ArticleRadiology. Imaging cancer2025
Improving Clinically Significant Prostate Cancer Detection with a Multimodal Machine Learning Approach: A Large-Scale Multicenter Study.
Article in Radiology. Imaging cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Apparent diffusion coefficient-based single-sequence radiomics integrated with clinical variables and Prostate Imaging Reporting and Data System for predicting clinically significant prostate cancer.Translational andrology and urology · 2026Article
- The evolving PI-RADS paradigm.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Review
- Integrating multimodal clinical data with a large model for prostate cancer diagnosis.NPJ digital medicine · 2026Article
- Research integrity and data ethics in AI-driven integrated healthcare: a critical appraisal.Frontiers in public health · 2026Review
- AI-driven precision diagnosis and treatment of prostate cancer: a narrative review.Frontiers in oncology · 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
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose To develop and prospectively validate a clinical and radiologic model to predict clinically significant prostate cancer (csPCa) using biparametric MRI (bpMRI). Materials and Methods Retrospective data (acquired before March 31, 2022) from 12 medical centers were collected. Radiomic features were extracted from the whole prostate gland using segmentations generated by an automatic deep learning algorithm. A model incorporating bpMRI radiomics, age, prostate-specific antigens, the Prostate Imaging Reporting and Data System (PI-RADS), and the prostate zone lesion location was trained. A retrospective validation set and prospective data (acquired after March 31, 2022) were used to compare PI-RADS scoring (area under the receiver operating characteristic curve [AUC] and specificity at PI-RADS >3). Sensitivity analyses for sequence (T2-weighted, apparent diffusion coefficient, diffusion-weighted imaging) and scanner vendor (GE, Philips, Siemens) were performed, in addition to fairness analyses for relevant categories. Results The retrospective dataset for model development included 7157 male patients (mean age, 64.78 years; 3342 [46.7%] with csPCa), and the prospective dataset for model validation included 1629 patients (mean age, 66.19 years; 592 [36.3%] with csPCa). The multimodal model outperformed PI-RADS in the retrospective (AUC, 0.88 vs 0.80,
Indexed as
Identifiers
What OpenQuestion holds
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.