Artificial Intelligence-driven Digital Cytology-based Cervical Cancer Screening: Is the Time Ripe to Adopt This Disruptive Technology in Resource-constrained Settings? A Literature Review

The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, and Deep Learning in oncology, explaining key concepts and algorithms (like SVM, Naïve Bayes, and CNN) in a clear, accessible manner. It aims to make AI advancements understandable to a broad audience, focusing on their application in diagnosing, classifying, and predicting various cancer types, thereby underlining AI's potential to better patient outcomes. Moreover, we present a tabular summary of the most significant advances from the literature, offering a time-saving resource for readers to grasp each study's main contributions. The remarkable benefits of AI-powered algorithms in cancer care underscore their potential for advancing cancer research and clinical practice. This review is a valuable resource for researchers and clinicians interested in the transformative implications of AI in cancer care.

Performance of Human Gene EPB41L3 and HPV 16/18 Viral DNA Methylation to Triage hrHPV-Positive Women

More evidence from population-based cohort studies is required to confirm the application of methylation-based biomarkers in real-world settings. The cross-sectional and 24-month cumulative triage performance of a novel methylation assay targeting the host gene EPB41LE and HPV16/18 DNA L1/L2 regions among hrHPV-positive women was evaluated based on a population-based cohort study from China. Overall methylation positivity was 12.4% among hrHPV-positive women. Methylation-positive women had significantly higher risks of hrHPV persistence at 12M and 24M follow-up (RR12M = 1.9, 95%CI: 1.5-2.6 and RR24M = 1.7, 95%CI: 1.2-2.5). For CIN2+, cross-sectional triage sensitivity of methylation was similar to HPV16/18 (70.6% vs. 64.7%, pexact = 1.000), but was lower than cytology (94.1%), although not significantly (pexact = 0.213). The specificity (91.2%) of methylation was significantly higher than other triage methods (p < 0.001 for all). The longitudinal sensitivity of methylation over 24M follow-up was 56.0%, lower (but not significantly so) than HPV16/18 (64.0%, pexact = 0.688) and cytology (76.0%, pexact = 0.125). Methylation testing showed high positive predictive values for CIN2+ (41.4% at baseline, 50.0% at 24-month), while the CIN2+ risk of methylation negative women (cNPV) remained considerable (2.5% at baseline, 6.9% at 24-month). Study findings indicate that methylation has better specificity and predictive values for the presence or development of cervical precancer and might therefore be considered for the strategy of HPV screening and methylation triage followed by immediate treatment of triage-positive women and delayed follow-up of hrHPV-positive/methylation-negative women.