Evidence map›Paper›PMID 35676445›Full record

ArticleNPJ digital medicine2022

Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials.

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 and 23 more

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 109 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
109citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

109 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  13. Immune Spatial Organization Predicts Distant Metastasis Risk in Aggressive Localized Prostate Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026
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49 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

33 authors.

Andre EstevaArtera, Inc, Mountain View, CA, USA. aesteva@artera.ai.
Jean FengDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0003-2041-3104
Douwe van der WalArtera, Inc, Mountain View, CA, USA.
Shih-Cheng HuangDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Jeffry P SimkoDepartment of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.
Sandy DeVriesNRG Oncology Biospecimen Bank, San Francisco, CA, USA.
Emmalyn ChenArtera, Inc, Mountain View, CA, USA.
Edward M SchaefferDepartment of Urology, Northwestern University, Evanston, IL, USA.
Todd M MorganDivision of Urologic Oncology, University of Michigan Comprehensive Cancer Center, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-1140-0603
Yilun SunDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.ORCID http://orcid.org/0000-0002-1341-382X
Amirata GhorbaniSalesforce Research, San Francisco, CA, USA.
Nikhil NaikSalesforce Research, San Francisco, CA, USA.
Dhruv NathawaniSalesforce Research, San Francisco, CA, USA.ORCID http://orcid.org/0000-0002-0296-8408
Richard SocherSalesforce Research, San Francisco, CA, USA.
Jeff M MichalskiDepartment of Radiation Oncology, Washington University School of Medicine, Saint Louis, MO, USA.
Mack RoachDepartment of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.
Thomas M PisanskyDepartment of Radiation Oncology, Mayo Clinic, Rochester, MN, USA.
Jedidiah M MonsonDepartment of Radiation Oncology, cCare, Fresno, CA, USA.
Farah NazDepartment of Radiation Oncology, Horizon Health Network-Saint John Regional Hospital, Saint John, JB E2L 4L2, CA, Canada.
James WallaceDepartment of Hematology and Oncology, Ingalls Memorial Hospital, Harvey, IL, USA.
Michelle J FergusonDepartment of Radiation Oncology, Allan Blair Cancer Centre, Regina, SK S4T 7T1, CA, Canada.
Jean-Paul BaharyDepartment of Radiation Oncology, CHUM - Centre Hospitalier de l'Universite de Montreal, Montreal, QC H2X 3E4, CA, Canada.
James ZouDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-8880-4764
Matthew LungrenDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Serena YeungDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-0529-0628
Ashley E RossDepartment of Urology, Northwestern University, Evanston, IL, USA.
NRG Prostate Cancer AI Consortium
Howard M SandlerDepartment of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-8193-5769
Phuoc T TranDepartment of Radiation Oncology, University of Maryland, Baltimore, MD, USA.
Daniel E SprattDepartment of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA.
Stephanie PughNRG Oncology Statistics and Data Management Center, Philadelphia, PA, USA.
Felix Y Feng *Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.
Osama Mohamad *Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.

Funding

NRG Oncology Network Group Operations Center - GY9 BIQSFP Reports/BudgetsU10CA180868 · NCI · NRG ONCOLOGY FOUNDATION, INC. · PI NORMAN WOLMARK · 2014 to 2026
$206.8M
Statistics CoreU10CA180822 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Mei-Yin C Polley · 2014 to 2026
$146.4M
NRG Oncology NCORP Research Base-BIQSFPUG1CA189867 · NCI · NRG ONCOLOGY FOUNDATION, INC. · PI Deborah Watkins Bruner, Lisa A. Kachnic · 2014 to 2026
$140.5M
NRG Oncology Biospecimen BankU24CA196067 · NCI · NRG ONCOLOGY FOUNDATION, INC. · PI Tanner J. Freeman, Nilsa Del Carmen Ramirez Milan · 2015 to 2026
$42.6M
NCI NIH HHS U10 CA180822NCI NIH HHS U10 CA180868NCI NIH HHS U24 CA196067NCI NIH HHS UG1 CA189867
6 · The paper itself

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.

Identifiers

PMID35676445
PMCPMC9177850

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.