Trial reportJCO precision oncology2026
Development and Validation of a Computational Histology Artificial Intelligence-Powered Biomarker in Metastatic Hormone-Sensitive Prostate Cancer on Randomized Phase III Trials.
Trial report in JCO precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
28 authors.
Funding
Abstract
purposeMetastatic hormone-sensitive prostate cancer (mHSPC) is a heterogeneous disease state with multiple treatment options. Biomarkers are needed for risk stratification and to guide treatment intensification or deintensification. We used a computational histology artificial intelligence-based platform (CHAI) to develop and validate a digital image-only prognostic biomarker in mHSPC with participant-level data from two prospective, phase III randomized controlled trials.
methodsThe CHAI platform extracted quantitative histomorphologic features from whole-slide images of hematoxylin and eosin-stained diagnostic specimens. Data from CHAARTED were used to construct a signature of features associated with overall survival (OS). A continuous risk score was dichotomized into favorable- and unfavorable-risk groups. The performance of the locked model was assessed in ENZAMET as an independent validation cohort using Kaplan-Meier methods and multivariable Cox proportional hazards models. Institutional review board approval was obtained for each participating data set. Given all patient information was deidentified, the study was considered institutional review board-exempt and consent waived.
resultsOverall, 1,191 participants were included: 507 in development (CHAARTED) and 684 in validation (ENZAMET). In the validation cohort, CHAI unfavorable-risk participants had worse OS (hazard ratio [HR], 2.6 [95% CI, 2.0 to 3.4];
conclusionWe developed and validated an image-only AI-based biomarker associated with clinical outcomes and potential benefit from treatment escalation in mHSPC independent of conventional clinicopathologic risk factors.
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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.