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Application Value of Radiomics-Based Machine Learning for Preoperative Risk Stratification of Bladder Cancer:
Zirong He1, Yinghua Liu1, Qin Jiang1
1Department of Pediatric Surgery, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, No. 32 West Second Section, First Ring Road, Qingyang District, Chengdu, Sichuan, 610072, China, 86 18981838032.
Radiomics-based machine learning shows promise for preoperative risk stratification in bladder cancer, particularly for muscle invasion and high-grade tumors. However, methodological limitations and bias currently hinder clinical translation.
Area of Science:
- Radiology
- Oncology
- Artificial Intelligence
Background:
- Radiomics-based machine learning (ML) is being explored for bladder cancer risk stratification.
- Existing evidence for its effectiveness in detecting muscle invasion, high-grade tumors, and other factors is limited.
Purpose of the Study:
- To systematically evaluate the performance of radiomics-based ML in preoperative risk stratification for bladder cancer.
- To inform the development of intelligent risk assessment tools for bladder cancer.
Main Methods:
- Systematic retrieval of studies from Embase, Cochrane Library, PubMed, and Web of Science up to October 17, 2025.
- Assessment of risk of bias using the Prediction Model Risk of Bias Assessment Tool for Artificial Intelligence.
- Quantification of study quality using the Radiomics Quality Scoring tool and certainty of evidence using the GRADE framework.
Main Results:
- The meta-analysis included 57 studies with 11,933 participants, primarily focusing on muscle invasion and high-grade tumors.
- Pooled area under the receiver operating characteristic curve (AUROC) for muscle invasion detection ranged from 0.840 to 0.916 across different imaging modalities (CT, MRI, ultrasound).
- Combined models integrating clinical features with CT- or MRI-based radiomics showed high AUROCs (0.874-0.921) for muscle invasion and high-grade tumor detection.
Conclusions:
- This systematic review is the first to comprehensively assess radiomics for preoperative bladder cancer risk stratification.
- While promising, significant methodological shortcomings, high risk of bias, and low GRADE levels limit current clinical applicability.
- Future research should focus on standardizing methods, conducting multicenter studies, and validating external datasets.
