower specificity (60.7% vs. 72.1%, respectively, P < 0.001) in premenopausal women. These results were similar when identifying HSIL+ (includes high‐grade squamous intraepithelial lesion and ICC).The present study suggests that initial screening with HPV E6/E7 mRNA testing rather than combined screening is a suitable candidate for cervical cancer screening in China (especially for premenopausal women) based on economic reasons. [ABSTRACT FROM AUTHOR]

Retinoblastoma is the most common intraocular malignant tumor that poses a serious threat to the lives of children. Treatment plans for RB vary depending on the stage of the disease, and there is an uneven distribution of medical resources across different regions. To standardize the clinical diagnosis and treatment of RB, experts have conducted extensive discussions and proposed guidelines for RB diagnosis and treatment. These guidelines aim to improve the clinical management of RB and serve as a reference for clinicians in their practice. [ABSTRACT FROM AUTHOR]

Molecular alterations in cancerous tissues exhibit intercellular genetic and epigenetic heterogeneity, complicating the performance of diagnostic assays, particularly for early cancer detection. Conventional liquid biopsy methods have limited sensitivity and/or ability to assess epigenetic heterogeneity of rare epiallelic variants cost-effectively. We report an approach, named REM-DREAMing (Ratiometric-Encoded Multiplex Discrimination of Rare EpiAlleles by Melt), which leverages a digital microfluidic platform that incorporates a ratiometric fluorescence multiplex detection scheme and precise digital high-resolution melt analysis to enable low-cost, parallelized analysis of heterogeneous methylation patterns on a molecule-by-molecule basis for the detection of cancer in liquid biopsies. We applied the platform to simultaneously assess intermolecular epigenetic heterogeneity in five methylation biomarkers for improved, blood-based screening for early-stage non-small cell lung cancer. In a cohort of 48 low-volume liquid biopsy specimens from patients with indeterminant pulmonary nodules, we show that assessment of intermolecular methylation density distributions can notably improve the performance of multigene methylation biomarker panels for the early detection of cancer. [ABSTRACT FROM AUTHOR]

This study introduces a novel approach for the early detection of pancreatic cancer through biofluid spectroscopy, leveraging a unique machine learning pipeline comprising class-specific principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) in both real patient and synthetic data. By conducting separate PCA on cancerous and non-cancerous samples and integrating the projections prior to LDA and SVM classification, we demonstrate significantly improved diagnostic accuracy compared to traditional methods. This methodology not only enhances predictive performance but also offers deeper insights into the influence of molecular spectra on model efficacy. Our findings, validated on real patient data, suggest a promising avenue for developing non-invasive, accurate diagnostic tools for early-stage pancreatic cancer detection. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer, though one of the most common cancers affecting women globally, holds immense potential for prevention through screening. Therefore, we conducted this meta-analysis to assess the rate of cervical cancer screening in Arab countries and identify barriers among those who did not participate. Methods: A comprehensive search was conducted from January 1st to June 1st,2024, including all observational studies that reported cervical cancer screening uptake in any Arab country. A meta-analysis was performed using a random-effects model to estimate the pooled prevalence, and sensitivity analyses were conducted to test the robustness of the findings. The study followed PRISMA guidelines. Results: This meta-analysis, covering 55 studies and 204,940 Arab women, found an overall cervical cancer screening uptake rate of 18.2% (95% CI: 13.9–23.6), with sensitivity analysis confirming the reliability of this estimate. Country disparities were evident, with Bahrain having the highest uptake at 44.1%, while Somalia had the lowest at 8.9%. Among women who underwent screening, the majority were ever-married (94.7%) and held positive attitudes towards screening (91.0%). Barriers to screening were common among women who did not participate, with the most frequent reasons being a lack of information (25.1%), the misconception of feeling healthy (24.5%), fear of the procedure (19.3%), and feelings of embarrassment (13.2%). Additionally, women who were screened had lower perceived barrier scores (SMD = -0.466) and higher perceived benefits scores (SMD = 0.379) than those who were not (p < 0.05). Conclusion: This meta-analysis reveals a low overall cervical cancer screening uptake (18.2%) among Arab women. Key barriers such as lack of information, fear, the misconception of feeling healthy, and embarrassment hinder uptake. This alarmingly low rate underscores the urgent need for targeted interventions to address these barriers and promote awareness of early detection's life-saving potential. [ABSTRACT FROM AUTHOR]

Background Aims Materials and methods Results Conclusions Human papillomavirus (HPV) testing became the primary cervical screening modality in Aotearoa New Zealand in September 2023. To inform the national roll‐out of HPV primary screening, a multiregion implementation study (‘Let's Test for HPV’) was undertaken in primary care in 2022–2023.To explore participant perspectives and information needs following receipt of an HPV detected result.An online survey completed in 2023 by 921 Let's Test for HPV participants included 95 people with an HPV detected result (10.3%). Data collection included: adequacy of information provision, receipt of results, emotional response, views on HPV primary screening and needing cervical cytology and/or colposcopy, and intent to screen again.Receipt of an HPV detected result was worrying for most people and many had unanswered questions about HPV, how HPV testing fits with cytology, implications for follow‐up and outcomes. Knowledge gaps and uncertainty appeared to be linked with feeling anxious about possible outcomes and fear of a cancer diagnosis. All survey participants received recommended follow‐up (cytology and/or colposcopy). Having a choice of screening test and the ability to self‐test were welcomed and the majority (88%) expressed intent to screen again.These survey findings highlight the importance of giving clear information about potential outcomes at the time of screening and again when sharing results. Sensitive delivery of results, providing reassurance and answering questions are also important to mitigate fear and worry. Population‐level education would help improve understanding of key messages about HPV testing and the changes to cervical screening. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion Mammographic imaging is essential for breast cancer detection and diagnosis. In addition to masses, calcifications are of concern and the early detection of breast cancer also heavily relies on the correct interpretation of suspicious microcalcification clusters. Even with advances in imaging and the introduction of novel techniques such as digital breast tomosynthesis and contrast‐enhanced mammography, a correct interpretation can still be challenging given the subtle nature and large variety of calcifications.Computer simulated lesion models can serve to develop, optimize, or improve imaging techniques. In addition to their use in comparative (virtual clinical trial) detection experiments, these models have potential application in training deep learning models and in the understanding and interpretation of breast lesions. Existing simulation methods, however, often lack the capacity to model the diversity occurring in breast lesions or to generate models relevant for a specific case. This study focuses on clusters of microcalcifications and introduces an automated, flexible toolbox designed to generate microcalcification cluster models customized to specific tasks.The toolbox allows users to control a large number of simulation parameters related to model characteristics such as lesion size, calcification shape, or number of microcalcifications per cluster. This leads to the capability of creating models that range from regular to complex clusters. Based on the input parameters, which are either tuned manually or pre‐set for a specific clinical type, different sets of models can be simulated depending on the use case. Two lesion generation methods are described. The first method generates three‐dimensional microcalcification clusters models based on geometrical shapes and transformations. The second method creates two‐dimensional (2D) microcalcification cluster models for a specific 2D mammographic image. This novel method employs radiomics analysis to account for local textures, ensuring the simulated microcalcification cluster is appropriately integrated within the existing breast tissue. The toolbox is implemented in the Python language and can be conveniently run through a Jupyter Notebook interface, openly accessible at https://gitlab.kuleuven.be/medphysqa/deploy/breast‐calcifications. Validation studies performed by radiologists assessed the level of malignancy and realism of clusters tuned with specific parameters and inserted in mammographic images.The flexibility of the toolbox with multiple simulation methods is illustrated, as well as the compatibility with different simulation frameworks and image types. The automation allows for the straightforward and fast generation of diverse microcalcification cluster models. The generated models are most likely applicable for various tasks as they can be configured in a variety of ways and inserted in different types of mammographic images of multiple acquisition systems. Validation studies confirmed the capacity to simulate realistic clusters and capture clinical properties when tuned with appropriate parameter settings.This simulation toolbox offers a flexible means of simulating microcalcification cluster models with potential use in both technical and clinical research in mammography imaging. The 3D generation methods allow for specifying many characteristics regarding the calcification shape and cluster architecture, and the 2D generation method presents a novel manner to create microcalcification clusters tailored to existing breast textures. [ABSTRACT FROM AUTHOR]

Background: Most epithelial ovarian cancer (EOC) eventually develops recurrence. Identification of high-risk patients can prompt earlier intervention and improve long-term outcomes. We used laboratory and clinical data to create models based on machine learning for EOC platinum resistance recurrence identification. Methods: This study was designed as a retrospective cohort analysis. Initially, we identified 1,392 patients diagnosed with epithelial ovarian cancer who underwent platinum-based chemotherapy at Yunnan Cancer Hospital between January 1, 2012, and June 30, 2022. We collected data on the patients' clinicopathologic characteristics, routine laboratory results, surgical information, details of chemotherapy regimens, and survival outcomes. Subsequently, to identify relevant variables influencing the recurrence of platinum resistance, we screened thirty potential factors using two distinct variable selection methods: Lasso regression and multiple logistic regression analysis. Following this screening process, five machine learning algorithms were employed to develop predictive models based on the selected variables. These included decision tree analysis (DTA), K-Nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost). The performance of these models was compared against that of traditional logistic regression. To ensure robust internal validation and facilitate comparison among model performance metrics, a five-fold cross-validation method was implemented. Key performance indicators for the models included the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and average accuracy. Finally, we will visualize these models through nomograms, decision tree diagrams, variable importance plots, etc., to assist clinicians in their practice. Results: Multiple logistic regression analysis identified eight variables associated with platinum resistance recurrence. In the lasso regression, seven variables were selected. Based on the findings from both Lasso regression and multiple logistic regression analysis, models were developed using these 7 and 8 factors. Among these, the XGBoost model derived from multiple logistic regression exhibited superior performance and demonstrated good discrimination during internal validation, achieving an AUC of 0.784, a sensitivity of 0.735, a specificity of 0.713, an average accuracy of 80.4%, with a cut-off value set at 0.240. Conversely, the LR model based on lasso regression yielded commendable results as well; it achieved an AUC of 0.738, a sensitivity of 0.541, a specificity of 0.836, with a cut-off value established at 0.154 and an accuracy rate of 79.6%. Finally, we visualized both models through nomograms to illustrate the significance of each variable involved in their development. Conclusions: We have successfully developed predictive models for platinum-resistant recurrence of epithelial ovarian cancer, utilizing routine clinical and laboratory data. Among these models, the XGBoost model—derived from variables selected through multiple logistic regression—demonstrated the best performance. It exhibited high AUC values and average accuracy during internal validation, making it a recommended tool for clinical use. However, due to variations in time and context, influencing factors may change over time; thus, continuous evolution of the model is necessary. We propose a framework for this ongoing model adaptation. [ABSTRACT FROM AUTHOR]

Objectives: We study the introduction of a web portal allowing women to manage their appointments in the public cervical cancer screening program of an Italian Local Health Unit (LHU). We investigate how this intervention affects program participation and the ability of the LHU to collect information on women's screening outside the program. Methods: We use administrative data from the LHU and a difference-in-differences design to compare outcomes of women in the treated group (n=870), who could reschedule appointments either through phone calls or through the portal, and the control group (n=768), who could only reschedule by phone. Results: The portal reduces the number of appointments rescheduled by phone (b = −0.176, 95% CI = −0.228 to −0.114, p < 0.001). It also makes women more likely to report both screenings outside the program (b = 0.049, 95% CI = 0.006–0.912, p < 0.05) and the various reasons for canceling their appointments. However, the portal also decreases the probability of screening (b = −0.156, 95% CI = −0.216–0.096, p < 0.001). Two-thirds of this reduction is due to increased unjustified no-shows (b = 0.099, 95% CI = 0.043–0.155, p < 0.001). Conclusions: Digital innovations increase efficiency of screening programs. However, they may discourage screening, by triggering behavioral biases such as limited attention and procrastination. Pairing portals with frequent reminders may help to boost participation. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma remains a health challenge for humanity. Therefore, there is an urgent need to develop novel biomarkers with high efficiency yet fast ability to meet the requirements of hepatocellular carcinoma treatment. Methods: A total of 229 patients with HBV-associated hepatocellular carcinoma (HCC), 298 patients with chronic hepatitis B (CHB), and 96 healthy controls were retrospectively analyzed. Methylation levels of the Mex3a promoter in peripheral blood mononuclear cells (PBMCs) were measured using MethyLight to obtain clinical and laboratory parameters. Results: The Mex3a promoter methylation level in HCC patients (median: 0.289% and interquartile range: 0.126%–0.590%) was significantly lower than that in CHB patients (median: 0.999%, interquartile range: 0.417%–1.268%, and p < 0.001) and healthy people (median: 2.172%, interquartile range: 1.225%–3.098%, and p < 0.001). The Mex3a mRNA levels in HCC patients (median: 12.198 and interquartile range: 3.112–18.996) were significantly higher than those in CHB patients (median: 1.623 and interquartile range: 0.066–6.000, and p < 0.001) and healthy controls (median: 0.329, interquartile range: 0.031–1.547, and p < 0.001). MethyLight data were expressed as a percentage of the methylated reference (PMR) value. The Mex3a PMR value was negatively correlated with the mRNA expression level (Spearman's R = −0.829 and p < 0.001). The Mex3a PMR value of HCC patients was significantly correlated with age (Spearman's R = 0.113 and p = 0.044), and the mRNA level was significantly correlated with ALT (Spearman's R = 0.132 and p = 0.046). The Mex3a promoter methylation levels and mRNA levels were also independent factors in the development of liver cancer. The Mex3a promoter methylation and mRNA levels were better at distinguishing HCC from CHB than AFP [area under the receiver operating characteristic curve (AUC) for predicting HCC vs. CHB: 0.915 vs. 0.715: p < 0.001]. The combined use of AFP and Mex3a methylation levels and mRNA levels further improved the area under the receiver operating characteristic curve. Conclusion: The presence of Mex3a promoter hypomethylation in hepatocellular carcinoma can be used as a non-invasive biomarker for the early detection of liver cancer. [ABSTRACT FROM AUTHOR]

Background: To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED). Methods: In the OPTIMACT trial, 870 patients with suspected nontraumatic pulmonary disease underwent ULDCT. The ED radiologist prospectively read the examinations and reported incidental pulmonary nodules requiring follow-up. All ULDCTs were processed post hoc using an AI deep learning software marking pulmonary nodules ≥ 6 mm. Three chest radiologists independently reviewed the subset of ULDCTs with either prospectively detected incidental nodules in 35/870 patients or AI marks in 458/870 patients; findings scored as nodules by at least two chest radiologists were used as true positive reference standard. Proportions of true and false positives were compared. Results: During the OPTIMACT study, 59 incidental pulmonary nodules requiring follow-up were prospectively reported. In the current analysis, 18/59 (30.5%) nodules were scored as true positive while 104/1,862 (5.6%) AI marks in 84/870 patients (9.7%) were scored as true positive. Overall, 5.8 times more (104 versus 18) true positive pulmonary nodules were detected with the use of AI, at the expense of 42.9 times more (1,758 versus 41) false positives. There was a median number of 1 (IQR: 0–2) AI mark per ULDCT. Conclusion: The use of AI on ULDCT in patients suspected of pulmonary disease in an emergency setting results in the detection of many more incidental pulmonary nodules requiring follow-up (5.8×) with a high trade-off in terms of false positives (42.9×). Relevance statement: AI aids in the detection of incidental pulmonary nodules that require follow-up at chest-CT, aiding early pulmonary cancer detection but also results in an increase of false positive results that are mainly clustered in patients with major abnormalities. Trial registration: The OPTIMACT trial was registered on 6 December 2016 in the National Trial Register (number NTR6163) (onderzoekmetmensen.nl). Key Points: An AI deep learning algorithm was tested on 870 ULDCT examinations acquired in the ED. AI detected 5.8 times more pulmonary nodules requiring follow-up (true positives). AI resulted in the detection of 42.9 times more false positive results, clustered in patients with major abnormalities. AI in the ED setting may aid in early pulmonary cancer detection with a high trade-off in terms of false positives. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common malignancy in women globally, stemming from gene mutations that prompt irregular cellular growth and subsequent tumor development. Early-stage detection of cancer cells results in a remarkable 99% survival rate. This research presents a microwave imaging technique for the non-invasive identification of tumors in the initial stages within the women's breast. A low-cost antenna array with an Artificial Magnetic Conductor (AMC) is proposed, featuring a compact structure size of 37.2 × 37.2 mm 2 . The AMC, a metamaterial, acts as a reflective surface to enhance frequency selectivity, specifically at 8.48 GHz. The maximum gains reached 9.35 dBi in simulated results and 10.51 dBi in measured results. The fabricated antenna validates the simulated findings, and its operational efficiency has undergone experimental validation. Moreover, fidelity factors in face-to-face (FtF) and side-by-side (SbS) scenarios are delineated. The antenna, operating as a transceiver, is applied to a modeled breast phantom across five distinct cases for numerical simulations pertaining to cancer cell detection applications. The outcomes of this research bear considerable implications for advancing early-stage breast cancer detection methodologies. [ABSTRACT FROM AUTHOR]

Extensive evidence underscores the pivotal role of socioeconomic status (SES) in shaping cancer-related outcomes. However, synthesizing definitive and actionable insights from the expansive body of literature remains a significant challenge. To elucidate the associations between SES, cancer outcomes, and the overall cancer burden, we conducted a comprehensive burden estimation coupled with an umbrella review of relevant meta-analyses. Our findings reveal that robust or highly suggestive meta-analytic evidence supports only a limited number of these associations. Individuals with lower SES, compared to those with higher SES, are disproportionately disadvantaged by reduced access to immunotherapy, KRAS testing for colorectal cancer, targeted cancer therapies, and precision treatments for melanoma. Additionally, they exhibit lower rates of breast cancer screening and higher incidence rates of lung cancer. Furthermore, countries with a higher Human Development Index demonstrate a substantially greater burden related cancer incidence, with this disparity being more pronounced among men than women. Socioeconomic status has been previously linked to cancer outcomes. Here, the authors use an umbrella review to identify differences in access to immunotherapy and cancer screening. [ABSTRACT FROM AUTHOR]

Purpose: To assess the diagnostic performance of prostate MRI by estimating the proportion of clinically significant prostate cancer (csPCa) in patients without prostate pathology. Materials and methods: This three-center retrospective study included prostate MRI examinations performed for clinical suspicion of csPCa (Grade group ≥ 2) between 2018 and 2022. Examinations were divided into two groups: pathological diagnosis within 1 year after the MRI (post-MRI pathology) is present and absent. Risk prediction models were developed using the extracted eleven common predictive variables from the patients with post-MRI pathology. Then, the csPCa proportion in the patients without post-MRI pathology was estimated by applying the model. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values (PPV/NPV) of prostate MRI in diagnosing csPCa were subsequently calculated for patients with and without post-MRI prostate pathology (estimated statistics) with a positive threshold of PI-RADS ≥ 3. Results: Of 12,191 examinations enrolled (mean age, 65.7 years ± 8.4 [standard deviation]), PI-RADS 1–2 was most frequently assigned (55.4%) with the lowest pathological confirmation rate of 14.0–18.2%. Post-MRI prostate pathology was found in 5670 (46.5%) examinations. The estimated csPCa proportions across facilities were 12.6–15.3%, 18.4–31.4%, 45.7–69.9%, and 75.4–88.3% in PI-RADS scores of 1–2, 3, 4, and 5, respectively. The estimated (observed) performance statistics were as follows: AUC, 0.78–0.81 (0.76–0.79); sensitivity, 76.6–77.3%; specificity, 67.5–78.6%; PPV, 49.8–66.6% (52.0–67.7%); and NPV, 84.4–87.2% (82.4–86.6%). Conclusion: We proposed a method to estimate the probabilities harboring csPCa for patients who underwent prostate MRI examinations, which allows us to understand the PI-RADS diagnostic performance with several metrics. Clinical relevance statement: The reported estimated performance metrics are expected to aid in understanding the true diagnostic value of PI-RADS in the entire prostate MRI population performed with clinical suspicion of prostate cancer. Key Points: Calculating performance metrics only from patients who underwent prostate biopsy may be biased due to biopsy selection criteria, especially in PI-RADS 1–2. The estimated area under the receiver operating characteristic curve of PI-RADS in the entire prostate MRI population ranged from 0.78 to 0.81 at three facilities. The estimated statistics are expected to help us understand the true PI-RADS performance and serve as a reference for future studies. [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]