ArticleNature cancer2025
An MRI-pathology foundation model for noninvasive diagnosis and grading of prostate cancer.
Article in Nature cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07614256 (A Multicenter Study of a Deep Learning Model Based on Spatial Registration of Multimodal Imaging and Digital Pathology for Predicting Clinically Significant Prostate Cancer), which is not on this map. Cited by 9 papers.
What it found
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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 Multicenter Study of a Deep Learning Model Based on Spatial Registration of Multimodal Imaging and Digital Pathology for Predicting Clinically Significant Prostate Cancer
Who cites it
9 citing papers in PubMed.
- Navigating Uncertainty in MRI Diagnosis: A Human-AI Collaborative Strategy for Stratifying Clinically Significant Prostate Cancer.Journal of magnetic resonance imaging : JMRI · 2026Article
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Segment any tumour: an uncertainty-aware vision foundation model for whole-body analysis.Nature communications · 2026Article
- An extrinsic enzyme-activatable fluorescent probe for high-contrast tumor imaging.Chemical science · 2026Article
- Modern integrative prostate cancer diagnostics.Current opinion in urology · 2026Review
- A Sensor-Oriented Multimodal Medical Data Acquisition and Modeling Framework for Tumor Grading and Treatment Response Analysis.Sensors (Basel, Switzerland) · 2026Article
- Development and validation of a risk prediction model for prostate cancer integrating quantitative MRI and clinical data.Frontiers in oncology · 2026Article
- Foundation Models in Cancer Pathology: Techniques, Applications, and Future Directions.Research (Washington, D.C.) · 2026Review
- Artificial intelligence for prostate cancer diagnostics.Nature cancer · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Prostate cancer is a leading health concern for men, yet current clinical assessments of tumor aggressiveness rely on invasive procedures that often lead to inconsistencies. There remains a critical need for accurate, noninvasive diagnosis and grading methods. Here we developed a foundation model trained on multiparametric magnetic resonance imaging (MRI) and paired pathology data for noninvasive diagnosis and grading of prostate cancer. Our model, MRI-based Predicted Transformer for Prostate Cancer (MRI-PTPCa), was trained under contrastive learning on nearly 1.3 million image-pathology pairs from over 5,500 patients in discovery, modeling, external and prospective cohorts. During real-world testing, prediction of MRI-PTPCa demonstrated consistency with pathology and superior performance (area under the curve above 0.978; grading accuracy 89.1%) compared with clinical measures and other prediction models. This work introduces a scalable, noninvasive approach to prostate cancer diagnosis and grading, offering a robust tool to support clinical decision-making while reducing reliance on biopsies.
Indexed as
Identifiers
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Registered trials
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.