ArticleNEJM evidence2023
Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.
Article in NEJM evidence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 6 registered trials, which are not on this map. Cited by 74 papers, 3 of them syntheses that pooled 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.
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.
A phase iii trial of the study of endocrine therapy used as a cytoreductive and cytostatic agent prior to radiation therapy in good prognosis locally confined adenocarcinoma of the prostate
A Phase III Trial to Evaluate the Duration of Neoadjuvant Total Androgen Suppression (TAS) and Radiation Therapy (RT) in Intermediate-Risk Prostate Cancer
A Phase III Randomized Study Of High Dose 3D-CRT/IMRT Versus Standard Dose 3D-CRT/IMRT In Patients Treated For Localized Prostate Cancer
A phase iii trial of the use of long term total androgen suppression following neoadjuvant hormonal cytoreduction and radiotherapy in locally advanced carcinoma of the prostate
A phase iii trial comparing whole pelvic irradiation followed by a conedown boost to boost irradiation only and comparing neoadjuvant to adjuvant total androgen suppression (tas)
Prostate-only, Dose-escalated Radiotherapy Plus Concomitant Androgen Deprivation Therapy in Primary Localized, NCCN High Risk and MMAI Classifier Low or Intermediate-risk Prostate Cancer - a Prospective, Single-arm, Phase II Study
Who cites it
74 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Identifying and Addressing Barriers to Live Primary Prostate Cancer Cell Research in Veterans.Cancer research communications · 2026Pooled it
- The Role of Artificial Intelligence in the Characterization and Outcome Prediction of Prostate Cancer: A Systematic Review.Tomography (Ann Arbor, Mich.) · 2026Pooled it
- Association of hormone therapy with spheno-orbital meningiomas: bridging evidence and unknowns.Frontiers in oncology · 2026Pooled it
- Development and Validation of an Artificial Intelligence Digital Pathology Biomarker to Predict Benefit of Long-Term Hormonal Therapy and Radiotherapy in Men With High-Risk Prostate Cancer Across Multiple Phase III Trials.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2025Trial
- Re-examining post-operative chemoradiotherapy in head and neck cancer: an updated long-term combined analysis of RTOG 9501/EORTC 22931.Annals of oncology : official journal of the European Society for Medical Oncology · 2025Trial
- Digital Pathology-based Artificial Intelligence Biomarker Validation in Metastatic Prostate Cancer.European urology oncology · 2025Trial
- Assessing Algorithmic Fairness With a Multimodal Artificial Intelligence Model in Men of African and Non-African Origin on NRG Oncology Prostate Cancer Phase III Trials.JCO clinical cancer informatics · 2025Trial
- Present status of prostate cancer diagnosis, limitations, challenges, and future endeavors.Annals of medicine · 2026Review
- Artificial Intelligence impact on decision-making regarding short-term ADT with prostate radiotherapy: ASTuTE clinical trial interim results.Clinical and translational radiation oncology · 2026Article
- Translating machine learning predictions into meaningful risk estimates to support clinical decisions: a post hoc analysis of chronic obstructive pulmonary disease adverse outcomes using unified auto clinical scores.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 2026Article
- [Use of artificial intelligence in clinical practice and hospitals].Urologie (Heidelberg, Germany) · 2026Review
- Salvage Androgen Deprivation Therapy Use After Definitive Radiotherapy With vs Without Androgen Deprivation Therapy in Prostate Cancer.The Prostate · 2026Article
- A decision-oriented framework for genomic testing across the prostate cancer continuum.Cancer genetics · 2026Review
- Circulating Cell-Free miR-25-3p Is Upregulated in Brazilian Patients with Prostate Cancer and Contributes to Tumor Aggressiveness.Non-coding RNA · 2026Article
- Experimental Detection Methods and Clinical Translational Challenges of Disulfidptosis in Cancer.Cells · 2026Review
- Review
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Genomic Classification to Predict Survival in Metastatic Prostate Cancer: Development of Somatic Tumor Risk Assessment for Overall Survival-Prostate.JCO precision oncology · 2026Article
- Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
28 authors.
Funding
Abstract
backgroundAndrogen 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.
methodsWe 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.
resultsOverall, 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.
conclusionsOur 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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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.