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Cited by 153Prostate Cancer Diagnosis and TreatmentRadiomics and Machine Learning in Medical ImagingBreast Cancer Treatment Studies

Artificial intelligence predictive model for hormone therapy use in prostate cancer

Open access
Spratt, Daniel E., Tang, Siyi, Sun, Yilun, Huang, Huei-Chung, Chen, Emmalyn, Mohamad, Osama, Armstrong, Andrew J., Tward, Jonathan D. +20 more·2023·NEJM Evidence
DOI 10.1056/evidoa2300023PMC PMC11195914
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Abstract

BACKGROUND: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life, and there remain no validated predictive models to guide its use. METHODS: We used digital pathology images from pretreatment prostate tissue and clinical data from 5727 patients enrolled in five phase 3 randomized trials, in which treatment was radiotherapy with or without ADT, as our data source to develop and validate an artificial intelligence (AI)–derived predictive patient-specific model that would determine which patients would develop the primary end point of distant metastasis. The model used baseline data to provide a binary output that a given patient will likely benefit from ADT or not. After the model was locked, validation was performed using data from NRG Oncology/Radiation Therapy Oncology Group (RTOG) 9408 (n=1594), a trial that randomly assigned men to radiotherapy plus or minus 4 months of ADT. Fine–Gray regression and restricted mean survival times were used to assess the interaction between treatment and the predictive model and within predictive model–positive, i.e., benefited from ADT, and –negative subgroup treatment effects. RESULTS: Overall, in the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis. Of these enrolled patients, 543 (34%) were model positive, and ADT significantly reduced the risk of distant metastasis compared with radiotherapy alone. Of 1051 patients who were model negative, ADT did not provide benefit. CONCLUSIONS: Our AI-based predictive model was able to identify patients with a predominantly intermediate risk for prostate cancer likely to benefit from short-term ADT. (Supported by a grant [U10CA180822] from NRG Oncology Statistical and Data Management Center, a grant [UG1CA189867] from NCI Community Oncology Research Program, a grant [U10CA180868] from NRG Oncology Operations, and a grant [U24CA196067] from NRG Specimen Bank from the National Cancer Institute and by Artera, Inc. ClinicalTrials.gov numbers NCT00767286, NCT00002597, NCT00769548, NCT00005044, and NCT00033631.)

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  • 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 [114]

  • More recent systematic review evidence supports ctDNA as a promising precision oncology biomarker while emphasizing the need for assay standardization, improved reporting practices, and validation in more diverse populations.

    “More recent systematic review evidence supports ctDNA as a promising precision oncology biomarker while emphasizing the need for assay standardization, improved reporting practices, and validation in more diverse populations [20].”

    From A decision-oriented framework for genomic testing across the prostate cancer continuum (2026) · cites this paper as [20]

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  1. Chen X, Fan H, Girshick R, He K. Improved baselines with momentum contrastive learning. March 9, 2020 (http://arxiv.org/ abs/2003.04297). preprint. · arXiv

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Authors
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., Zhang, J., Mitani, A., Simko, J. P., DeVries, S., van der Wal, D., Pinckaers, H., Monson, J. M., Campbell, H. A., Wallace, J., Ferguson, M. J., Bahary, J.-P., Schaeffer, E. M., Sandler, H. M., Tran, P. T., Rodgers, J. P., Esteva, A., Yamashita, R., & Feng, F. Y.
Affiliations
  • University Hospitals Seidman Cancer Center (US)
  • Stanford University (US)
  • Artera, Inc., Los Altos, CA
  • University of California, San Francisco (US)
  • Duke University (US)
  • University of Utah (US)
  • Dana-Farber Brigham Cancer Center (US)
  • University of Wisconsin–Madison (US)
  • Saint Agnes Medical Center (US)
  • Saint John Regional Hospital (CA)
  • University of Chicago (US)
  • Department of Radiation Oncology, Allan Blair Cancer Centre, Regina, SK, Canada
  • Centre Hospitalier de l’Université de Montréal (CA)
  • Northwestern University (US)
  • Cedars-Sinai Medical Center (US)
  • University of Maryland, Baltimore (US)
  • American College of Radiology (US)
ORCID
Venue
NEJM Evidence
Publisher
Massachusetts Medical Society
Published
2023-07-25
Volume
2
Issue
8
Pages
EVIDoa2300023-EVIDoa2300023
ISSN
2766-5526 (ISSN-L)
DOI
10.1056/evidoa2300023
PMC
PMC11195914
OpenAlex
W4382537003
Cited by
153
References
27
Citations by year
2026: 43 · 2025: 66 · 2024: 37 · 2023: 6
FWCI
27.82
Type
journal article
Language
en
Open access
Locations
  • NEJM Evidence (publishedVersion)
  • Research Square (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • PubMed Central (submittedVersion, cc-by)
Funders
  • NRG Oncology (U24CA196067, U10 CA180822, UG1CA189867, U10CA180868)
  • National Cancer Institute (UG1CA189867, U10CA180868, UG1CA233234, U54CA273956, U24CA196067, U01CA257638, U10CA180822)
Topics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Prostate Cancer Diagnosis and Treatment
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
Keywords
prostate cancer, androgen deprivation therapy, radiotherapy, prediction model, artificial intelligence, digital pathology, distant metastasis, intermediate-risk prostate cancer, treatment selection, patient-specific prediction, treatment effect heterogeneity, intermediate endpoints

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