ArticleFrontiers in oncology2022
Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Habitat-based radiomics-clinical analysis for early prediction of bladder cancer recurrence: a retrospective cohort study.Abdominal radiology (New York) · 2026Article
- CT image-derived radiomics predicts molecular subtypes in bladder urothelial carcinoma: validation of a non-invasive classification strategy.Scientific reports · 2026Article
- Perspective on the integration of radiomics and spatial omics in the analysis of the tumor microenvironment of bladder cancer and prospects for precision diagnosis and treatment.Frontiers in immunology · 2026Review
- CT-based Radiomics of Intratumoral and Peritumoral Regions to Predict the Recurrence Risk in Patients with Non-muscle-invasive Bladder Cancer within Two Years after TURBT.Current medical imaging · 2025Article
- Current role of radiomics and radiogenomics in predicting oncological outcomes in bladder cancer.Current urology · 2025Review
- Use of ultrasound imaging Omics in predicting molecular typing and assessing the risk of postoperative recurrence in breast cancer.BMC women's health · 2024Article
- Prognostic predictive value of urothelial carcinoma of the bladder after TURBT based on multiphase CT radiomics.Abdominal radiology (New York) · 2024Article
- Feasibility analysis of arterial CT radiomics model to predict the risk of local and metastatic recurrence after radical cystectomy for bladder cancer.Discover oncology · 2024Article
- Prediction of Ki-67 expression in bladder cancer based on CT radiomics nomogram.Frontiers in oncology · 2024Article
- Contrast-enhanced computed tomography-based radiomics nomogram for predicting HER2 status in urothelial bladder carcinoma.Frontiers in oncology · 2024Article
- Deep learning signature based on multiphase enhanced CT for bladder cancer recurrence prediction: a multi-center study.EClinicalMedicine · 2023Article
- Value of the application of computed tomography-based radiomics for preoperative prediction of unfavorable pathology in initial bladder cancer.Cancer medicine · 2023Article
- Radiomics for the Prediction of Overall Survival in Patients with Bladder Cancer Prior to Radical Cystectomy.Cancers · 2022Article
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8 authors.
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Abstract
Background: Predicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer. Objective: To explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer within 2 years after surgery. Methods: Patients with bladder cancer who underwent surgical treatment at the First Affiliated Hospital of Soochow University from January 2016 to December 2019 were retrospectively included and followed up to record the disease recurrence. A total of 183 patients were included in the study, and they were randomly divided into training group and validation group in a ratio of 7: 3. The three basic models which are plain scan, corticomedullary phase, and nephrographic phase as well as two combination models, namely, corticomedullary phase + nephrographic phase and plain scan + corticomedullary phase + nephrographic phase, were built with the logistic regression algorithm, and we selected the model with higher performance and calculated the Rad-score (radiomics score) of each patient. The clinical risk factors and Rad-score were screened by Cox univariate and multivariate proportional hazard models in turn to obtain the independent risk factors, then the radiomics-clinical model was constructed, and their performance was evaluated. Results: Of the 183 patients included, 128 patients constituted the training group and 55 patients constituted the validation group. In terms of the radiomics-clinical model constructed by three independent risk factors-number of tumors, tumor grade, and Rad-score-the AUCs of the training group and validation group were 0.813 (95% CI 0.740-0.886) and 0.838 (95% CI 0.733-0.943), respectively. In the validation group, the diagnostic accuracy, sensitivity, and specificity were 0.727, 0.739, and 0.719, respectively. Conclusion: Combining with radiomics based on multiphase CT images and clinical risk factors, the radiomics-clinical model constructed to predict the recurrence risk of bladder cancer within 2 years after surgery had a good performance.
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