ArticleEuropean urology open science2026
A Novel Radiomics-based Interpretable Model for Bladder Cancer Grade Prediction Using White-Light Cystoscopy Images.
Article in European urology open science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and objective: White-light cystoscopy (WLC) is the standard diagnostic modality for bladder cancer, but preoperative grading remains inaccurate. We developed a multichannel radiomics model to predict tumour grade (low-grade [LG] vs high-grade [HG]) from WLC and to identify imaging biomarkers. Methods: WLC images were retrospectively collected from 423 patients across two centres. A total of 2624 tumour regions were segmented for training, with 584 and 358 regions for internal and external validation, respectively. Radiomic features were extracted from the greyscale and red-green-blue channels. Feature selection was performed using coefficient thresholding and the least absolute shrinkage and selection operator. Five machine-learning classifiers were trained. Model performance was assessed using discrimination, calibration, and decision curve analysis (DCA). Interpretability was assessed using SHapley Additive exPlanations (SHAP) and feature visualisation. Key findings and limitations: The support vector machine model achieved robust performance, with an area under the receiver operating characteristic curve of 0.87 (95% confidence interval [CI] = 0.84-0.89) for internal validation and 0.79 (95% CI = 0.73-0.85) for external validation. SHAP analysis revealed distinct radiomic patterns differentiating LG from HG tumours. Limitations include retrospective design, manual segmentation, and a small, imbalanced external set, so validation reflects preliminary transportability rather than robustness or generalisability. Although calibration was acceptable and net benefit appeared at thresholds ≥ 0.30, external data constraints warrant caution. Conclusions and clinical implications: The proposed multichannel radiomics model supports grade prediction from WLC images and identifies a green channel. This approach provides a basis for developing real-time, filter-based tools for intraoperative risk stratification.
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.