Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials
Open accessPublished version of a preprint
Abstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Compared to the unimodal imaging model, the Esteva et al. (2022) multimodal model obtained a notably higher AUC of 0.837 vs. 0.779 for predicting distant metastasis after 5 years.
“Compared to the unimodal imaging model, the multimodal obtained a notably higher AUC of 0.837 vs. 0.779 for predicting distant metastasis after 5 years.”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Esteva et al., 2022
The Results section’s ablation study says the multimodal model performed better than the pathology-images-only model for five-year distant metastasis. Our copy does not give the claimed 0.837-versus-0.779 comparison in text; its reported 0.84-versus-0.74 comparison is against NCCN risk groups, not the imaging-only model. No page number is available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredEsteva et al. (2022) describes a multimodal model that integrates digital pathology slides and clinical variables for risk stratification in prostate cancer.
“First, Esteva et al. (2022) describes a multimodal model that integrates digital pathology slides and clinical variables for risk stratification in prostate cancer.”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Esteva et al., 2022
The Esteva et al. (2022) model employs a ResNet50 pretrained in a self-supervised manner to extract a feature vector from pathology slides and concatenates this image feature vector with a clinical variable vector for final prediction.
“Specifically, the model employs a ResNet50 pretrained in a self-supervised manner to extract a feature vector from the pathology slides and subsequently concatenates this image feature vector with a clinical variable vector for the final prediction.”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Esteva et al., 2022
ArteraAI developed and tested a multimodal AI approach using histology images and clinical data to predict prostate cancer prognosis.
“AI company ArteraAI has developed and tested a multimodal AI approach (113, 114) that uses histology images and clinical data to predict prostate cancer prognosis.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [113]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21660 · Cited by 108,699 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Momentum Contrast for Unsupervised Visual Representation Learning
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347 · see citing works
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101 · see citing works
CatBoost: unbiased boosting with categorical featuresPreprint
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.09516 · Cited by 3,041 · see citing works
Improved Baselines with Momentum Contrastive LearningPreprint
Chen, X., Fan, H., Girshick, R., & He, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04297 · Cited by 2,579 · see citing works
Djavan, B., Waldert, M., Zlotta, A. R., Dobroński, P., Seitz, C., Mesut Remzi, A., BORKOWSKI, A., Schulman, C., & Marberger, M. (2001) · The Journal of Urology · doi:10.1016/s0022-5347(05)65851-x · Cited by 2,048 · see citing works
Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
Jing, L., & Tian, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2992393 · Cited by 2,036 · see citing works
Blanche, P., Dartigues, J., & Jacqmin‐Gadda, H. (2013) · Statistics in Medicine · doi:10.1002/sim.5958 · Cited by 1,883 · see citing works
Deep learning in histopathology: the path to the clinic
van der Laak, J., Litjens, G., & Ciompi, F. (2021) · Nature Medicine · doi:10.1038/s41591-021-01343-4 · Cited by 902 · see citing works
Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J., Hulsbergen-van de Kaa, C., & Litjens, G. (2020) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30739-9 · Cited by 705 · see citing works
Radiotherapy and Short-Term Androgen Deprivation for Localized Prostate Cancer
Jones, C. U., Hunt, D. L., McGowan, D. G., Amin, M. B., CHETNER, M. P., Bruner, D. W., Leibenhaut, M. H., Husain, S. M., Rotman, M. Z., Souhami, L., et al. (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1012348 · Cited by 696 · see citing works
Horwitz, E. M., Bae, K., Hanks, G. E., Porter, A. T., Grignon, D. J., Brereton, H. D., Venkatesan, V., Lawton, C. A., Rosenthal, S. A., Sandler, H. M., et al. (2008) · Journal of Clinical Oncology · doi:10.1200/jco.2007.14.9021 · Cited by 687 · see citing works
Roach, M., DeSilvio, M., Lawton, C. A., Uhl, V., Machtay, M. X., Seider, M. J., Rotman, M., Jones, C. U., Asbell, S. O., Valicenti, R. K., et al. (2003) · Journal of Clinical Oncology · doi:10.1200/jco.2003.05.004 · Cited by 634 · see citing works
Beede, E., Baylor, E., Hersch, F., Iurchenko, A., Wilcox, L., Ruamviboonsuk, P., & Vardoulakis, L. M. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376718 · Cited by 614 · see citing works
NCCN Guidelines Insights: Prostate Cancer, Version 1.2021
Schaeffer, E. M., Srinivas, S., Antonarakis, E. S., Armstrong, A. J., Bekelman, J. E., Cheng, H. H., D'Amico, A. V., Davis, B. J., Desai, N. B., Dorff, T. B., et al. (2021) · Journal of the National Comprehensive Cancer Network · doi:10.6004/jnccn.2021.0008 · Cited by 526 · see citing works
Artificial intelligence for clinical oncology
Kann, B. H., Hosny, A., & Aerts, H. J. W. L. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.04.002 · Cited by 387 · see citing works
Interobserver reproducibility of Gleason grading of prostatic carcinoma: General pathologist
Allsbrook, W. C., Mangold, K. A., Johnson, M. H., Lane, R. B., Lane, C. G., & Epstein, J. I. (2001) · Human Pathology · doi:10.1053/hupa.2001.21135 · Cited by 380 · see citing works
Michalski, J. M., Moughan, J., Purdy, J. A., Bosch, W. R., Bruner, D. W., Bahary, J.-P., Lau, H., Duclos, M., Parliament, M. B., Morton, G., et al. (2018) · JAMA Oncology · doi:10.1001/jamaoncol.2018.0039 · Cited by 357 · see citing works
Interobserver reproducibility of Gleason grading of prostatic carcinoma: Urologic pathologists
Allsbrook, W. C., Mangold, K. A., Johnson, M. H., Lane, R. B., Lane, C. G., Amin, M. B., Bostwick, D. G., Humphrey, P. A., Jones, E. C., Reuter, V. E., et al. (2001) · Human Pathology · doi:10.1053/hupa.2001.21134 · Cited by 334 · see citing works
Ward, E., Sherman, R., Henley, S. J., Jemal, A., Siegel, D. A., Feuer, E. J., Firth, A. U., Kohler, B., Scott, S., Ma, J., et al. (2019) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djz106 · Cited by 324 · see citing works
NCCN Guidelines Updates: Prostate Cancer and Prostate Cancer Early Detection
Carroll, P. R., & Mohler, J. L. (2018) · Journal of the National Comprehensive Cancer Network · doi:10.6004/jnccn.2018.0036 · Cited by 290 · see citing works
Naik, N., Madani, A., Esteva, A., Keskar, N. S., Press, M. F., Ruderman, D., Agus, D. B., & Socher, R. (2020) · Nature Communications · doi:10.1038/s41467-020-19334-3 · Cited by 253 · see citing works
Interpretable survival prediction for colorectal cancer using deep learning
Wulczyn, E., Steiner, D. F., Moran, M., Plass, M., Reihs, R., Tan, F., Flament-Auvigne, I., Brown, T., Regitnig, P., Chen, P.-H. C., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00427-2 · Cited by 231 · see citing works
Nagpal, K., Foote, D., Tan, F. E., Liu, Y., Chen, P.-H. C., Steiner, D. F., Manoj, N. S., Olson, N. H., Smith, J. L., Mohtashamian, A., et al. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2020.2485 · Cited by 223 · see citing works
Spratt, D. E., Zhang, J., Santiago‐Jiménez, M., Dess, R. T., Davis, J. W., Den, R. B., Dicker, A. P., Kane, C. J., Pollack, A. S., Stoyanova, R. S., et al. (2017) · Journal of Clinical Oncology · doi:10.1200/jco.2017.74.2940 · Cited by 211 · see citing works
Decipher Genomic Classifier Measured on Prostate Biopsy Predicts Metastasis Risk
Klein, E. A., Haddad, Z., Yousefi, K., Lam, L. L. C., Wang, Q., Choeurng, V., Palmer-Aronsten, B., Buerki, C., Davicioni, E., Li, J., et al. (2016) · Urology · doi:10.1016/j.urology.2016.01.012 · Cited by 165 · see citing works
Artificial intelligence in cancer therapy
Ho, D. (2020) · Science · doi:10.1126/science.aaz3023 · Cited by 164 · see citing works
Pisansky, T. M., Hunt, D. L., Gomella, L. G., Amin, M. B., Balogh, A. G., Chinn, D. M., Seider, M. J., Duclos, M., Rosenthal, S. A., Bauman, G. S., et al. (2014) · Journal of Clinical Oncology · doi:10.1200/jco.2014.58.0662 · Cited by 140 · see citing works
The evolving Gleason grading system.
Chen, N., & Zhou, Q. (2016) · PubMed · doi:10.3978/j.issn.1000-9604.2016.02.04 · Cited by 120 · see citing works
Analysing differences between algorithm configurations through ablation
Fawcett, C., & Hoos, H. H. (2015) · Journal of Heuristics · doi:10.1007/s10732-014-9275-9 · Cited by 114 · see citing works
Gaudreau, P.-O., Stagg, J., Soulières, D., & Fred Saad, C. (2016) · Biomarkers in Cancer · doi:10.4137/bic.s31802 · Cited by 95 · see citing works
Chen, X., Ye, G., Zhang, C., Li, X., & Shen, K. (2016) · Chinese Journal of Cancer Research · doi:10.21147/j.issn.1000-9604.2016.06.02 · Cited by 66 · see citing works
Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading
Wulczyn, E., Nagpal, K., Symonds, M., Moran, M., Plass, M., Reihs, R., Nader, F., Tan, F., Cai, Y., Brown, T., et al. (2021) · Communications Medicine · doi:10.1038/s43856-021-00005-3 · Cited by 51 · see citing works
Genomic biomarkers in prostate cancer
Kornberg, Z., Cooperberg, M. R., Spratt, D. E., & Feng, F. Y.-C. (2018) · Translational Andrology and Urology · doi:10.21037/tau.2018.06.02 · Cited by 42 · see citing works
Daskivich, T. J., Wood, L. N., Skarecky, D. W., Ahlering, T. E., & Freedland, S. J. (2016) · The Journal of Urology · doi:10.1016/j.juro.2016.08.096 · Cited by 16 · see citing works
Cancer Treatment by Caryophyllaceae-Type Cyclopeptides
Tehrani, M. H. H., Gholibeikian, M., Bamoniri, A., & Mirjalili, B. B. F. (2021) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.600856 · Cited by 15 · see citing works
Molecular Biomarkers in Localized Prostate Cancer: ASCO Guideline Summary
Eggener, S. E., Rumble, R. B., & Beltran, H. (2020) · JCO Oncology Practice · doi:10.1200/jop.19.00752 · Cited by 11 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Swanson, K., Wu, E., Zhang, A., Alizadeh, A. A., & Zou, J. (2023) · Cell · doi:10.1016/j.cell.2023.01.035 · Cited by 597 · see citing works
Huang, S.-C., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00811-0 · Cited by 479 · see citing works
“In recent years, deep learning models have demonstrated diagnostic accuracy comparable to that of human experts in narrow clinical tasks for several medical domains and imaging modalities, including chest and extremity X-rays 8 – 10 , computed tomography (CT) 11 , magnetic resonance imaging (MRI) 12 , whole slide imag…”
BackgroundMethodProstate Cancer, Version 3.2024
Schaeffer, E. M., Srinivas, S., Adra, N., An, Y., Bitting, R. L., Chapin, B. F., Cheng, H. H., D'Amico, A. V., Desai, N. B., Dorff, T. B., et al. (2024) · Journal of the National Comprehensive Cancer Network · doi:10.6004/jnccn.2024.0019 · Cited by 318 · see citing works
Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196 · see citing works
Fountzilas, E., Pearce, T. E., Baysal, M. A., Chakraborty, A., & Tsimberidou, A. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01471-y · Cited by 219 · see citing works
Artificial intelligence predictive model for hormone therapy use in prostate cancer
Spratt, D. E., Tang, S., Sun, Y., Huang, H.-C., Chen, E., Mohamad, O., Armstrong, A. J., Tward, J. D., Nguyen, P. L., Lang, J. M., et al. (2023) · NEJM Evidence · doi:10.1056/evidoa2300023 · Cited by 153 · see citing works
Multimodal Deep Learning Approaches for Precision Oncology: A Comprehensive Review
Yang, H., Yang, M., Chen, J., Yao, G., Zou, Q., & Jia, L. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae699 · Cited by 115 · see citing works
Schouten, D., Nicoletti, G., Dille, B., Chia, C., Vendittelli, P., Schuurmans, M., Litjens, G., & Khalili, N. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103621 · Cited by 108 · see citing works
Prediction of recurrence risk in endometrial cancer with multimodal deep learning
Volinsky-Fremond, S., Horeweg, N., Andani, S., Wolf, J. B., Lafarge, M. W., de Kroon, C. D., Ørtoft, G., Høgdall, E. V. S., Dijkstra, J., Jobsen, J. J., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02993-w · Cited by 106 · see citing works
Implementation of Digital Pathology and Artificial Intelligence in Routine Pathology Practice
Zhang, D. Y., Venkat, A., Khasawneh, H., Sali, R., Zhang, V., & Pei, Z. (2024) · Laboratory Investigation · doi:10.1016/j.labinv.2024.102111 · Cited by 91 · see citing works
Artificial intelligence applications in prostate cancer
Baydoun, A., Jia, A. Y., Zaorsky, N. G., Kashani, R., Rao, S. K., Shoag, J. E., Vince, R., Bittencourt, L. K., Zuhour, R. J., Price, A. T., et al. (2023) · Prostate Cancer and Prostatic Diseases · doi:10.1038/s41391-023-00684-0 · Cited by 84 · see citing works
Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture
Talaat, F. M., El–Sappagh, S., Alnowaiser, K., & Hassan, E. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02419-0 · Cited by 83 · see citing works
Machine learning in computational histopathology: Challenges and opportunities
Cooper, M. C., Ji, Z., & Krishnan, R. G. (2023) · Genes Chromosomes and Cancer · doi:10.1002/gcc.23177 · Cited by 81 · see citing works
Simon, B. D., Ozyoruk, K. B., Gelikman, D. G., Harmon, S. A., & Türkbey, B. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242631 · Cited by 73 · see citing works
Rodler, S., Kopliku, R., Ulrich, D., Kaltenhauser, A., Casuscelli, J., Eismann, L., Waidelich, R., Buchner, A., Butz, A., Cacciamani, G. E., et al. (2024) · European Urology Focus · doi:10.1016/j.euf.2023.10.020 · Cited by 65 · see citing works
Artificial intelligence-based biomarkers for treatment decisions in oncology
Ligero, M., Nahhas, O. S. M. E., Aldea, M. D., & Kather, J. N. (2025) · Trends in cancer · doi:10.1016/j.trecan.2024.12.001 · Cited by 70 · see citing works
Huang, P., Li, C., He, P., Xiao, H., Ping, Y., Feng, P., Tian, S., Chen, H., Mercaldo, F., Santone, A., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102333 · Cited by 57 · see citing works
Harnessing artificial intelligence for prostate cancer management
Zhu, L., Pan, J., Mou, W., Deng, L., Zhu, Y., Wang, Y., Pareek, G., Hyams, E. S., Carneiro, B. A., Hadfield, M. J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101506 · Cited by 56 · see citing works
Artificial intelligence in cancer: applications, challenges, and future perspectives
Cheng, H., & Shi, S.-S. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02450-3 · Cited by 60 · see citing works
Riaz, I. B., Harmon, S. A., Chen, Z., Naqvi, S. A. A., & Liang Cheng, C. (2024) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_438516 · Cited by 49 · see citing works
Cancer therapy with iRGD as a tumor-penetrating peptide
Thirumalai, A., Girigoswami, K., Pallavi, P., Harini, K., Gowtham, P., & Girigoswami, A. (2023) · Bulletin du Cancer · doi:10.1016/j.bulcan.2023.08.009 · Cited by 45 · see citing works
Liu, F., Beck, S., Yang, L., Luo, H., & Zhang, K. C. (2026) · Nature reviews. Cancer · doi:10.1038/s41568-026-00922-2 · Cited by 54 · see citing works
Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review
Rabilloud, N., Allaume, P., Acosta, O., de Crevoisier, R., Bourgade, R., Loussouarn, D., Rioux-Leclercq, N. C., Khene, Z., Mathiéu, R., Bensalah, K., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13162676 · Cited by 47 · see citing works
Developing medical imaging AI for emerging infectious diseases
Huang, S.-C., Chaudhari, A. S., Langlotz, C. P., Shah, N. H., Yeung, S. Y., & Lungren, M. P. (2022) · Nature Communications · doi:10.1038/s41467-022-34234-4 · Cited by 42 · see citing works
Towards equitable AI in oncology
Viswanathan, V. S., Parmar, V. V., & Madabhushi, A. (2024) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-024-00909-8 · Cited by 44 · see citing works
Fu, X., Sahai, E. A., & Wilkins, A. C. (2023) · The Journal of Pathology · doi:10.1002/path.6153 · Cited by 36 · see citing works
Ross, A. E., Zhang, J., Huang, H.-C., Yamashita, R., Keim-Malpass, J., Simko, J. P., DeVries, S., Morgan, T. M., Souhami, L., Dobelbower, M. C., et al. (2024) · European Urology Oncology · doi:10.1016/j.euo.2024.01.004 · Cited by 35 · see citing works
The unintended consequences of artificial intelligence in paediatric radiology
Ciet, P., Eade, C., Ho, M., Laborie, L. B., Mahomed, N., Naidoo, J., Pace, E., Segal, B., Toso, S., Tschauner, S., et al. (2023) · Pediatric Radiology · doi:10.1007/s00247-023-05746-y · Cited by 34 · see citing works
Armstrong, A. J., Liu, V. Y. T., Selvaraju, R. R., Chen, E., Simko, J., DeVries, S., Sartor, O. A., Sandler, H. M., Mohamad, O., Huang, H.-C. R., et al. (2025) · Journal of Clinical Oncology · doi:10.1200/jco.24.00365 · Cited by 34 · see citing works
Framework for the Pathology Workup of Metastatic Castration-Resistant Prostate Cancer Biopsies
Haffner, M. C., Morris, M. J., Ding, C. C., Sayar, E., Mehra, R., Robinson, B. D., True, L. D., Gleave, M. E., Lotan, T. L., Aggarwal, R. R., et al. (2024) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-24-2061 · Cited by 34 · see citing works
New approach methodologies for drug discovery
Liu, W., Pang, P. D., Wu, C. A., Tagle, D. A., & Wu, J. C. (2026) · Cell · doi:10.1016/j.cell.2026.02.012 · Cited by 32 · see citing works
Deep Learning and Multimodal Artificial Intelligence in Orthopaedic Surgery
Bozzo, A., Tsui, J. M. G., Bhatnagar, S., & Forsberg, J. A. (2024) · Journal of the American Academy of Orthopaedic Surgeons · doi:10.5435/jaaos-d-23-00831 · Cited by 33 · see citing works
Wang, Y.-R., Wang, P., Yan, Z., Zhou, Q., Güntürkün, F., Li, P., Hu, Y., Wu, W. E., Zhao, K., Zhang, M., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.06.002 · Cited by 31 · see citing works
Trends in Active Surveillance for Men With Intermediate-Risk Prostate Cancer
Diven, M. A., Tshering, L., Ma, X., Hu, J. C., Barbieri, C. E., McClure, T., & Nagar, H. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.29760 · Cited by 28 · see citing works
Artificial intelligence in pathologic diagnosis, prognosis and prediction of prostate cancer
Zhu, M. (2024) · American Journal of Clinical and Experimental Urology · doi:10.62347/jsae9732 · Cited by 27 · see citing works
Parker, C. T. A., Mendes, L. S. T., Liu, V. Y. T., Grist, E., Joun, S., Yamashita, R., Mitani, A., Chen, E., Parry, M., Sachdeva, A., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100885 · Cited by 26 · see citing works
Abdel–Wahab, M., Coleman, C. N., Eriksen, J. G., Lee, P., Kraus, R., Harsdorf, E., Lee, B., Dicker, A. P., Hahn, E., Agarwal, J. P., et al. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00038-x · Cited by 25 · see citing works
Wang, J. H., Deek, M. P., Mendes, A. A., Song, Y., Shetty, A., Bazyar, S., Van der Eecken, K., Chen, E., Showalter, T. N., Royce, T. J., et al. (2025) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2024.110618 · Cited by 24 · see citing works
Agrawal, S., & Vagha, S. J. (2024) · Cureus · doi:10.7759/cureus.66225 · Cited by 32 · see citing works
Tward, J. D., Huang, H.-C. R., Esteva, A., Mohamad, O., van der Wal, D., Simko, J., DeVries, S., Zhang, J., Joun, S., Showalter, T. N., et al. (2024) · JCO Precision Oncology · doi:10.1200/po.24.00145 · Cited by 24 · see citing works
Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides
Garberis, I., Gaury, V., Saillard, C., Drubay, D., Elgui, K., Schmauch, B., Jaeger, A., Herpin, L., Linhart, J., Sapateiro, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60824-z · Cited by 24 · see citing works
Applications of artificial intelligence in prostate cancer histopathology
Busby, D., Grauer, R., Pandav, K. B., Khosla, A., Jain, P., Menon, M., Haines, G. K., Cordon‐Cardo, C., Gorin, M. A., & Tewari, A. K. (2023) · Urologic Oncology Seminars and Original Investigations · doi:10.1016/j.urolonc.2022.12.002 · Cited by 24 · see citing works
Jiang, J., Fan, Z., Shen, J., Chen, X., Guo, H., Dong, S., & Jiang, T. (2025) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-025-07248-5 · Cited by 21 · see citing works
AI-derived Tumor Volume from Multiparametric MRI and Outcomes in Localized Prostate Cancer
Yang, D. D., Lee, L. K., Tsui, J. M. G., Leeman, J. E., McClure, H. M., Sudhyadhom, A., Guthier, C. V., Taplin, M., Trinh, Q., Mouw, K. W., et al. (2024) · Radiology · doi:10.1148/radiol.240041 · Cited by 20 · see citing works
Multimodal integration strategies for clinical application in oncology
Zhang, B., Wan, Z., Luo, Y., Zhao, X., Samayoa, J. A., Zhao, W., & Wu, S. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1609079 · Cited by 23 · see citing works
Artificial Intelligence and Pathomics
Moghadam, P. A., Bashashati, A., & Goldenberg, S. L. (2023) · Urologic Clinics of North America · doi:10.1016/j.ucl.2023.06.001 · Cited by 20 · see citing works
Integrating Artificial Intelligence and Machine Learning Into Cancer Clinical Trials
Kang, J., Chowdhry, A. K., Pugh, S. L., & Park, J. H. (2023) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2023.06.004 · Cited by 21 · see citing works
Defining the Role of Large Language Models in Urologic Care and Research
Gupta, R., Pedraza, A. M., Gorin, M. A., & Tewari, A. K. (2023) · European Urology Oncology · doi:10.1016/j.euo.2023.07.017 · Cited by 20 · see citing works
Bjartell, A. S., Krzyzanowska, A., Liu, V. Y. T., Tierney, M. K., Royce, T. J., Sjöström, M., Palominos-Rivera, M. M., Chen, E., Kraft, A. M., Esteva, A., et al. (2025) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-24-3656 · Cited by 19 · see citing works
Duwe, G., Mercier, D., Kauth, V., Moench, K., Rajashekar, V., Junker, M., Dengel, A. R., Haferkamp, A., & Höfner, T. (2025) · European Journal of Cancer · doi:10.1016/j.ejca.2025.115367 · Cited by 18 · see citing works
Digital twins for personalized treatment in uro-oncology in the era of artificial intelligence
Görtz, M., Brandl, C., Nitschke, A., Riediger, A. L., Stromer, D., Byczkowski, M., Heuveline, V., & Weidemüller, M. (2025) · Nature Reviews Urology · doi:10.1038/s41585-025-01096-6 · Cited by 21 · see citing works
Marvaso, G., Isaksson, L. J., Zaffaroni, M., Vincini, M. G., Summers, P. E., Pepa, M., Corrao, G., Mazzola, G. C., Rotondi, M., Mastroleo, F., et al. (2024) · European Radiology · doi:10.1007/s00330-024-10699-3 · Cited by 18 · see citing works
Digital Pathology–based Artificial Intelligence Biomarker Validation in Metastatic Prostate Cancer
Markowski, M. C., Ren, Y., Tierney, M. K., Royce, T. J., Yamashita, R., Croucher, D. C., Huang, H.-C. R., Todorovic, T., Chen, E., Showalter, T. N., et al. (2024) · European Urology Oncology · doi:10.1016/j.euo.2024.11.009 · Cited by 17 · see citing works
Ortiz-Pérez, D., Benavent-Lledó, M., Rodriguez-Juan, J., García‐Rodríguez, J., & Tomás, D. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114264 · Cited by 19 · see citing works
Bryant, A. K., Zamora‐Resendiz, R., Dai, X., Morrow, D., Lin, Y., Jungles, K. M., Rae, J. M., Tate, A., Pearson, A. N., Jiang, R., et al. (2024) · Cancer Medicine · doi:10.1002/cam4.7253 · Cited by 18 · see citing works
Zhou, J., Tang, J., Zuo, Y., Wan, P., Zhang, D., & Shao, W. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00971 · Cited by 15 · see citing works
Bozzo, A., Hollingsworth, A., Chatterjee, S., Apte, A., Deng, J., Sun, S., Tap, W., Aoude, A., Bhatnagar, S., & Healey, J. H. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00695-7 · Cited by 16 · see citing works
Feng, F. Y.-C., Smith, M. R., Fred Saad, C., Mobadersany, P., Tian, S. K., Yip, S. S. F., Greshock, J. D., Khan, N. S., Yu, M. K., McCarthy, S. A., et al. (2025) · JCO Precision Oncology · doi:10.1200/po-24-00653 · Cited by 15 · see citing works
Spratt, D. E., Liu, V. Y. T., Jia, A. Y., Royce, T. J., Sandler, H. M., Pugh, S. L., Tran, P. T., & Feng, F. Y.-C. (2024) · European Urology · doi:10.1016/j.eururo.2024.06.019 · Cited by 14 · see citing works
Park, H. J., Kim, E. J., & Kim, J. (2024) · International Neurourology Journal · doi:10.5213/inj.2448402.201 · Cited by 14 · see citing works
Artificial intelligence: a new era in prostate cancer diagnosis and treatment
Vidiyala, N., Parupathi, P. R., Sunkishala, P., Muppavarapu, C. S., Gujja, A., Kanagala, P., Meduri, S. K., & Nyavanandi, D. (2025) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2025.126024 · Cited by 13 · see citing works
Ten challenges and opportunities in computational immuno-oncology
Bao, R., Hutson, A. D., Madabhushi, A., Jönsson, V. D., Rosario, S. R., Barnholtz‐Sloan, J. S., Fertig, E. J., Marathe, H. G., Harris, L. N., Altreuter, J., et al. (2024) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2024-009721 · Cited by 14 · see citing works
The state of the art in artificial intelligence and digital pathology in prostate cancer
Ni, H. M., Kouzy, R., Sabbagh, A., Rooney, M. K., Feng, J., Castillo, S. P., Gadoue, S. M., Kouzi, Z. E., Hoffman, K. E., Yuan, Y., et al. (2025) · Nature Reviews Urology · doi:10.1038/s41585-025-01070-2 · Cited by 13 · see citing works
Zaorsky, N. G., Sun, Y., Nabid, A., Zapatero, A., Bolla, M., Joseph, D. J., Maingon, P., Guerrero, A., Gonzalez, A. A., Segundo, C. G. S., et al. (2025) · JAMA Oncology · doi:10.1001/jamaoncol.2025.4800 · Cited by 12 · see citing works
Wilkins, A. C., Gusterson, B. A., Tovey, H., Griffin, C. L., Stuttle, C., Daley, F. M., Corbishley, C. M., Dearnaley, D. P., Hall, E., & Somaiah, N. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104436 · Cited by 12 · see citing works
Roach, M., Zhang, J., Mohamad, O., van der Wal, D., Simko, J., DeVries, S., Huang, H.-C. R., Joun, S., Schaeffer, E. M., Morgan, T. M., et al. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-24-00284 · Cited by 14 · see citing works
The AI revolution: how multimodal intelligence will reshape the oncology ecosystem
Dellamonica, D., Ruau, D., Griffiths, B., Rossi, G., Li, B. T., Razavi, P., Maio, T. D., Gonzalez, T., Remorino, A., Reis-Filho, J. S., et al. (2025) · npj Artificial Intelligence · doi:10.1038/s44387-025-00044-4 · Cited by 14 · see citing works
Prostate Cancer Risk Stratification by Digital Histopathology and Deep Learning
Shao, Y., Bazargani, R., Karimi, D., Wang, J., Fazli, L., Goldenberg, S. L., Gleave, M. E., Black, P. C., Bashashati, A., & Salcudean, S. E. (2024) · JCO Clinical Cancer Informatics · doi:10.1200/cci.23.00184 · Cited by 12 · see citing works
Sebastian, M. M., Batra, H. V., Saini, M. L., Kearney, S. J., Sherry, L., Alexanian, S., Cohen, M., Weber, W., Lennerz, J., & Parwani, A. V. (2026) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2025.100542 · Cited by 12 · see citing works
Adachi, M., Taki, T., Kojima, M., Sakamoto, N., Matsuura, K., Hayashi, R., Tabuchi, K., Ishikawa, S., Ishii, G., & Sakashita, S. (2024) · The Journal of Pathology Clinical Research · doi:10.1002/2056-4538.12392 · Cited by 11 · see citing works
Demrozi, F., Farmanbar, M., & Engan, K. (2025) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2025.100632 · Cited by 10 · see citing works
Unveiling genetic variant-level biomarkers for aggressive prostate cancer
Taz, T. A., & Arslanturk, S. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101410 · Cited by 10 · see citing works
Gerrard, P., Zhang, J., Yamashita, R., Huang, H.-C. R., Nag, S., Nhek, S., Kish, J. B., Cole, A. I., Silberman, N., Royce, T. J., et al. (2024) · AI in Precision Oncology · doi:10.1089/aipo.2024.0004 · Cited by 10 · see citing works
Wegener, E. E., Ng, M., Guerrieri, M., Showalter, T. N., de Leon, J., Ramani, S., Dreosti, M., Lim, T. S., Wong, B., Chao, M., et al. (2025) · BMC Cancer · doi:10.1186/s12885-025-13622-1 · Cited by 10 · see citing works
Adem, A. M., Kant, R., Sonia, S., Kumar, K., Mittal, V., Jain, P., & Joshi, K. K. (2025) · Biomedical & Pharmacology Journal · doi:10.13005/bpj/3073 · Cited by 9 · see citing works
Computer-assisted diagnosis to improve diagnostic pathology: A review
Caputo, A., Maffei, E., Gupta, N., Cima, L., Merolla, F., Cazzaniga, G., Pepe, P., Verze, P., & Fraggetta, F. (2025) · Indian Journal of Pathology and Microbiology · doi:10.4103/ijpm.ijpm_339_24 · Cited by 9 · see citing works
He, D., Zhuang, H., Ma, Y., Xia, B., Chatterjee, A., Fan, X., Qi, S., Qian, W., Zhang, Z., & Liu, J. (2025) · Cancers · doi:10.3390/cancers17091556 · Cited by 9 · see citing works
Simon, B., Harmon, S. A., Merriman, K. M., Tetreault, J., Esengür, Ö. T., Stecko, H., Yılmaz, E. C., Clifton, L., Thakur, A., Blake, Z., et al. (2025) · Clinical Imaging · doi:10.1016/j.clinimag.2025.110579 · Cited by 9 · see citing works
Makhlouf, H. R., Ossandon, M. R., Farahani, K., Lubensky, I. A., & Harris, L. N. (2025) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2025.100531 · Cited by 9 · see citing works
Machine Learning & Molecular Radiation Tumor Biomarkers
Rydzewski, N. R., Helzer, K. T., Bootsma, M. L., Shi, Y., Bakhtiar, H., Sjöström, M., & Zhao, S. G. (2023) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2023.03.002 · Cited by 9 · see citing works
Crawford, E. D., Bryce, A. H., Hussain, M., Agarwal, N., Beltran, H., Cooperberg, M. R., Petrylak, D. P., Shore, N. D., Spratt, D. E., Tagawa, S. T., et al. (2024) · JU Open Plus · doi:10.1097/ju9.0000000000000137 · Cited by 9 · see citing works
Development of a deep learning system for predicting biochemical recurrence in prostate cancer
Cao, L., He, R., Zhang, A., Li, L., Cao, W., Liu, N., & Zhang, P. (2025) · BMC Cancer · doi:10.1186/s12885-025-13628-9 · Cited by 9 · see citing works
Artificial Intelligence in Cancer Immunotherapy: Navigating Challenges and Unlocking Opportunities
Xiang, W., Yu, L., Chen, X. S., & Herold, M. J. (2024) · Engineering · doi:10.1016/j.eng.2024.12.014 · Cited by 8 · see citing works
Artificial intelligence-assisted prostate cancer diagnosis for reduced use of immunohistochemistry
Blilie, A., Mulliqi, N., Ji, X., Szolnoky, K., Boman, S. E., Titus, M., Gonzalez, G. M., Asenjo, J., Gambacorta, M., Libretti, P., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-01185-y · Cited by 9 · see citing works
Cai, X., Peng, C.-R., Ding, C.-Y., Cui, Y., Gao, L., Xu, Z., Li, L., Zhang, J., & Li, T. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01125-y · Cited by 9 · see citing works
Morgan, T. M., Ren, Y., Tang, S., Zwerink, W., Chen, E., Mitani, A., Huang, H.-C. R., Simko, J., DeVries, S., Pollack, A. S., et al. (2025) · European Urology · doi:10.1016/j.eururo.2025.12.007 · Cited by 8 · see citing works
Keim‐Malpass, J., & Kausch, S. L. (2023) · Seminars in Oncology Nursing · doi:10.1016/j.soncn.2023.151432 · Cited by 8 · see citing works
Manzoor, F., Gupta, V., Pinky, L. Y., Wang, Z., Chen, Z., Deng, Y., & Neupane, S. (2025) · medRxiv · doi:10.1101/2025.08.07.25333235 · Cited by 8 · see citing works
MMIST-ccRCC: A Real World Medical Dataset for the Development of Multi-Modal Systems
Mota, T., Verdelho, M. R., Araújo, D. J., Bissoto, A. E., Santiago, C., & Barata, C. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00246 · Cited by 7 · see citing works
Fay, M., Liao, R. S., Lone, Z. M., Reddy, C. A., Hassan, M., Xie, C., Jain, P., Huang, W., Basu, H. S., Nair, S. S., et al. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-24-00292 · Cited by 7 · see citing works
Cha, E., Lin, Z., Lu, J., Oliveira, L. D., Erak, E., Mendes, A. A. D. A., Dairo, O., Ertunç, O., Kulaç, İ., Valle, J. A. B.-D., et al. (2025) · European Urology · doi:10.1016/j.eururo.2025.08.018 · Cited by 7 · see citing works
Artificial intelligence in genitourinary pathology
Patel, A., Parwani, A. V., & Satturwar, S. P. (2025) · Histopathology · doi:10.1111/his.70020 · Cited by 10 · see citing works
Nour, S. M., Shehab, R. S., Said, S. A., & Halim, I. T. A. (2025) · Applied System Innovation · doi:10.3390/asi8010007 · Cited by 7 · see citing works
Artificial Intelligence, Machine Learning and Big Data in Radiation Oncology
Zhu, S., Ma, S. J., Farag, A. A., Huerta, T. R., Gamez, M. E., & Blakaj, D. M. (2025) · Hematology/Oncology Clinics of North America · doi:10.1016/j.hoc.2024.12.002 · Cited by 7 · see citing works
Somara, S., Joshi, A. M., Mitra, K., Desai, S. S., Lundberg, M. S., Bhasin, S., & Hunsberger, J. G. (2025) · Current Stem Cell Reports · doi:10.1007/s40778-025-00249-y · Cited by 8 · see citing works
Rizi, F. R., Jamadi, M. S., Rahimi, S., Bighamian, M., Paidar, P., Taki, M. J., & Bahmaie, N. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1763359 · Cited by 7 · see citing works
Arunachalam, M., Chiranjeev, C., Mondal, B., & Sanjay, T. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-3731-8.ch017 · Cited by 6 · see citing works
Zhang, D., Svensson, M., Edén, P., & Dihge, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-78040-y · Cited by 6 · see citing works
Improved Multimodal Fusion for Small Datasets with Auxiliary Supervision
Holste, G., van der Wal, D., Pinckaers, H., Yamashita, R., Mitani, A., & Esteva, A. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230356 · Cited by 6 · see citing works
Dykstra, M. P., Regan, S. N., Yin, H., McLaughlin, P. W., Boike, T. P., Bhatt, A. K., Zaki, M., Kendrick, D., Mislmani, M., Paluch, S., et al. (2024) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2024.05.026 · Cited by 6 · see citing works
- Authors
- Esteva, A., Feng, J., van der Wal, D., Huang, S.-C., Simko, J. P., DeVries, S., Chen, E., Schaeffer, E. M., Morgan, T. M., Sun, Y., Ghorbani, A., Naik, N., Nathawani, D., Socher, R., Michalski, J. M., Roach, M., Pisansky, T. M., Monson, J. M., Naz, F., Wallace, J., Ferguson, M. J., Bahary, J.-P., Zou, J., Lungren, M., Yeung, S., Ross, A. E., NRG Prostate Cancer AI Consortium, Kucharczyk, M., Souhami, L., Ballas, L., Peters, C. A., Liu, S., Balogh, A. G., Randolph-Jackson, P. D., Schwartz, D. L., Girvigian, M. R., Saito, N. G., Raben, A., Rabinovitch, R. A., Katato, K., Sandler, H. M., Tran, P. T., Spratt, D. E., Pugh, S., Feng, F. Y., & Mohamad, O.
- Affiliations
- University of California, San Francisco (US)
- Stanford University (US)
- NRG Oncology (US)
- Northwestern University (US)
- University of Michigan (US)
- Case Western Reserve University (US)
- Salesforce (United States) (US)
- Washington University in St. Louis (US)
- Mayo Clinic (US)
- California Cancer Associates for Research and Excellence (US)
- Saint John Regional Hospital (CA)
- Ingalls Memorial Hospital (US)
- Centre Hospitalier de l’Université de Montréal (CA)
- Nova Scotia Cancer Centre (CA)
- University of Southern California (US)
- Northeast Radiation Oncology Center (US)
- Ronald Reagan UCLA Medical Center (US)
- VA NY Harbor Healthcare System (US)
- Kaiser Permanente (US)
- Indiana University School of Medicine (US)
- Christiana Care Health System (US)
- University of Colorado Hospital (US)
- Hurley Medical Center (US)
- Cedars-Sinai Medical Center (US)
- University of Maryland, Baltimore (US)
- University Hospitals Seidman Cancer Center (US)
- Corresponding author
- Esteva, Andre
- ORCID
- Esteva, Andre
- Feng, Jean
- Huang, Shih-Cheng
- Chen, Emmalyn
- Schaeffer, Edward M.
- Morgan, Todd M.
- Sun, Yilun
- Ghorbani, Amirata
- Naik, Nikhil
- Nathawani, Dhruv
- Socher, Richard
- Michalski, Jeff M.
- Roach, Mack
- Pisansky, Thomas M.
- Naz, Farah
- Wallace, James
- Bahary, Jean-Paul
- Zou, James
- Lungren, Matthew
- Yeung, Serena
- Ross, Ashley E.
- Kucharczyk, Michael
- Ballas, Leslie
- Liu, Sandy
- Schwartz, David L.
- Girvigian, Michael R.
- Saito, Naoyuki G.
- Raben, Adam
- Rabinovitch, Rachel A.
- Sandler, Howard M.
- Tran, Phuoc T.
- Spratt, Daniel E.
- Pugh, Stephanie
- Feng, Felix Y.
- Mohamad, Osama
- Venue
- npj Digital Medicine
- Published
- 2022-06-08
- Volume
- 5
- Issue
- 1
- Pages
- 71-71
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC9177850
- OpenAlex
- W4282942659
- Cited by
- 217
- References
- 39
- Citations by year
- 2026: 58 · 2025: 85 · 2024: 44 · 2023: 26 · 2022: 4
- FWCI
- 22.55
- Type
- journal article
- Language
- en
- Update notices
- correction (2023-02-22)
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Commons@Becker (Washington University School of Medicine) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- NRG Oncology (U24CA196067, U10 CA180822, UG1CA189867, U10CA180868)
- National Cancer Institute (UG1CA189867, U10CA180868, U24CA196067, U10CA180822)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Prostate Cancer Diagnosis and Treatment
- Keywords
- prostate cancer, multimodal deep learning, digital histopathology, prostate biopsy, outcome prediction, risk stratification, NCCN risk classification, long-term clinical outcomes, treatment selection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
