Prostate Cancer Risk Stratification in NRG Oncology Phase III Randomized Trials Using Multimodal Deep Learning With Digital Histopathology.

Autor:	Tward JD; Department of Radiation Oncology, University of Utah, Salt Lake City, UT., Huang HC; Artera, Inc, Los Altos, CA., Esteva A; Artera, Inc, Los Altos, CA., Mohamad O; Department of Radiation Oncology, University of California San Francisco, San Francisco, CA., van der Wal D; Artera, Inc, Los Altos, CA., Simko JP; Department of Urology, University of California San Francisco, San Francisco, CA., DeVries S; NRG Oncology Biospecimen Bank, San Francisco, CA., Zhang J; Artera, Inc, Los Altos, CA., Joun S; Artera, Inc, Los Altos, CA., Showalter TN; Artera, Inc, Los Altos, CA.; Department of Radiation Oncology, University of Virginia School of Medicine, Charlottesville, VA., Schaeffer EM; Department of Urology, Northwestern University, Evanston, IL., Morgan TM; Division of Urologic Oncology, University of Michigan Comprehensive Cancer Center, Ann Arbor, MI., Monson JM; Department of Radiation Oncology, Saint Agnes Medical Center, Fresno, CA., Wallace JA; Ingalls Memorial Hospital, Harvey, IL., Bahary JP; Department of Radiation Oncology, CHUM-Centre Hospitalier de l'Universite de Montreal, Montreal, QC, Canada., Sandler HM; Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA., Spratt DE; Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH., Rodgers JP; NRG Oncology Statistics and Data Management Center, American College of Radiology, Philadelphia, PA., Feng FY; Department of Radiation Oncology, University of California San Francisco, San Francisco, CA., Tran PT; Department of Radiation Oncology, University of Maryland, Baltimore, MD.
Jazyk:	angličtina
Zdroj:	JCO precision oncology [JCO Precis Oncol] 2024 Oct; Vol. 8, pp. e2400145. Date of Electronic Publication: 2024 Oct 24.
DOI:	10.1200/PO.24.00145
Abstrakt:	Purpose: Current 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 Methods: The 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. Results: The 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. Conclusion: The 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.
Databáze:	MEDLINE
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