Prostate cancer is the most frequent cancer in men and a leading cause of cancer death. Determining a patient's optimal therapy is a challenge, where oncologists must select a therapy with the highest likelihood of success and the lowest likelihood of toxicity. International standards for prognostication rely on non-specific and semi-quantitative tools, commonly leading to over- and under-treatment. Tissue-based molecular biomarkers have attempted to address this, but most have limited validation in prospective randomized trials and expensive processing costs, posing substantial barriers to widespread adoption. There remains a significant need for accurate and scalable tools to support therapy personalization. Here we demonstrate prostate cancer therapy personalization by predicting long-term, clinically relevant outcomes using a multimodal deep learning architecture and train models using clinical data and digital histopathology from prostate biopsies. We train and validate models using five phase III randomized trials conducted across hundreds of clinical centers. Histopathological data was available for 5654 of 7764 randomized patients (71%) with a median follow-up of 11.4 years. Compared to the most common risk-stratification tool-risk groups developed by the National Cancer Center Network (NCCN)-our models have superior discriminatory performance across all endpoints, ranging from 9.2% to 14.6% relative improvement in a held-out validation set. This artificial intelligence-based tool improves prognostication over standard tools and allows oncologists to computationally predict the likeliest outcomes of specific patients to determine optimal treatment. Outfitted with digital scanners and internet access, any clinic could offer such capabilities, enabling global access to therapy personalization.
NPJ digital medicine. 2022 Jun 08*** epublish ***
Andre Esteva, Jean Feng, Douwe van der Wal, Shih-Cheng Huang, Jeffry P Simko, Sandy DeVries, Emmalyn Chen, Edward M Schaeffer, Todd M Morgan, Yilun Sun, Amirata Ghorbani, Nikhil Naik, Dhruv Nathawani, Richard Socher, Jeff M Michalski, Mack Roach, Thomas M Pisansky, Jedidiah M Monson, Farah Naz, James Wallace, Michelle J Ferguson, Jean-Paul Bahary, James Zou, Matthew Lungren, Serena Yeung, Ashley E Ross, NRG Prostate Cancer AI Consortium , Howard M Sandler, Phuoc T Tran, Daniel E Spratt, Stephanie Pugh, Felix Y Feng, Osama Mohamad
Artera, Inc, Mountain View, CA, USA. ., Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA., Artera, Inc, Mountain View, CA, USA., Department of Biomedical Data Science, Stanford University, Stanford, CA, USA., Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA., NRG Oncology Biospecimen Bank, San Francisco, CA, USA., Department of Urology, Northwestern University, Evanston, IL, USA., Division of Urologic Oncology, University of Michigan Comprehensive Cancer Center, Ann Arbor, MI, USA., Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA., Salesforce Research, San Francisco, CA, USA., Department of Radiation Oncology, Washington University School of Medicine, Saint Louis, MO, USA., Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA., Department of Radiation Oncology, cCare, Fresno, CA, USA., Department of Radiation Oncology, Horizon Health Network-Saint John Regional Hospital, Saint John, JB E2L 4L2, CA, Canada., Department of Hematology and Oncology, Ingalls Memorial Hospital, Harvey, IL, USA., Department of Radiation Oncology, Allan Blair Cancer Centre, Regina, SK S4T 7T1, CA, Canada., Department of Radiation Oncology, CHUM - Centre Hospitalier de l'Universite de Montreal, Montreal, QC H2X 3E4, CA, Canada., Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA., Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA., NRG Oncology Statistics and Data Management Center, Philadelphia, PA, USA.