SynthesisJCO precision oncology2024
Prostate Cancer Risk Stratification in NRG Oncology Phase III Randomized Trials Using Multimodal Deep Learning With Digital Histopathology.
Synthesis in JCO precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Decision-centered artificial intelligence for perioperative care outside the operating room: a practical review for surgeons.Journal of minimally invasive surgery · 2026Review
- Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.Cancers · 2026Review
- A Deep Learning-Based Multimodal Clinico-Histology-Genomic Prognostic Model in Prostate Cancer.Annals of surgical oncology · 2026Article
- Testosterone-Sparing Treatment Strategies for Biochemically Recurrent Prostate Cancer After Radical Prostatectomy.Current oncology reports · 2026Review
- AI-driven precision diagnosis and treatment of prostate cancer: a narrative review.Frontiers in oncology · 2026Review
- Artificial Intelligence Versus Conventional Methods for NCCN Risk Stratification in Localized Prostate Cancer (2020-2025): A Systematic Review.Prostate cancer · 2026Review
- The state of the art in artificial intelligence and digital pathology in prostate cancer.Nature reviews. Urology · 2026Review
- The Evolving Landscape of Novel and Old Biomarkers in Localized High-Risk Prostate Cancer: State of the Art, Clinical Utility, and Limitations Toward Precision Oncology.Journal of personalized medicine · 2025Review
- Large Language Models Could Revolutionize Health Care, but Technical Hurdles May Limit Their Applications.Journal of medical Internet research · 2025Article
- Biopsy-free radical prostatectomy: a narrative review considering rationale, limitations, and current data.Prostate international · 2025Review
- Biopsy-free radical prostatectomy for prostate cancer-modern reality or pipe dream?Translational andrology and urology · 2025Article
- Validation of a Digital Pathology-Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Article
- Discordant molecular biomarkers in unfavorable intermediate-risk localized prostate cancer.CA: a cancer journal for cliniciansArticle
- The contemporary management of prostate cancer.CA: a cancer journal for cliniciansReview
Corrections and comments
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Authors and funding
20 authors.
Funding
Abstract
purposeCurrent clinical risk stratification methods for localized prostate cancer are suboptimal, leading to over- and undertreatment. Recently, machine learning approaches using digital histopathology have shown superior prognostic ability in phase III trials. This study aims to develop a clinically usable risk grouping system using multimodal artificial intelligence (MMAI) models that outperform current National Comprehensive Cancer Network (NCCN) risk groups. MATERIALS AND
methodsThe cohort comprised 9,787 patients with localized prostate cancer from eight NRG Oncology randomized phase III trials, treated with radiation therapy, androgen deprivation therapy, and/or chemotherapy. Locked MMAI models, which used digital histopathology images and clinical data, were applied to each patient. Expert consensus on cut points defined low-, intermediate-, and high-risk groups on the basis of 10-year distant metastasis rates of 3% and 10%, respectively. The MMAI's reclassification and prognostic performance were compared with the three-tier NCCN risk groups.
resultsThe median follow-up for censored patients was 7.9 years. According to NCCN risk categories, 30.4% of patients were low-risk, 25.5% intermediate-risk, and 44.1% high-risk. The MMAI risk classification identified 43.5% of patients as low-risk, 34.6% as intermediate-risk, and 21.8% as high-risk. MMAI reclassified 1,039 (42.0%) patients initially categorized by NCCN. Despite the MMAI low-risk group being larger than the NCCN low-risk group, the 10-year metastasis risks were comparable: 1.7% (95% CI, 0.2 to 3.2) for NCCN and 3.2% (95% CI, 1.7 to 4.7) for MMAI. The overall 10-year metastasis risk for NCCN high-risk patients was 16.6%, with MMAI further stratifying this group into low-, intermediate-, and high-risk, showing metastasis rates of 3.4%, 8.2%, and 26.3%, respectively.
conclusionThe MMAI risk grouping system expands the population of men identified as having low metastatic risk and accurately pinpoints a high-risk subset with elevated metastasis rates. This approach aims to prevent both overtreatment and undertreatment in localized prostate cancer, facilitating shared decision making.
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