Defining precancer: a grand challenge for the cancer community

Nature reviews. Cancer, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

---

Tumor-infiltrating lymphocytes in HER2-positive breast cancer: potential impact and challenges

ESMO Open, Journal Year: 2025, Volume and Issue: 10(2), P. 104120 - 104120

Published: Jan. 18, 2025

Language: Английский

---

Revue de presse

Annales de Pathologie, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

---

Artificial Intelligence in Relation to Accurate Information and Tasks in Gynecologic Oncology and Clinical Medicine—Dunning–Kruger Effects and Ultracrepidarianism

Diagnostics, Journal Year: 2025, Volume and Issue: 15(6), P. 735 - 735

Published: March 15, 2025

Publications on the application of artificial intelligence (AI) to many situations, including those in clinical medicine, created 2023–2024 are reviewed here. Because short time frame covered, here, it is not possible conduct exhaustive analysis as would be case meta-analyses or systematic reviews. Consequently, this literature review presents an examination narrative AI’s relation contemporary topics related medicine. The landscape findings here span 254 papers published 2024 topically reporting AI which 83 articles considered present because they contain evidence-based findings. In particular, types cases deal with accuracy initial differential diagnoses, cancer treatment recommendations, board-style exams, and performance various tasks, imaging. Importantly, summaries validation techniques used evaluate presented. This focuses AIs that have a relevancy evidenced by evaluation publications. speaks both what has been promised delivered systems. Readers will able understand when generative may expressing views without having necessary information (ultracrepidarianism) responding if had expert knowledge does not. A lack awareness deliver inadequate confabulated can result incorrect medical decisions inappropriate applications (Dunning–Kruger effect). As result, certain cases, system might underperform provide results greatly overestimate any validity.

Language: Английский

---

Artificial Intelligence in Ovarian Cancer: A Systematic Review and Meta-Analysis of Predictive AI Models in Genomics, Radiomics, and Immunotherapy

AI, Journal Year: 2025, Volume and Issue: 6(4), P. 84 - 84

Published: April 18, 2025

Background/Objectives: Artificial intelligence (AI) is increasingly influencing oncological research by enabling precision medicine in ovarian cancer through enhanced prediction of therapy response and patient stratification. This systematic review meta-analysis was conducted to assess the performance AI-driven models across three key domains: genomics molecular profiling, radiomics-based imaging analysis, immunotherapy response. Methods: Relevant studies were identified a search multiple databases (2020–2025), adhering PRISMA guidelines. Results: Thirteen met inclusion criteria, involving over 10,000 patients encompassing diverse AI such as machine learning classifiers deep architectures. Pooled AUCs indicated strong predictive for genomics-based (0.78), (0.88), immunotherapy-based (0.77) models. Notably, radiogenomics-based integrating data yielded highest accuracy (AUC = 0.975), highlighting potential multi-modal approaches. Heterogeneity risk bias assessed, evidence certainty graded. Conclusions: Overall, demonstrated promise predicting therapeutic outcomes cancer, with radiomics integrated radiogenomics emerging leading strategies. Future efforts should prioritize explainability, prospective multi-center validation, integration immune spatial transcriptomic support clinical implementation individualized treatment Unlike earlier reviews, this study synthesizes broader range applications provides pooled metrics It examines methodological soundness selected highlights current gaps opportunities translation, offering comprehensive forward-looking perspective field.

Language: Английский

---

Probing the relevance of BRCA1 and BRCA2 germline pathogenic variants beyond breast and ovarian cancer

JNCI Journal of the National Cancer Institute, Journal Year: 2024, Volume and Issue: 116(12), P. 1871 - 1874

Published: Aug. 22, 2024

Language: Английский

---

Optimizing DeepHRD Interpretability for Enhanced Clinical Decision Making

Journal of Clinical Oncology, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 18, 2024

Language: Английский

---

Reply to: Optimizing DeepHRD Interpretability for Enhanced Clinical Decision Making

Journal of Clinical Oncology, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 18, 2024

Language: Английский

---

Deep Learning AI and Precision Oncology: Predicting Homologous Recombination Deficiency

Onco Zine - The International Oncology Network, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 2, 2024

Deep-learning Artificial Intelligence (AI) has reshaped cancer research as well the development of personalized clinical care. Advances in high-performance computing and novel innovative deep-learning architectures have led to a paradigm shift – dramatically affecting all aspects oncology research, including detection classification cancer, molecular characterization tumors, their microenvironment, drug discovery (and some cases repurposing old drugs or used different therapeutic areas), prediction treatment outcomes.

Language: Английский

---

Re: Deep Learning Artificial Intelligence Predicts Homologous Recombination Deficiency and Platinum Response from Histologic Slides

European Urology, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

---