ArticleBMC medicine2025
Unsupervised learning-based quantitative analysis of CT intratumoral subregions predicts risk stratification of bladder cancer patients.
Article in BMC medicine, 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.
- Intratumor habitat aware deep learning framework for preoperative lymph node metastasis prediction in laryngeal carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- [Innovative imaging techniques for urothelial carcinoma].Urologie (Heidelberg, Germany) · 2026Review
- An interpretable machine learning model based on CT imaging for predicting lymphovascular invasion and survival in bladder urothelial carcinoma: a multicenter study.BMC medical imaging · 2025Article
- Radiogenomic analysis of muscle-invasive bladder cancer using CT-based texture analysis.Bladder cancer (Amsterdam, Netherlands)Article
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
Abstract
backgroundPreoperative diagnosis of muscle invasion and American Joint Committee on Cancer (AJCC) stage plays a crucial role in guiding treatment strategies for bladder cancer (BCa). Utilizing quantitative analysis of tumor subregions via CT imaging holds promise in identifying high-risk populations. Developing and evaluating the performance of an unsupervised clustering algorithm-based intratumoral subregion radiomics model for distinguishing between bladder muscle invasion and AJCC stage.
methodsThis retrospective study included BCa patients who underwent CT imaging prior to transurethral resection of bladder tumor (TURBT) between January 2006 and December 2022, with all cases being histologically confirmed. Utilizing an unsupervised clustering learning method, the tumor region was classified into three intratumoral subregions, and radiomics features were extracted from both the whole tumor region and each intratumoral subregion. Following feature selection and nested cross-validation, seven predictive models were developed: clinicopathological model, whole-tumor model, intratumoral subregion 1 model, intratumoral subregion 2 model, intratumoral subregion 3 model, merged intratumoral subregion model, and fusion model. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUROC).
resultsA total of 1017 patients were included in this study, with 778 from center A (training cohort; median age, 67 years [IQR, 69-75 years]) and 239 from center B (external validation cohort; median age, 58.5 years [IQR, 66-74 years]). In the external validation cohort, the fusion model AUROC was highest for predicting muscle invasion and AJCC stage tasks, at 0.884 (95% CI, 0.842, 0.926) and 0.832 (95% CI, 0.768, 0.897), respectively. The merged intratumoral subregion model outperformed the whole-tumor model (AUROC, muscle invasion, 0.871 vs 0.804, p = 0.004; AJCC stage, 0.832 vs 0.804, p = 0.4).
conclusionsThe subregional radiomics model based on preoperative CT shows potential value in predicting muscle invasion and AJCC stage. This noninvasive risk stratification tool may offer supportive insights into surgical approaches and treatment decisions for BCa patients.
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.