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Breast cancer is a prevalent and highly lethal cancer affecting women globally. While non-invasive techniques like ultrasound and mammogram are used for diagnosis, histological examination after biopsy is considered the gold standard. However, manual examination of tissues for abnormality is labor-intensive, expensive, and requires prior domain knowledge. Early detection, awareness, and access to specialized medical infrastructure in resource-constrained and remote areas are significant challenges but crucial for saving lives. In recent years, deep learning-based approaches have shown promising results in breast cancer detection, facilitated by advancements in GPU memory, computation power, and the availability of digital data. Motivated by these observations, we propose the Breast-NET deep convolutional neural network model for breast cancer detection and grading using histological images. Our model's performance is evaluated on the BreakHis dataset, and we demonstrate its generalization ability on the Invasive Ductal Carcinoma (IDC) grading and IDC datasets. Extensive experimental and statistical performance analysis, along with an ablation study, validates the efficiency of our proposed model. Furthermore, we demonstrate the effectiveness of transfer learning with seven pre-trained convolutional neural networks for breast cancer detection and grading. Experimental results show that our framework outperforms state-of-the-art approaches in terms of accuracy, space, and computational complexity for the BreakHis, IDC grading, and IDC datasets. [ABSTRACT FROM AUTHOR]

There is a growing interest in using fecal mRNA transcripts as biomarkers for non-invasive detection of colorectal cancer (CRC). The following study compares different RNA extraction and reverse transcription PCR (RT-PCR) methods for mRNA detection in stool and identifies a robust and sensitive protocol. A combination of the Stool total RNA purification kit (Norgen) and the Superscript III one-step RT-PCR kit (Invitrogen) provided high RNA purity and sensitive and consistent mRNA detection, making them well-suited candidates for large-scale studies. We tested the protocol by detecting the mRNA of several immune genes (CXCL1, IL8, IL1B, IL6, PTGS2, and SPP1) in 22 CRCs, 24 adenomatous polyps, and 22 control stool samples. All these inflammatory markers, except for CXCL1, showed a strong association with CRC. Cancer stool samples showed increased levels of IL1B, IL8, and PTGS2 transcripts compared to polyp and control groups. Thus, this work supports the potential use of fecal mRNA as biomarkers for CRC detection. [ABSTRACT FROM AUTHOR]

The incidence of Early-Onset Colorectal Cancer (EOCRC) is increasing. However, the prognosis of EOCRC compared to Late-Onset Colorectal Cancer (LOCRC), and the ideal age for initial colorectal cancer (CRC) screening are not clear. In this study, we identified the pathological differences between the groups and determined the optimal screening age for CRC patients. We included 10,172 patients diagnosed with CRC from January 2011 to December 2021 in this study. Survival differences were compared by plotting Kaplan–Meier survival curves and conducting landmark analysis. Additionally, the diagnostic age of CRC patients was analyzed using age cumulative curves. Compared to LOCRC patients, EOCRC patients had a higher proportion of deficient mismatch repair (dMMR) and more advanced TNM staging (P < 0.05). The five-year survival of EOCRC patients was significantly better than that of LOCRC patients (P < 0.05). Laparoscopic surgery improved the long-term survival of EOCRC patients. Proficient mismatch repair (pMMR) favored the long-term survival of EOCRC patients. The survival rate of EOCRC patients at TNM stages I and II was higher than that of LOCRC patients at the same stages (P < 0.05). The age cumulative curve showed a substantial increase in the number of CRC patients at 40 years. The long-term prognosis of EOCRC patients is better than that of LOCRC patients, especially among those with pMMR, stages I–II, and who undergo laparoscopic surgery. For people with a high risk of cancer, such as a family history of cancer and poor lifestyle habits, the starting age for CRC screening should be 40 years. [ABSTRACT FROM AUTHOR]

Early detection and treatment of skin cancer are important for patient recovery and survival. Dermoscopy images can help clinicians for timely identification of cancer, but manual diagnosis is time-consuming, costly, and prone to human error. To conduct this, an innovative deep learning-based approach has been proposed for automatic melanoma detection. The proposed method involves preprocessing dermoscopy images to remove artifacts, enhance contrast, and cancel noise, followed by feeding them into an optimized Convolutional Neural Network (CNN). The CNN is trained using an innovative metaheuristic called the Improved Chameleon Swarm Algorithm (CSA) to optimize its performance. The approach has been validated using the SIIM-ISIC Melanoma dataset and the results have been confirmed through rigorous evaluation metrics. Simulation results demonstrate the efficacy of the proposed method in accurately diagnosing melanoma from dermoscopy images by highlighting its potential as a valuable tool for clinicians in early cancer detection. [ABSTRACT FROM AUTHOR]

The role of collagen and its receptor, discoidin domain receptor 1 (DDR1) in immune response of colorectal cancer (CRC) remains unclear. We identified DDR1 as a promising target of immunotherapy resistance using a pooled in vivo CRISPR/sgRNA screening in microsatellite stable (MSS) CRC mouse models. Our findings demonstrated that knockdown or inhibition of DDR1 could enhance infiltration of CD8+ T cells and sensitize MSS CRC to PD-1 blockade. Furthermore, DDR1 was found to facilitate kinase domain phosphorylation, upregulate EZH2, consequently elevating H3K27me3 levels at the CXCL10 promotor, which led to the suppression of CXCL10 transcription once bound to collagen in ECM. Lastly, DDR1 was found positively correlated with collagen I expression in MSS CRC specimens. These findings indicated that targeting DDR1 or its inhibitor 7rh might be potential strategy for overcoming immunotherapy resistance in MSS CRC. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer, albeit preventable, is the second-most deadly gynecological cancer in developing nations. Little is known about cervical cancer among Bangladeshi women. This study aims to estimate the prevalence of cervical cancer screening and demographic correlates to identify potential variabilities in screening rates among different demographic groups and regions. Methods: This study used secondary data from the WHO STEPS 2018 Survey. We used Bayesian regression to perform the bivariate analyses between the outcome and each explanatory factor, as it generates more acceptable results and improves parameter estimates. The top-ranked socio-demographic factors were identified using a two-step cluster analysis. This method determines the relevance of predictor variables and automatically establishes the number of clusters. Results: The prevalence of Bangladeshi women who had ever been screened for cervical cancer was 6.2%. In the adjusted model, women with the following socio-demographic factors had a higher likelihood of developing cervical cancer: being 18–29 years old (AOR = 3.3, 95% CI: 0.24, 15.27) or 45–59 years old (AOR = 2.8, 95% CI: 1.22, 6.0), currently married (AOR = 2.3, 95% CI: 1.36, 3.70), and employed (AOR = 2.4, 95% CI: 1.40, 4.06). Women in the Barisal division were found to have higher odds of being screened for cervical cancer (AOR = 21, 95% CI: 0.66, 121.97). Cluster analysis found residence status predisposes women to cervical cancer screening. Conclusion: There is a significant potential for substantial reductions in the burden of cervical cancer in Bangladesh by strengthening the application of cervical cancer screening. Future studies should examine how socioeconomic status, culture, and healthcare access affect cervical cancer screening trends for different divisions in Bangladesh. An independent national cancer registry is urgently needed to evaluate screening trends and outcomes. [ABSTRACT FROM AUTHOR]

Objectives: To assess the impact of differences in Prostate-Specific Antigen (PSA) testing rates on prostate cancer (PCa) diagnosis and PCa-specific mortality among Māori men in a New Zealand (NZ) population. Patients and Methods: Māori men aged 40 years or older, without a history of PCa, with a PSA test between 2006 and 2018 were included. The cohort was divided into two groups; the “screened group” (ScG) consisting of men who had at least one PSA test every four years or less, and the “non-screened group” (non-SG). We measured the rate of cancer diagnoses and used competing risk analysis to assess survival. Results: The study cohort included 63,939 Māori men, with 37,048 (58%) in the ScG. PCa was more frequently diagnosed in the ScG (3.7% vs. 3.0%, P < 0.001). A higher proportion of high-grade cancers were found in the non-SG (32.7% vs. 25.6%, P = 0.001). The 10-year cancer-specific survival was significantly higher in the ScG (99.4% vs. 98.5%, P < 0.001). In a multivariable risk model, PSA testing frequency was an independent predictor of PCa mortality. (HR 2.43, [95% CI 1.97–3.01], P < 0.001). Conclusions: In a cohort of only Māori men, lower PSA testing rates were associated with a higher risk of PCa-related death. Therefore, regular PSA testing for Māori could improve cancer-specific survival among Māori men. Regular PSA testing should be considered a priority area for improving PCa survival in this population. [ABSTRACT FROM AUTHOR]

Background: The Pink Zone Pattern (PP) sign is a typical color alteration of early gastric cancer (EGC) under magnifying endoscopic narrow-band imaging (ME-NBI). By integrating the color changes (PP sign) with the "vessel plus surface (VS)" classification system, we developed an innovative diagnostic system for EGC and named it "Pink Microsurface Microvascular (PSV)" system. Here, we aimed to elucidate the diagnostic performance of the PSV system for EGC. Methods: We conducted a single-center prospective clinical study (before-after design) consisting of 2 cross-sectional studies at 2 separate periods. In the before phase, 184 suspected lesions were evaluated using the VS system under ME-NBI; in the after phase, 183 suspected lesions were evaluated using the PSV system. We compared the diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) between the VS group and the PSV group. Results: The accuracy, sensitivity, specificity, PPV, and NPV of the VS system for EGC were 84.6%, 87.0%, 83.6%, 67.8%, and 94.2%, respectively, and those for the PSV system were 93.0%, 92.0%, 93.4%, 85.2%, and 96.6%, respectively. The accuracy, specificity, and PPV of the PSV system were superior to those of the VS system. However, the sensitivity and NPV did not significantly differ between the VS system and the PSV system. The VS system was inconclusive for 22 lesions (12.0%) and the PSV system was inconclusive for 11 lesions (6.0%). The PSV system could identify more suspicious lesions than the VS system. Conclusions: We propose a new PSV diagnostic system by combining the VS system and the PP sign. Compared with the VS system, the PSV system could identify more suspected lesions and improve the diagnostic performance of EGC. [ABSTRACT FROM AUTHOR]

Different oncologists make their own decisions about the detection and classification of the type of ovarian cancer from histopathological whole slide images. However, it is necessary to have an automated system that is more accurate and standardized for decision-making, which is essential for early detection of ovarian cancer. To help doctors, an automated detection and classification of ovarian cancer system is proposed. This model starts by extracting the main features from the histopathology images based on the ResNet-50 model to detect and classify the cancer. Then, recursive feature elimination based on a decision tree is introduced to remove unnecessary features extracted during the feature extraction process. Adam optimizers were implemented to optimize the network's weights during training data. Finally, the advantages of combining deep learning and fuzzy logic are combined to classify the images of ovarian cancer. The dataset consists of 288 hematoxylin and eosin (H&E) stained whole slides with clinical information from 78 patients. H&E-stained Whole Slide Images (WSIs), including 162 effective and 126 invalid WSIs were obtained from different tissue blocks of post-treatment specimens. Experimental results can diagnose ovarian cancer with a potential accuracy of 98.99%, sensitivity of 99%, specificity of 98.96%, and F1-score of 98.99%. The results show promising results indicating the potential of using fuzzy deep-learning classifiers for predicting ovarian cancer. [ABSTRACT FROM AUTHOR]

MRI has gained prominence in the diagnostic workup of prostate cancer (PCa) patients, with the Prostate Imaging Reporting and Data System (PI-RADS) being widely used for cancer detection. Beyond PI-RADS, other MRI-based scoring tools have emerged to address broader aspects within the PCa domain. However, the multitude of available MRI-based grading systems has led to inconsistencies in their application within clinical workflows. The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) assesses the likelihood of clinically significant radiological changes of PCa during active surveillance, and the Prostate Imaging for Local Recurrence Reporting (PI-RR) scoring system evaluates the risk of local recurrence after whole-gland therapies with curative intent. Underlying any system is the requirement to assess image quality using the Prostate Imaging Quality Scoring System (PI-QUAL). This article offers practicing radiologists a comprehensive overview of currently available scoring systems with clinical evidence supporting their use for managing PCa patients to enhance consistency in interpretation and facilitate effective communication with referring clinicians. Key Points: Assessing image quality is essential for all prostate MRI interpretations and the PI-QUAL score represents the standardized tool for this purpose. Current urological clinical guidelines for prostate cancer diagnosis and localization recommend adhering to the PI-RADS recommendations. The PRECISE and PI-RR scoring systems can be used for assessing radiological changes of prostate cancer during active surveillance and the likelihood of local recurrence after radical treatments respectively. [ABSTRACT FROM AUTHOR]

Multiparametric MRI is the optimal primary investigation when prostate cancer is suspected, and its ability to rule in and rule out clinically significant disease relies on high-quality anatomical and functional images. Avenues for achieving consistent high-quality acquisitions include meticulous patient preparation, scanner setup, optimised pulse sequences, personnel training, and artificial intelligence systems. The impact of these interventions on the final images needs to be quantified. The prostate imaging quality (PI-QUAL) scoring system was the first standardised quantification method that demonstrated the potential for clinical benefit by relating image quality to cancer detection ability by MRI. We present the updated version of PI-QUAL (PI-QUAL v2) which applies to prostate MRI performed with or without intravenous contrast medium using a simplified 3-point scale focused on critical technical and qualitative image parameters. Clinical relevance statement: High image quality is crucial for prostate MRI, and the updated version of the PI-QUAL score (PI-QUAL v2) aims to address the limitations of version 1. It is now applicable to both multiparametric MRI and MRI without intravenous contrast medium. Key Points: High-quality images are essential for prostate cancer diagnosis and management using MRI. PI-QUAL v2 simplifies image assessment and expands its applicability to prostate MRI without contrast medium. PI-QUAL v2 focuses on critical technical and qualitative image parameters and emphasises T2-WI and DWI. [ABSTRACT FROM AUTHOR]

Objectives: To establish and evaluate an ultra-fast MRI screening protocol for prostate cancer (PCa) in comparison to the standard multiparametric (mp) protocol, reducing scan time and maintaining adequate diagnostic performance. Materials and methods: This prospective single-center study included consecutive biopsy-naïve patients with suspected PCa between December 2022 and March 2023. A PI-RADSv2.1 conform mpMRI protocol was acquired in a 3 T scanner (scan time: 25 min 45 sec). In addition, two deep-learning (DL) accelerated sequences (T2- and diffusion-weighted) were acquired, serving as a screening protocol (scan time: 3 min 28 sec). Two readers evaluated image quality and the probability of PCa regarding PI-RADSv2.1 scores in two sessions. The diagnostic performance of the screening protocol with mpMRI serving as the reference standard was derived. Inter- and intra-reader agreements were evaluated using weighted kappa statistics. Results: We included 77 patients with 97 lesions (mean age: 66 years; SD: 7.7). Diagnostic performance of the screening protocol was excellent with a sensitivity and specificity of 100%/100% and 89%/98% (cut-off ≥ PI-RADS 4) for reader 1 (R1) and reader 2 (R2), respectively. Mean image quality was 3.96 (R1) and 4.35 (R2) for the standard protocol vs. 4.74 and 4.57 for the screening protocol (p < 0.05). Inter-reader agreement was moderate (κ: 0.55) for the screening protocol and substantial (κ: 0.61) for the multiparametric protocol. Conclusion: The ultra-fast screening protocol showed similar diagnostic performance and better imaging quality compared to the mpMRI in under 15% of scan time, improving efficacy and enabling the implementation of screening protocols in clinical routine. Clinical relevance statement: The ultra-fast protocol enables examinations without contrast administration, drastically reducing scan time to 3.5 min with similar diagnostic performance and better imaging quality. This facilitates patient-friendly, efficient examinations and addresses the conflict of increasing demand for examinations at currently exhausted capacities. Key Points: Time-consuming MRI protocols are in conflict with an expected increase in examinations required for prostate cancer screening. An ultra-fast MRI protocol shows similar performance and better image quality compared to the standard protocol. Deep-learning acceleration facilitates efficient and patient-friendly examinations, thus improving prostate cancer screening capacity. [ABSTRACT FROM AUTHOR]

Background and aim: The present meta-analysis aims to investigate a potential link between pelvic inflammatory disease (PID) and an increased risk of genitourinary cancers (ovarian, cervical, uterus, and vagina cancers). While previous research has hinted at a possible link, this meta-analysis seeks to delve deeper into the available evidence. Understanding this association is crucial for preventive strategies and improving clinical management practices. Methodology: A comprehensive literature search was conducted across various databases, covering studies published between 2016 and 2024. We included 13 observational studies meeting stringent criteria, followed by meticulous data extraction and quality assessment. Meta-analytical techniques were then employed to calculate pooled odds ratios (ORs), adjusted hazard ratios (HRs), and 95% confidence intervals (CIs), with heterogeneity assessed using the I2 statistic. Results: Our analysis revealed significant findings, underscoring the association between PID and increased risks of genitourinary cancers. Specifically, individuals with a history of PID demonstrated notably higher odds of developing ovarian cancer (OR = 1.477, 95% CI 1.033–2.207), uterine cancer (OR = 1.263, 95% CI 0.827–2.143), cervical cancer (OR = 1.000, 95% CI 0.900–1.100), and vaginal cancer (OR = 2.500, 95% CI 1.400–4.000) compared to those without such a history. The overall heterogeneity across studies was high (I2 = 82.92%), suggesting varying trends across different populations and study designs. Conclusion: This meta-analysis provides updated evidence supporting a significant association between PID and an increased risk of cervical, ovarian, and uterine cancers. Early detection and management of PID are crucial in potentially mitigating the risk of these cancers. [ABSTRACT FROM AUTHOR]

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Background: Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data. Methods: Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features. Results: For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607–0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862–0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer. Conclusion: This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes. [ABSTRACT FROM AUTHOR]

Background: To compare the diagnostic utility of computed tomography (CT) and ultrasonography (USG) for preoperative determination of metastatic lymph nodes. Methods: Head and neck squamous cell carcinoma (HNSCC) cases planned for neck dissection were included. All underwent preoperative neck USG and CT followed by histopathological evaluation after surgery. Sensitivity, specificity, positive and negative predictive value along with accuracy was calculated for each outcome variable. Efficacy was calculated by comparing these to postoperative histopathology. Both imaging modalities were compared with lymph nodal volume. Results: Eighty-seven lymph nodes were selected from 30 patients. Median volume on CT was 5412.15 mm3 for metastatic and 237.7 mm3 for benign nodes and on ultrasound, 4168.92 mm3 for metastatic and 233.1 mm3 for benign nodes (p < 0.001). Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for volume on USG were 81.8%, 98.7%, 96.6%, 90%, and 97.4% and on CT were 81.8%, 100%, 97.7%, 100%, and 97.4% respectively. Conclusions: Lymph nodal volume had higher sensitivity and specificity for detection of metastatic cervical lymphadenopathy and individually or in combination should be included as one of the criteria in preoperative assessment of HNSCC. [ABSTRACT FROM AUTHOR]

This systematic review investigates the efficacy of metabolite biomarkers for risk assessment or early detection of colorectal cancer (CRC) and its precursors, focusing on pre-diagnostic biospecimens. Searches in PubMed, Web of Science, and SCOPUS through December 2023 identified relevant prospective studies. Relevant data were extracted, and the risk of bias was assessed with the QUADAS-2 tool. Among the 26 studies included, significant heterogeneity existed for case numbers, metabolite identification, and validation approaches. Thirteen studies evaluated individual metabolites, mainly lipids, while eleven studies derived metabolite panels, and two studies did both. Nine panels were internally validated, resulting in an area under the curve (AUC) ranging from 0.69 to 0.95 for CRC precursors and 0.72 to 1.0 for CRC. External validation was limited to one panel (AUC = 0.72). Metabolite panels and lipid-based biomarkers show promise for CRC risk assessment and early detection but require standardization and extensive validation for clinical use. [ABSTRACT FROM AUTHOR]

The Strengthening the Screening of Lung Cancer in Europe (SOLACE) initiative, supported by Europe's Beating Cancer Plan, is dedicated to advancing lung cancer screening. This initiative brings together the most extensive pan-European network of respiratory and radiology experts, involving 37 partners from 15 countries. SOLACE aims to enhance equitable access to lung cancer screening by developing targeted recruitment strategies for underrepresented and high-risk populations. Through comprehensive work packages, SOLACE integrates scientific research, pilot studies, and sustainability efforts to bolster regional and national screening efforts across EU member states. Critical relevance statement: The SOLACE project aims to facilitate the optimization and implementation of equitable lung cancer screening programs across the heterogeneous healthcare landscape in EU member states. Key Points: The effectiveness of lung cancer screening is supported by both scientific evidence and now increasing legislative support. SOLACE aims to develop, test, and disseminate tools to facilitate the realization of lung cancer screening at both a national and regional level. Previously underrepresented populations in lung cancer screening will be targeted by tailored recruitment strategies. SOLACE forms the first pan-European network of experts poised to drive real-world implementation of lung cancer screening. [ABSTRACT FROM AUTHOR]

In the past decade, the medical field is witnessing a greater need and the subsequent involvement of technology and automation. Skin cancer detection is one particular area in the medical field that is in dire need of such automation because day by day, the complexity of skin cancer is increasing and it is becoming more difficult to depend on specialized doctors at all instances. The aim of this work is to create a skin cancer detection system using DICOM images and multi-column convolutional neural networks (CNNs). DICOM dataset was chosen because it contains additional information about a patient which is not available in raw images, like age, gender, special medical conditions, time period, etc. This work uses the ISIC 2020 dataset which contains DICOM formatted data of cancerous and non-cancerous images of the outer skin. The images are trained in a separate multi-column CNN architecture, and simultaneously the additional information, or tag information, is trained in a separate dense network, then the parameters from these two sub-models are concatenated to give a single prediction. For comparison purpose, three other models were developed, one based on raw images alone, one based on preprocessed images and one based on preprocessed images alone with a multi-column CNN network. The following evaluation metrics are recorded and compared for all models: accuracy (training, validation and testing), F1-score, specificity and root-mean-square error (RMSE). Additionally, a comparative analysis is carried out with a previous work on the same field published in 2019 that utilized a MobileNet architecture. The proposed model achieved high accuracy when it is compared with other models. Moreover, the model also provide the results for a tenfold cross-validation carried out and to highlight the specific data split that yields the best results for training, validation and testing accuracies. [ABSTRACT FROM AUTHOR]

The increasing incidence of skin cancer necessitates advancements in early detection methods, where deep learning can be beneficial. This study introduces SkinNet-14, a novel deep learning model designed to classify skin cancer types using low-resolution dermoscopy images. Unlike existing models that require high-resolution images and extensive training times, SkinNet-14 leverages a modified compact convolutional transformer (CCT) architecture to effectively process 32 × 32 pixel images, significantly reducing the computational load and training duration. The framework employs several image preprocessing and augmentation strategies to enhance input image quality and balance the dataset to address class imbalances in medical datasets. The model was tested on three distinct datasets—HAM10000, ISIC and PAD—demonstrating high performance with accuracies of 97.85%, 96.00% and 98.14%, respectively, while significantly reducing the training time to 2–8 s per epoch. Compared to traditional transfer learning models, SkinNet-14 not only improves accuracy but also ensures stability even with smaller training sets. This research addresses a critical gap in automated skin cancer detection, specifically in contexts with limited resources, and highlights the capabilities of transformer-based models that are efficient in medical image analysis. [ABSTRACT FROM AUTHOR]

Transitioning to an individualized risk-based approach can significantly enhance cervical cancer screening programs. We aimed to derive and internally validate a prediction model for assessing the risk of cervical intraepithelial neoplasia grade 3 or higher (CIN3+) and cancer in women eligible for screening. This retrospective study utilized data from the Estonian electronic health records, including 517,884 women from the health insurance database and linked health registries. We employed Cox proportional hazard regression, incorporating reproductive and medical history variables (14 covariates), and utilized the least absolute shrinkage and selection operator (LASSO) for variable selection. A 10-fold cross-validation for internal validation of the model was used. The main outcomes were the performance of discrimination and calibration. Over the 8-year follow-up, we identified 1326 women with cervical cancer and 5929 with CIN3+, with absolute risks of 0.3% and 1.1%, respectively. The prediction model for CIN3 + and cervical cancer had good discriminative power and was well calibrated Harrell's C of 0.74 (0.73–0.74) (calibration slope 1.00 (0.97–1.02) and 0.67 (0.66–0.69) (calibration slope 0.92 (0.84–1.00) respectively. A developed model based on nationwide electronic health data showed potential utility for risk stratification to supplement screening efforts. This work was supported through grants number PRG2218 from the Estonian Research Council, and EMP416 from the EEA (European Economic Area) and Norway Grants. [ABSTRACT FROM AUTHOR]

Enhancing early-stage breast cancer detection requires integrating additional screening methods with current diagnostic imaging. Omics screening, using easily collectible serum samples, could serve as an initial step. Alongside biomarker identification capabilities, omics analysis allows for a comprehensive analysis of prevalent histological types—DCIS and IDC. Employing metabolomics, metallomics, and machine learning, could yield accurate screening models with valuable insights into organism responses. Serum samples of confirmed breast cancer patients were utilized to analyze metabolite and metal ion profiles, using two distinct analysis methods, proton NMR for metabolomics and ICP-OES for metallomics. The resulting responses were then subjected to discriminant analysis, progression biomarker exploration, examination of correlations between patients' metabolites and metal ions, and the impact of age and menopause status. Measured NMR spectra and metabolite relative integrals were used to achieve statistically significant discrimination through MVA between breast cancer and control groups. The analysis identified 24 metabolites and 4 metal ions crucial for discrimination. Furthermore, four metabolites were associated with disease progression. Additionally, there were important correlations and relationships between metabolite relative integrals, metal ion concentrations, and age/menopausal status subgroups. Quantified relative integrals allowed for discrimination between studied subgroups, validated with a holdout set. Feature importance and statistical analysis for metabolomics and metallomics extracted a set of common entities which in combination provides valuable insights into ongoing molecular disturbances and disease progression. [ABSTRACT FROM AUTHOR]

This study aims to identify colorectal cancer (CRC)-related proteomic profiles and develop a prediction model for CRC onset by integrating proteomic profiles with genetic and non-genetic factors (QCancer-15) to improve the risk stratification and estimate of personalized initial screening age. Here, using a two-stage strategy, we prioritize 15 protein biomarkers as predictors to construct a protein risk score (ProS). The risk prediction model integrating proteomic profiles with polygenic risk score (PRS) and QCancer-15 risk score (QCancer-S) shows improved performance (C-statistic: 0.79 vs. 0.71, P = 4.94E–03 in training cohort; 0.75 vs 0.69, P = 5.49E–04 in validation cohort) and net benefit than QCancer-S alone. The combined model markedly stratifies the risk of CRC onset. Participants with high ProS, PRS, or combined risk score are proposed to start screening at age 46, 41, or before 40 years old. In this work, the integration of blood proteomics with PRS and QCancer-15 demonstrates improved performance for risk stratification and clinical implication for the derivation of risk-adapted starting ages of CRC screening, which may contribute to the decision-making process for CRC screening. Non-invasive approaches for risk stratification and screening remain crucial for colorectal cancer (CRC). Here, the authors develop a model for CRC risk stratification by integrating plasma proteomics data with polygenic risk score and QCancer-15 risk score, which could guide recommendations for earlier screening of high-risk populations. [ABSTRACT FROM AUTHOR]

Purpose: Although cystoscopy is a reliable tool for detecting bladder cancer, it poses a high burden on patients and entails high costs. This highlights the need for non-invasive and cost-effective alternatives. This study aimed to validate a previously developed urinary methylation marker panel containing GHSR and MAL. Methods: We enrolled 134 patients who underwent cystoscopy because of hematuria, including 63 individuals with primary bladder cancer and 71 with non-malignant findings. Urine samples were self-collected at home and sent via regular mail. Subsequently, DNA was extracted and the hypermethylation of GHSR and MAL was evaluated using quantitative methylation-specific polymerase chain reaction. The performance of methylation markers was assessed using area-under-the-curve (AUC) analysis and sensitivity and specificity based on pre-established cut-off values. Results: Validation of the marker panel GHSR/MAL resulted in an AUC of 0.87 at 79% sensitivity and 80% specificity. Sensitivity was comparable to the previous investigation (P > 0.9), though specificity was significantly lower (P = 0.026). Sensitivity was higher for high-grade tumors compared to low-grade tumors (94% vs. 60%, P = 0.002). Conclusion: Validation of the GHSR/MAL methylation marker panel on at home collected urine samples confirms its robust performance for bladder cancer detection in a hematuria population, and underscores the diagnostic potential for future clinical application. [ABSTRACT FROM AUTHOR]

Objectives: To validate the performance of Mirai, a mammography-based deep learning model, in predicting breast cancer risk over a 1–5-year period in Mexican women. Methods: This retrospective single-center study included mammograms in Mexican women who underwent screening mammography between January 2014 and December 2016. For women with consecutive mammograms during the study period, only the initial mammogram was included. Pathology and imaging follow-up served as the reference standard. Model performance in the entire dataset was evaluated, including the concordance index (C-Index) and area under the receiver operating characteristic curve (AUC). Mirai's performance in terms of AUC was also evaluated between mammography systems (Hologic versus IMS). Clinical utility was evaluated by determining a cutoff point for Mirai's continuous risk index based on identifying the top 10% of patients in the high-risk category. Results: Of 3110 patients (median age 52.6 years ± 8.9), throughout the 5-year follow-up period, 3034 patients remained cancer-free, while 76 patients developed breast cancer. Mirai achieved a C-index of 0.63 (95% CI: 0.6–0.7) for the entire dataset. Mirai achieved a higher mean C-index in the Hologic subgroup (0.63 [95% CI: 0.5–0.7]) versus the IMS subgroup (0.55 [95% CI: 0.4–0.7]). With a Mirai index score > 0.029 (10% threshold) to identify high-risk individuals, the study revealed that individuals in the high-risk group had nearly three times the risk of developing breast cancer compared to those in the low-risk group. Conclusions: Mirai has a moderate performance in predicting future breast cancer among Mexican women. Critical relevance statement: Prospective efforts should refine and apply the Mirai model, especially to minority populations and women aged between 30 and 40 years who are currently not targeted for routine screening. Key Points: The applicability of AI models to non-White, minority populations remains understudied. The Mirai model is linked to future cancer events in Mexican women. Further research is needed to enhance model performance and establish usage guidelines. [ABSTRACT FROM AUTHOR]

Prostate cancer (PC) is an epithelial malignancy occurring in the prostate. PC ranks second in incidence among all male malignancies globally by the latest statistics from the World Health Organization. Notably, China has seen a more rapid increase in PC incidence compared to developed European and American nations. By 2022, the newly reported cases and deaths due to PC in China increased to 134,200 and 47,500, respectively. Thus, early diagnosis and standardized treatment for prostate cancer in China remain far-reaching objectives. Burgeoning research on advanced PC and castration-resistant prostate cancer in recent years have paved the way for a new era of integrated treatment methods including novel endocrine drugs, chemotherapy, targeted therapy, and immunotherapy. Future therapies involve precision treatment guided by genetic testing and individualized integrated treatment as part of a multidisciplinary integrated diagnosis and treatment model for PC. The Genitourinary Oncology Committee of the China Anti-Cancer Association (CACA-GU) has invited multidisciplinary experts across fields including surgery, oncology, pathology, radiology, herbal medicine, physiatry, and psychology to collaboratively write, discuss, and revise guidelines on managing PC. The CACA Guidelines for Holistic Integrative Management of Prostate Cancer includes epidemiology, screening and diagnosis, treatment for localized PC, diagnosis and treatment of PC recurrence after radical prostatectomy, management of metastatic PC, traditional Chinese medicine diagnosis and treatment of PC, and rehabilitation from PC. This guideline aims to standardize the clinical diagnosis and treatment management of PC in China. It is more aligned with China's clinical practice, highlights Chinese characteristics, and bears significant clinical importance. [ABSTRACT FROM AUTHOR]

Using the modified discrete dipole approximation (DDA) method, the absorption spectrum of the multi-core-satellite structures (which are formed by connecting plasmonic nanoparticles by two probe molecules and one microRNA-21 molecule) was investigated. Two spherical and chain models were considered for the connection of the plasmonic nanoparticles. The results showed that using the chain connection model of nanoparticles leads to the formation of multiple absorption peaks, which we classified in the three wavelength regions, the short, the middle, and the long region. The three regions was 350 to 500 nm, 500 to 650 nm, and 650 to 800 nm for silver-based structures and 500 to 600 nm, 600 to 700 nm, and 700 to 800 nm for gold based structures. Due to the large number of peaks, the wavelength of the peak with the highest intensity was examined in each region. The superposition of spectra of the different structures can lead to the formation of broad peaks at longer wavelengths, which is in agreement with the previously published experimental results. [ABSTRACT FROM AUTHOR]

In this article, a theoretical study of Tamm plasmon based Refractive Index (RI) sensor for cancer cell detection has been proposed. The proposed sensor structure comprises of a thin titanium nitride (TiN) layer of thickness (dm = 30 nm) followed by a liquid cell of thickness (dL = 1500 nm) then followed by 16 pairs of semi-porous DBR (spDBR). The high and low index layers of spDBR constitute of silicon nitride (Si3N4) and porous Si3N4, respectively. We considered the porosity of low-index layer as 75%. The existence of Tamm plasmon between TiN and spDBR interface and the effect of porosity on the photonic band gap (PBG) of the spDBR have been studied as well. The impact of dm and dL on the reflection spectrum of the proposed structure has also been investigated. We investigated the detection of cancer-affected cells by replacing the liquid cell with respective cancer-affected cell and its corresponding normal cell, including blood cancer (Jurkat), cervical cancer (HeLa), adrenal gland cancer (PC12), two types of breast cancer (MDA-MB-231 and MCF-7), and skin cancer (Basal) cells. The proposed structure achieved the sensitivity, Q-factor, figure of merit, and detection limit of 752.85 nm/RIU, 131.64, 70.62 RIU−1, and 5 × 10−4 RIU, respectively. The proposed design can also be utilized in biological and gas sensing applications. [ABSTRACT FROM AUTHOR]

We explored longitudinal associations between religion/spirituality (R/S) Salience and R/S Attendance, and colorectal cancer screening, among adults aged ≥ 50 years in Alberta, Canada. R/S Salience was not statistically significantly associated with colorectal cancer screening (adjusted odds ratio [aOR]: 1.06, 95% confidence interval [CI] 0.88–1.28). Conversely, R/S Attendance was statistically significantly associated with higher odds of colorectal cancer screening: the aOR was 1.28 (95% CI 1.02–1.59) for participants attending services at least once a month and 1.31 (95% CI 1.01–1.69) for participants attending between one and four times yearly, compared to participants who never attended. Researchers should explore the possibility of delivering colorectal cancer screening programs in R/S settings. [ABSTRACT FROM AUTHOR]

Exosomes, extracellular vesicles (EVs) with an average size of 50–150 nm, transfer various biomolecules and exchange signaling molecules between cells in a paracrine manner. Molecular investigations have revealed that EVs can reflect real-time metabolic changes in normal- and cancer-origin cells and thus harbor valid diagnostic biomarkers. Despite these advantages, the detection of low concentrations of cancer cell EVs in biological fluids is still a great challenge. Here, a new electrochemical Exosensor based on platinum-perovskite is developed for the direct detection of EVs using a biotinylated monoclonal CD63 antibody as a capture element. The label-free method exhibited higher sensitivity with a lower limit of quantification of 2000 EVs/µL with a dynamic linear range (LDR) of 2000 to 14,000 EVs/μL compared with other available methods. To enhance the selectivity of detection, EVs were simultaneously sandwiched between secondary antibodies of PSA (prostate-specific antigen), as an FDA-approved prostate cancer biomarker. Data indicated that this Exosensor can distinguish normal and cancer EVs in samples from healthy individuals and prostate cancer patients. Taken together, this technology offers a unique approach to label-free quantification of EVs and cancer detection in the early stages. [ABSTRACT FROM AUTHOR]

A new, sensitive, and cost-effective lab-on-paper-based immunosensor was designed based on electrochemical impedance spectroscopy (EIS) for the detection of exosomes. EIS was selected as the determination method since there was a surface blockage in electron transfer by binding the exosomes to the transducer. Briefly, the carbon working electrode (WE) on the paper electrode (PE) was modified with gold particles (AuPs@PE) and then conjugated with anti-CD9 (Anti-CD9/AuPs@PE) for the detection of exosomes. Variables involved in the biosensor design were optimized with the univariate mode. The developed method presents the limit of detection of 8.7 × 102 exosomes mL−1, which is lower than that of many other available methods under the best conditions. The biosensor was also tested with urine samples from cancer patients with high recoveries. Due to this a unique, low-cost, biodegradable technology is presented that can directly measure exosomes without labeling them for early cancer or metastasis detection. [ABSTRACT FROM AUTHOR]

Hypoxia is one of the defining characteristics of the tumor microenvironment (TME) in solid cancers. It has a major impact on the growth and spread of malignant cells as well as their resistance to common treatments like radiation and chemotherapy. Here, we explore the complex functions of hypoxia in the TME and investigate its effects on angiogenesis, immunological evasion, and cancer cell metabolism. For prognostic and therapeutic reasons, hypoxia identification is critical, and recent developments in imaging and molecular methods have enhanced our capacity to precisely locate underoxygenated areas inside tumors. Furthermore, targeted therapies that take advantage of hypoxia provide a potential new direction in the treatment of cancer. Therapeutic approaches that specifically target hypoxic conditions in tumors without causing adverse effects are being led by hypoxia-targeted nanocarriers and hypoxia-activated prodrugs (HAPs). This review provides an extensive overview of this dynamic and clinically significant area of oncology research by synthesizing current knowledge about the mechanisms of hypoxia in cancer, highlighting state-of-the-art detection methodologies, and assessing the potential and efficacy of hypoxia-targeted therapies. [ABSTRACT FROM AUTHOR]

Background: Game Changers for Cervical Cancer Prevention (GC-CCP), a peer-led, group advocacy training intervention, increased cervical cancer (CC) prevention advocacy not only among intervention recipients, but also their social network members (referred to as "alters") who were targeted with advocacy in a pilot randomized controlled trial. We examined mediators and moderators of this effect on alter advocacy, to understand how and for whom the intervention had such an effect. Method: Forty women (index participants) who had recently screened for CC enrolled and were randomly assigned to receive the GC-CCP intervention (n = 20) or the wait-list control (n = 20). Up to three alters from each participant (n = 103) were surveyed at baseline and month 6. Measures of CC-related cognitive constructs (knowledge, enacted stigma, and risk management self-efficacy), as well as extent of advocacy received from index participants, were assessed as mediators of the intervention effect on alter advocacy using multivariate regression analyses. Alter characteristics were examined as moderators. Results: Increased CC-related knowledge partially mediated the intervention effect on increased alter engagement in CC prevention advocacy; those with greater gains in knowledge reported greater engagement in advocacy. No moderators of the intervention effect were identified. Conclusion: The effect of GC-CCP on alter CC prevention advocacy is enhanced by increased alter knowledge pertaining to CC prevention, causes, and treatment and suggests this may be key for diffusion of intervention effects on increased CC prevention advocacy throughout a social network. Trial Registration: NCT04960748 (registered on clinicaltrials.gov, 7/14/2021). [ABSTRACT FROM AUTHOR]

Ovarian cancer is a common gynecological tumor, with a high mortality rate and difficult clinical treatment. Early detection of ovarian cancer has significant diagnostic value. In response to the problem of poor diagnostic performance of traditional early diagnosis methods, this article designed an automated early ovarian cancer detection system to improve the detection of early ovarian cancer. The conventional early diagnosis methods include serum CA125 (carbohydrate antigen 125) detection and positron emission tomography/computed tomography (PET/CT) imaging. This article combined serum CA125 detection and PET/CT imaging to detect the CA125 level and maximum standardized uptake value (SUV) in patient's serum. When the CA125 level exceeded 35U/ml and the maximum SUV value exceeded 2.5, the test was considered positive. This article selected 200 patients from Jingzhou Hospital for the experiment and compared the three detection methods. The average specificity of single serum CA125 detection, single PET/CT imaging, and automated detection in patients under 50 were 61.24%, 79.57%, and 97.79%, respectively. The automated early ovarian cancer detection system designed in this article can significantly improve the specificity of early ovarian cancer detection and has excellent application value for early ovarian cancer detection. [ABSTRACT FROM AUTHOR]

Purpose: Firefighting is known to be carcinogenic to humans. However, current lung cancer screening guidelines do not account for occupational exposure. We hypothesize that firefighting is an independent risk factor associated with the development of high-risk lung nodules on low-dose CT (LDCT). Methods: Members of a firefighter's union underwent LDCT at a single institution between April 2022 and June 2023 within a lung cancer screening program. Results were interpreted by designated chest radiologists and reported using the Lung-RADS scoring system. Demographic and radiographic data were recorded, and summary statistics are reported. Results: 1347 individuals underwent lung cancer screening, with a median age of 51 years (IQR 42–58), including 56 (4.2%) females. Overall, 899 (66.7%) were never smokers, 345 (25.6%) were former smokers, and 103 (7.7%) were current smokers. There were 41 firefighters (3.0%) who had high-risk (Lung-RADS 3 or 4) nodules requiring intervention or surveillance, of which 21 (1.5%) were Lung-RADS 3 and 20 (1.5%) that were Lung-RADS 4. Of the firefighters with high-risk nodules, only 6 (14.6%) were eligible for LDCT based on current screening guidelines. There were 7 high-risk nodules (0.5%) that required procedural intervention, 6 (85.7%) of which were from the non-screening eligible cohort. There were also 20 never-smoking firefighters (57.1%) with high-risk nodules that were non-screening eligible. Conclusion: Firefighting, even in the absence of smoking history, may be associated with the development of high-risk lung nodules on LDCT. Carefully selected occupational exposures should be considered in the development of future lung cancer screening guidelines. [ABSTRACT FROM AUTHOR]

Prostate-specific antigen (PSA)-based prostate cancer screening is a preference-sensitive decision for which experts recommend a shared decision making (SDM) approach. This study aimed to examine PSA screening SDM in primary care. Methods included qualitative analysis of audio-recorded patient-provider interactions supplemented by quantitative description. Participants included 5 clinic providers and 13 patients who were: (1) 40–69 years old, (2) Black, (3) male, and (4) attending clinic for routine primary care. Main measures were SDM element themes and "observing patient involvement in decision making" (OPTION) scoring. Some discussions addressed advantages, disadvantages, and/or scientific uncertainty of screening, however, few patients received all SDM elements. Nearly all providers recommended screening, however, only 3 patients were directly asked about screening preferences. Few patients were asked about prostate cancer knowledge (2), urological symptoms (3), or family history (6). Most providers discussed disadvantages (80%) and advantages (80%) of PSA screening. Average OPTION score was 25/100 (range 0–67) per provider. Our study found limited SDM during PSA screening consultations. The counseling that did take place utilized components of SDM but inconsistently and incompletely. We must improve SDM for PSA screening for diverse patient populations to promote health equity. This study highlights the need to improve SDM for PSA screening. [ABSTRACT FROM AUTHOR]

Breast cancer is the most frequently diagnosed cancer in women accounting for about 30% of all new cancer cases and the incidence is constantly increasing. Implementation of mammographic screening has contributed to a reduction in breast cancer mortality of at least 20% over the last 30 years. Screening programs usually include all women irrespective of their risk of developing breast cancer and with age being the only determining factor. This approach has some recognized limitations, including underdiagnosis, false positive cases, and overdiagnosis. Indeed, breast cancer remains a major cause of cancer-related deaths in women undergoing cancer screening. Supplemental imaging modalities, including digital breast tomosynthesis, ultrasound, breast MRI, and, more recently, contrast-enhanced mammography, are available and have already shown potential to further increase the diagnostic performances. Use of breast MRI is recommended in high-risk women and women with extremely dense breasts. Artificial intelligence has also shown promising results to support risk categorization and interval cancer reduction. The implementation of a risk-stratified approach instead of a "one-size-fits-all" approach may help to improve the benefit-to-harm ratio as well as the cost-effectiveness of breast cancer screening. Key Points: Regular mammography should still be considered the mainstay of the breast cancer screening. High-risk women and women with extremely dense breast tissue should use MRI for supplemental screening or US if MRI is not available. Women need to participate actively in the decision to undergo personalized screening. Key recommendations: Mammography is an effective imaging tool to diagnose breast cancer in an early stage and to reduce breast cancer mortality (evidence level I). Until more evidence is available to move to a personalized approach, regular mammography should be considered the mainstay of the breast cancer screening. High-risk women should start screening earlier; first with yearly breast MRI which can be supplemented by yearly or biennial mammography starting at 35–40 years old (evidence level I). Breast MRI screening should be also offered to women with extremely dense breasts (evidence level I). If MRI is not available, ultrasound can be performed as an alternative, although the added value of supplemental ultrasound regarding cancer detection remains limited. Individual screening recommendations should be made through a shared decision-making process between women and physicians. [ABSTRACT FROM AUTHOR]

Objectives: To develop and test zone-specific prostate-specific antigen density (sPSAD) combined with PI-RADS to guide prostate biopsy decision strategies (BDS). Methods: This retrospective study included consecutive patients, who underwent prostate MRI and biopsy (01/2012–10/2018). The whole gland and transition zone (TZ) were segmented at MRI using a retrained deep learning system (DLS; nnU-Net) to calculate PSAD and sPSAD, respectively. Additionally, sPSAD and PI-RADS were combined in a BDS, and diagnostic performances to detect Grade Group ≥ 2 (GG ≥ 2) prostate cancer were compared. Patient-based cancer detection using sPSAD was assessed by bootstrapping with 1000 repetitions and reported as area under the curve (AUC). Clinical utility of the BDS was tested in the hold-out test set using decision curve analysis. Statistics included nonparametric DeLong test for AUCs and Fisher-Yates test for remaining performance metrics. Results: A total of 1604 patients aged 67 (interquartile range, 61–73) with 48% GG ≥ 2 prevalence (774/1604) were evaluated. By employing DLS-based prostate and TZ volumes (DICE coefficients of 0.89 (95% confidence interval, 0.80–0.97) and 0.84 (0.70–0.99)), GG ≥ 2 detection using PSAD was inferior to sPSAD (AUC, 0.71 (0.68–0.74)/0.73 (0.70–0.76); p < 0.001). Combining PI-RADS with sPSAD, GG ≥ 2 detection specificity doubled from 18% (10–20%) to 43% (30–44%; p < 0.001) with similar sensitivity (93% (89–96%)/97% (94–99%); p = 0.052), when biopsies were taken in PI-RADS 4-5 and 3 only if sPSAD was ≥ 0.42 ng/mL/cc as compared to all PI-RADS 3-5 cases. Additionally, using the sPSAD-based BDS, false positives were reduced by 25% (123 (104–142)/165 (146–185); p < 0.001). Conclusion: Using sPSAD to guide biopsy decisions in PI-RADS 3 lesions can reduce false positives at MRI while maintaining high sensitivity for GG ≥ 2 cancers. Clinical relevance statement: Transition zone-specific prostate-specific antigen density can improve the accuracy of prostate cancer detection compared to MRI assessments alone, by lowering false-positive cases without significantly missing men with ISUP GG ≥ 2 cancers. Key Points: • Prostate biopsy decision strategies using PI-RADS at MRI are limited by a substantial proportion of false positives, not yielding grade group ≥ 2 prostate cancer. • PI-RADS combined with transition zone (TZ)-specific prostate-specific antigen density (PSAD) decreased the number of unproductive biopsies by 25% compared to PI-RADS only. • TZ-specific PSAD also improved the specificity of MRI-directed biopsies by 9% compared to the whole gland PSAD, while showing identical sensitivity. [ABSTRACT FROM AUTHOR]

Objectives: Supplemental MRI screening improves early breast cancer detection and reduces interval cancers in women with extremely dense breasts in a cost-effective way. Recently, the European Society of Breast Imaging recommended offering MRI screening to women with extremely dense breasts, but the debate on whether to implement it in breast cancer screening programs is ongoing. Insight into the participant experience and willingness to re-attend is important for this discussion. Methods: We calculated the re-attendance rates of the second and third MRI screening rounds of the DENSE trial. Moreover, we calculated age-adjusted odds ratios (ORs) to study the association between characteristics and re-attendance. Women who discontinued MRI screening were asked to provide one or more reasons for this. Results: The re-attendance rates were 81.3% (3458/4252) and 85.2% (2693/3160) in the second and third MRI screening round, respectively. A high age (> 65 years), a very low BMI, lower education, not being employed, smoking, and no alcohol consumption were correlated with lower re-attendance rates. Moderate or high levels of pain, discomfort, or anxiety experienced during the previous MRI screening round were correlated with lower re-attendance rates. Finally, a plurality of women mentioned an examination-related inconvenience as a reason to discontinue screening (39.1% and 34.8% in the second and third screening round, respectively). Conclusions: The willingness of women with dense breasts to re-attend an ongoing MRI screening study is high. However, emphasis should be placed on improving the MRI experience to increase the re-attendance rate if widespread supplemental MRI screening is implemented. Clinical relevance statement: For many women, MRI is an acceptable screening method, as re-attendance rates were high — even for screening in a clinical trial setting. To further enhance the (re-)attendance rate, one possible approach could be improving the overall MRI experience. Key Points: • The willingness to re-attend in an ongoing MRI screening study is high. • Pain, discomfort, and anxiety in the previous MRI screening round were related to lower re-attendance rates. • Emphasis should be placed on improving MRI experience to increase the re-attendance rate in supplemental MRI screening. [ABSTRACT FROM AUTHOR]

Objective: To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program. Materials and method: We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013–2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1–4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1–10 were stratified by VDG. Results: We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1–91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2–91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9–95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3–99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5–70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8–68.7) for VDG4. Conclusion: The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4. Clinical relevance statement: Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density. Key Points: • Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists. [ABSTRACT FROM AUTHOR]

Purpose: Cervical cancer is the fourth most common cancer in women worldwide. A successful screening concept for cervical cancer reduces the incidence and mortality of cervical cancer. Quality indicators (QIs) derived from the screening guidelines for cervical cancer and used by the certified dysplasia units and dysplasia consultancies are evaluated in this paper. The aim of this paper is to present the current data from the annual reports of these units and consultancies. Methods: The results of the basic data and indicators for the audit year 2022 in the gynaecological dysplasia consultancies and units are presented. In 2022, 84 dysplasia consultancies and 42 units were audited. 40 units and 84 consultancies are included in the annual report. QI outcomes for patients treated in certified dysplasia units and dysplasia consultancies are analysed. Median, overall proportion, and standard deviation were calculated for each QI. Results: The indicator year 2021 was analysed, which was audited in 2022 and evaluated in 2023. A total of nine QIs were analysed. Most target goals were met by the 84 certified dysplasia consultancies and by the 40 dysplasia units. The QIs evaluated are implemented to a very high degree. The targets for the three QIs were achieved by both the dysplasia consultancies and the units in at least 95% of the certified centres (QI 1: 100%, QI 2: 95%, QI 3: 100%; QI 1: 100%, QI 2: 97%, QI 3: 100%, respectively). The presentation of patients to the tumour board by the consultancies/units is working; the units are attending the tumour board more regularly than in previous years. Where the target was not met, the auditors issued deviations or reduced the duration of the certificate. The cases are discussed intensively in the sense of an individual case analysis and with the determination of measures on-site. Conclusions: The targets for the various indicators were largely met by the dysplasia consultancies and units in the 2022 audit year. The certification of gynaecological dysplasia consultancies/units which have to cooperate with certified gynaecological cancer centres, has for the first time ensured the continuity of healthcare from prevention and early diagnosis to treatment of gynaecological cancers. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related mortality and it is often diagnosed at advanced stages due to non-specific clinical presentation. Disease detection at localized disease stage followed by surgical resection remains the only potentially curative treatment. In this era of precision medicine, a multifaceted approach to early detection of PDAC includes targeted screening in high-risk populations, serum biomarkers and "liquid biopsies", and artificial intelligence augmented tumor detection from radiologic examinations. In this review, we will review these emerging techniques in the early detection of PDAC. [ABSTRACT FROM AUTHOR]

Purpose: To develop a deep learning (DL) zonal segmentation model of prostate MR from T2-weighted images and evaluate TZ-PSAD for prediction of the presence of csPCa (Gleason score of 7 or higher) compared to PSAD. Methods: 1020 patients with a prostate MRI were randomly selected to develop a DL zonal segmentation model. Test dataset included 20 cases in which 2 radiologists manually segmented both the peripheral zone (PZ) and TZ. Pair-wise Dice index was calculated for each zone. For the prediction of csPCa using PSAD and TZ-PSAD, we used 3461 consecutive MRI exams performed in patients without a history of prostate cancer, with pathological confirmation and available PSA values, but not used in the development of the segmentation model as internal test set and 1460 MRI exams from PI-CAI challenge as external test set. PSAD and TZ-PSAD were calculated from the segmentation model output. The area under the receiver operating curve (AUC) was compared between PSAD and TZ-PSAD using univariate and multivariate analysis (adjusts age) with the DeLong test. Results: Dice scores of the model against two radiologists were 0.87/0.87 and 0.74/0.72 for TZ and PZ, while those between the two radiologists were 0.88 for TZ and 0.75 for PZ. For the prediction of csPCa, the AUCs of TZPSAD were significantly higher than those of PSAD in both internal test set (univariate analysis, 0.75 vs. 0.73, p < 0.001; multivariate analysis, 0.80 vs. 0.78, p < 0.001) and external test set (univariate analysis, 0.76 vs. 0.74, p < 0.001; multivariate analysis, 0.77 vs. 0.75, p < 0.001 in external test set). Conclusion: DL model-derived zonal segmentation facilitates the practical measurement of TZ-PSAD and shows it to be a slightly better predictor of csPCa compared to the conventional PSAD. Use of TZ-PSAD may increase the sensitivity of detecting csPCa by 2–5% for a commonly used specificity level. [ABSTRACT FROM AUTHOR]

The phenomenon of surface plasmon resonance (SPR) has attracted a lot of attention in recent years due to its potential applications in various fields, including biology, chemistry, materials physics and sensing technologies. In particular, SPR is used in optical devices, sensors and optoelectronic devices because of its sensitive and selective sensing capabilities. In this paper, we have used a numerical approach based on the finite element method (FEM) to study the performance of a highly sensitive D-shaped optical fiber biosensor, exploiting SPR, for the early detection of cancer in individual living cells. The sensitive part of the proposed biosensor consists of a thin layer of gold (Au) covered by a layer of titanium dioxide TiO2. Our numerical analysis aims to find the optimum design and handling parameters for detecting three types of cancer, namely breast cancer MDA-MB-231, MCF-7 and skin cancer (basal cells). To achieve this goal, we modeled the sensor's sensitivity to the change in refractive index of the surrounding biological medium when introduced into healthy human cells and their cancerous counterparts. Our results show that the maximum sensitivity of the proposed sensor reaches the values of 2017 n m. R I U - 1 , 2016 n m. R I U - 1 and 1571 n m. R I U - 1 respectively when used for the detection of MDA-MB-231, MCF-7 and Basal cell cancers. RI resolution is estimated at 4 , 96. 10 - 6 R I U for MDA-MB-231 and MCF-7, while for Basal cell it is estimated at 6 , 37. 10 - 6 R I U . [ABSTRACT FROM AUTHOR]

Less than two-thirds of US adolescents are up-to-date with HPV vaccination. While mothers engaged in preventive care are more likely to seek preventive care for their children, current studies on associations between maternal cervical cancer screening (CCS) and adolescent HPV vaccination are needed. We assessed associations between maternal preventive service utilization and adolescent HPV vaccination using electronic health record data from a healthcare system in Washington State. We included adolescents (11–17 years) and their mothers with ≥ 1 primary care visit between 2018 and 2020. Outcomes were HPV vaccine initiation and completion. The primary exposure was maternal adherence to guideline-recommended CCS. Secondary exposures were maternal breast cancer screening adherence (for mothers ≥ 52 years) and ≥ 1 wellness visit ≤ 2 years. We used Generalized Estimating Equations to estimate prevalence ratios, and explore effect modification by adolescent sex, adolescent provider characteristics, and maternal language interpreter use. Of 4121 adolescents, 66% had a CCS-adherent mother, 82% initiated HPV vaccination, and 49% completed the series. CCS adherence was associated with higher initiation (adjusted prevalence ratio (APR):1.10, 95%CI:1.06–1.13) and completion (APR:1.16, 95%CI:1.08–1.23). Associations were stronger for male vs. female adolescents, adolescents who had a primary care provider in family practice vs. pediatrics, and adolescents who had the same primary care provider as their mother vs. not. Recent maternal wellness visit was also associated with higher initiation (APR:1.04, 95%CI:1.01–1.07) and completion (APR:1.12, 95%CI:1.05–1.20). Results suggest that delivering healthcare through a family-centered approach and engaging mothers in broad preventive care could increase adolescent HPV vaccination coverage. [ABSTRACT FROM AUTHOR]

Variational autoencoders (VAEs) are generative models known for learning compact and continuous latent representations of data. While they have proven effective in various applications, using latent representations for classification tasks presents challenges. Typically, a straightforward approach involves concatenating the mean and variance vectors and inputting them into a shallow neural network. In this paper, we introduce a novel approach for variational autoencoders, named VAE-GNA, which integrates Gaussian neurons into the latent space along with attention mechanisms. These neurons directly process mean and variance values through a suitable modified sigmoid function, not only improving classification, but also optimizing the training of the VAE in extracting features, in synergy with the classification network. Additionally, we investigate both additive and multiplicative attention mechanisms to enhance the model's capabilities. We applied the proposed method to automatic cancer detection using near-infrared (NIR) spectral data, showing that the experimental results of VAE-GNA surpass established baselines for spectral datasets. The results obtained indicate the feasibility and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Introduction: Venous thromboembolism (VTE) may be the first sign of an undiagnosed cancer. The RIETE and SOME scores aim to identify patients with acute VTE at high risk of occult cancer. In the present study, we evaluated the performance of both scores. Methods: The scores were evaluated in a retrospective cohort from two centers. The area under the receiver-operating characteristics curve (AUC) evaluated the discriminatory performance. Results: The RIETE score was applied to 815 patients with provoked and unprovoked VTE, of whom 56 (6.9%) were diagnosed with cancer. Of the 203 patients classified as high-risk, 18 were diagnosed with cancer, representing 32.1% (18/56) of the total cancer diagnoses. In the group of 612 low-risk patients, 67.9% of the cancer cases were diagnosed (38/56). Sensitivity, specificity, negative and positive predictive values, and AUC were 32%, 76%, 94%, 9%, and 0.430 (95% confidence interval [CI], 0.38‒0.47), respectively. The SOME score could be calculated in 418 patients with unprovoked VTE, of whom 33 (7.9%) were diagnosed with cancer. Of the 45 patients classified as high-risk, three were diagnosed with cancer, representing 9.1% (3/33) of the total cancer diagnoses. In the group of 373 low-risk patients, 90.9% of the cancer cases were diagnosed (30/33). Sensitivity, specificity, negative and positive predictive values, and AUC were 33%, 88%, 94%, 20%, and 0.351 (95% CI, 0.27‒0.43), respectively. Conclusions: The performance of both scores was poor. Our results highlight the need to develop new models to identify high-risk patients who may benefit from an extensive cancer screening strategy. [ABSTRACT FROM AUTHOR]

In the quest for early cancer diagnosis, early identification and treatment are paramount. Recently, ctDNA detection has emerged as a viable avenue for early screening of cancer. The examination of ctDNA in fluid biopsies has gained substantial attention in tumor diagnosis and therapy. Both the scientific community and industry are actively exploring this field. However, developing cost-effective, portable, and real-time ctDNA measurement methods using conventional gene detection equipment poses a significant challenge. This challenge has led to the exploration of alternative approaches. Electrochemical biosensors, distinguished by their heightened sensitivity, remarkable specificity, affordability, and excellent portability, have emerged as a promising avenue for ctDNA detection. This review is dedicated to the specific focus on ctDNA detection, highlighting recent advancements in this evolving detection technology. We aimed to reference previous studies related to ctDNA-targeted cancer detection using electrochemical biosensors to advocate the utilization of electrochemical biosensors in healthcare diagnostics. Further research is imperative for the effective integration of ctDNA analysis into point-of-care cancer testing. Innovative approaches utilizing multiple markers need to be explored to advance this technology and make substantial contributions to societal well-being. [ABSTRACT FROM AUTHOR]

Breast cancer is the leading cause of cancer-related mortality among women worldwide. MicroRNAs (miRNAs), short non-coding RNAs, have been implicated in cancer-related processes such as tumor development, metastasis, angiogenesis, and drug resistance. Circulating miRNA-373 demonstrates higher relative exosomal serum levels in breast cancer patients compared to healthy women, making it a potential non-invasive biomarker. Separately, vascular endothelial growth factor (VEGF) is crucial for angiogenesis, and is elevated in breast cancer. In this case–control study, we aimed to investigate the diagnostic accuracy of miRNA-373 and VEGF as biomarkers for early-stage breast cancer detection. Serum samples were collected from 120 participants, comprising 30 breast cancer patients, 30 benign breast tumor patients, and 60 healthy controls, over the period of April 2022 to January 2023. MiRNA-373 expression was analyzed by reverse transcription-quantitative PCR with GAPDH normalisation, while VEGF levels in serum samples were measured by ELISA. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of both biomarkers. MiRNA-373 expression (∆Ct) differed significantly between the three groups (breast cancer: − 12.20 ± 1.11; benign tumors: − 12.79 ± 1.09; controls: − 13.64 ± 0.93). ROC analysis revealed moderate discriminative power for miRNA-373 (specificity = 76.7%; sensitivity = 70.0%; AUC = 0.839) and excellent discriminative power for VEGF (specificity = 85.0%; sensitivity = 90.0%; AUC = 0.944) in distinguishing early-stage breast cancer patients from healthy controls. In summary, this study demonstrates the promising potential of miRNA-373 as an early diagnostic biomarker for breast cancer detection, requiring further validation in larger cohorts. Our findings also reinforce the diagnostic value of circulating VEGF levels for breast cancer screening. [ABSTRACT FROM AUTHOR]

Purpose: Little is known about colorectal cancer screening in 9/11 World Trade Center (WTC)-exposed populations. We utilized survey data from the WTC Health Registry (WTCHR) to examine associations between enrollees' characteristics and colorectal cancer (CRC) screening. Methods: We studied 22,061 enrollees aged 50–75 who completed the WTCHR follow-up survey in 2015–2016. Those with a history of CRC were excluded. Screening was defined as a self-reported, routine colonoscopy or sigmoidoscopy during the 12-month period prior to the survey. Multivariable log binomial regression identified factors associated with screening in the 12 months preceding the survey. We also stratified by age group. Results: Of 22,061 enrollees, 23% were screened, with largely similar rates across age groups. Higher screening percentages were seen in selected groups including non-Hispanic Black enrollees (26.4%), males (24.3%), those married/living with a partner (24.1%), those with a higher household income (≥ $150 k, 25.4%), those who received services from the WTC Health Program (25.6%), and those with greater perceived social support (24.4%). On multivariable analyses, non-Hispanic Black enrollees [adjusted relative risk (aRR) = 1.30, 95% confidence interval (CI) 1.19–1.42] were significantly more likely to report screening, even after stratifying by age group. Hispanic enrollees, those with a higher household income, those with increased perceived social support, and those with diagnosed medical conditions under 70 years old were also associated with screening. Conclusion: We found that non-Hispanic Black compared with non-Hispanic White enrollees were more likely to obtain screening for CRC. Continued efforts to promote health and wellness of WTC-exposed population is essential. [ABSTRACT FROM AUTHOR]