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

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

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., Nguyen, Paul L., Lang, Joshua M., Zhang, Jingbin, Mitani, Akinori, Simko, Jeffry P., DeVries, Sandy, van der Wal, Douwe, Pinckaers, Hans, Monson, Jedidiah M., Campbell, Holly A., Wallace, James, Ferguson, Michelle J., Bahary, Jean-Paul, Schaeffer, Edward M., Sandler, Howard M., Tran, Phuoc T., Rodgers, Joseph P., Esteva, Andre, Yamashita, Rikiya, Feng, Felix Y.·2023·NEJM Evidence
DOI 10.1056/evidoa2300023PMC PMC11195914
Open this paper in CoChat — free accountOpen PDF

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.)

Overview

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1056/evidoa2300023

Cites

27 of 27 references verified0 citation errors · 0 not checkable

Statistics

Verification

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.

A few free checks a day · papers already verified open instantly · results are public

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  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

Cited by

Sorted by citations

See all 153

Related

See all

Metadata

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 › Urology › 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

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