ArticleAbdominal radiology (New York)2025
End-to-end [
Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- [Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Current Applications and Future Directions of Artificial Intelligence in Prostate Cancer Diagnosis: A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
- Enhanced ISUP grade prediction in prostate cancer using multi-center radiomics data.Abdominal radiology (New York) · 2025Article
- The Role of Radiomics and Artificial Intelligence Applied to Staging PSMA PET in Assessing Prostate Cancer Aggressiveness.Journal of clinical medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo develop an end-to-end radiomics-based pipeline for the prediction of International Society of Urological Pathology grade group (ISUP GG) in prostate cancer (PCa).
methodsThis retrospective study includes 356 patients (241 in training set and 115 in independent test set) with histopathologically confirmed PCa who underwent [
resultsThe best-performing radiomics model significantly outperformed clinical model (AUC 0.879 ± 0.041 vs. 0.799 ± 0.051, bAcc 0.745 ± 0.074 vs. 0.629 ± 0.045). On an external independent test set, best-performing radiomics model perform better than clinical model, with an AUC of 0.861 vs. 0.750, p = 0.002 (Delong), and bAcc of 0.764 vs. 0.582, p = 0.043 (McNemar). The learning curve, calibration curve and DCA demonstrated goodness-of-fit and improved benefits in clinical practice.
conclusionThe end-to-end radiomics-based pipeline is an effective non-invasive tool to predict ISUP GG in PCa.
Indexed as
Identifiers
39349643What 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.