Development and validation of a preoperative biparametric MRI-based habitat model for risk stratification of non-muscle-invasive bladder cancer.
6 affiliations
- 1Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou, Fujian, 350005, China.
- 2Department of Radiology, Binhai Campus of the First Affiliated Hospital, National Regional Medical Center, Fujian Medical University, Fuzhou, 350212, China.
- 3Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou, Fujian, 350005, China. [email protected].
- 4Department of Radiology, Binhai Campus of the First Affiliated Hospital, National Regional Medical Center, Fujian Medical University, Fuzhou, 350212, China. [email protected].
- 5Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou, Fujian, 350005, China. [email protected].
- 6Department of Radiology, Binhai Campus of the First Affiliated Hospital, National Regional Medical Center, Fujian Medical University, Fuzhou, 350212, China. [email protected].
Abstract
Background
Accurate preoperative risk stratification of non-muscle-invasive bladder cancer (NMIBC) is essential for developing individualized treatment plans. This study developed an interpretable habitat model based on biparametric MRI to improve preoperative risk stratification in patients with NMIBC.Methods
This two-center study enrolled a total of 206 patients. The internal dataset (n = 147) included the training set (n = 81), test set (n = 35), and time-sequential validation set (n = 31). The external dataset included 59 patients. The primary outcome was the dichotomized risk stratification according to the European Association of Urology (EAU) guidelines (low-intermediate risk vs. high-very high risk). K-means clustering was applied to segment tumors into habitat subregions. Following habitat feature extraction and selection, six habitat models were constructed, including support vector machine (SVM), logistic regression, linear discriminant analysis (LDA), Gaussian process classifier, random forest, and multilayer perceptron (MLP). For comparison, a VI-RADS model and a clinical prediction model were also developed. SHapley Additive exPlanations (SHAP) analysis was used to interpret the optimal model and visualize its decision-making process. Furthermore, we conducted propensity score matching (PSM) analysis to evaluate the generalizability of the habitat models.Results
All habitat models exhibited good diagnostic performance. The SVM habitat model achieved the highest AUC in both the training set (AUC = 0.95, 95% CI: 0.85-1.00) and the external validation set (AUC = 0.82, 95% CI: 0.61-1.00). It showed a trend toward better performance compared to the clinical model (AUC = 0.68, 95% CI: 0.51-0.83) and the VI-RADS model (AUC = 0.75, 95% CI: 0.58-0.92) in both sets, although the differences did not reach statistical significance. After PSM balancing all baseline covariates (all SMD < 0.1), SVM still maintained stable predictive capacity (AUC = 0.72, sensitivity = 0.769) in matched external cohort, verifying good cross-center generalization.Conclusion
Our biparametric MRI-based habitat models, particularly the SVM classifier, showed a trend toward improved preoperative risk stratification of NMIBC. These findings require large multicenter prospective validation before clinical individualized treatment decisions and follow-up planning.
Related
- Development and Internal Validation of a Preoperative Risk Stratification Model for Difficult Laparoscopic Cholecystectomy in Acute Cholecystitis
- Development and Internal Validation of the BRICS Score: A Preoperative Clinical and Hematological Model for Predicting High-Grade and Muscle-Invasive Bladder Cancer
- Core needle biopsy washout biomarkers combined with ultrasound for preoperative axillary risk stratification in breast cancer: a retrospective nomogram study with temporal validation
- Preoperative 3D morphological phenotyping of hepatocellular carcinoma via a foundation model: a non-invasive biomarker for recurrence risk stratification.
- Development and validation of machine learning models for preoperative prediction of perioperative outcomes in patients with thymic epithelial tumors
BibTeX
@article{he2026development,
title = {{Development and validation of a preoperative biparametric MRI-based habitat model for risk stratification of non-muscle-invasive bladder cancer.}},
author = {Pingping He and Ruiquan Chen and Qiaozhen Zheng and Minxuan Tian and Ming Chen and Wei Guo and Ruixiong You},
journal = {BMC medical imaging},
year = {2026},
month = aug,
doi = {10.1186/s12880-026-02630-7},
url = {https://europepmc.org/article/MED/42958093},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Development and validation of a preoperative biparametric MRI-based habitat model for risk stratification of non-muscle-invasive bladder cancer.
AU - Pingping He
AU - Ruiquan Chen
AU - Qiaozhen Zheng
AU - Minxuan Tian
AU - Ming Chen
AU - Wei Guo
AU - Ruixiong You
PY - 2026
DA - 2026/08/01
JO - BMC medical imaging
DO - 10.1186/s12880-026-02630-7
UR - https://europepmc.org/article/MED/42958093
AB - Background Accurate preoperative risk stratification of non-muscle-invasive bladder cancer (NMIBC) is essential for developing individualized treatment plans. This study developed an interpretable habitat model based on biparametric MRI to improve preoperative risk stratification in patients with NMIBC.Methods This two-center study enrolled a total of 206 patients. The internal dataset (n = 147) included the training set (n = 81), test set (n = 35), and time-sequential validation set (n = 31). The external dataset included 59 patients. The primary outcome was the dichotomized risk stratification according to the European Association of Urology (EAU) guidelines (low-intermediate risk vs. high-very high risk). K-means clustering was applied to segment tumors into habitat subregions. Following habitat feature extraction and selection, six habitat models were constructed, including support vector machine (SVM), logistic regression, linear discriminant analysis (LDA), Gaussian process classifier, random forest, and multilayer perceptron (MLP). For comparison, a VI-RADS model and a clinical prediction model were also developed. SHapley Additive exPlanations (SHAP) analysis was used to interpret the optimal model and visualize its decision-making process. Furthermore, we conducted propensity score matching (PSM) analysis to evaluate the generalizability of the habitat models.Results All habitat models exhibited good diagnostic performance. The SVM habitat model achieved the highest AUC in both the training set (AUC = 0.95, 95% CI: 0.85-1.00) and the external validation set (AUC = 0.82, 95% CI: 0.61-1.00). It showed a trend toward better performance compared to the clinical model (AUC = 0.68, 95% CI: 0.51-0.83) and the VI-RADS model (AUC = 0.75, 95% CI: 0.58-0.92) in both sets, although the differences did not reach statistical significance. After PSM balancing all baseline covariates (all SMD < 0.1), SVM still maintained stable predictive capacity (AUC = 0.72, sensitivity = 0.769) in matched external cohort, verifying good cross-center generalization.Conclusion Our biparametric MRI-based habitat models, particularly the SVM classifier, showed a trend toward improved preoperative risk stratification of NMIBC. These findings require large multicenter prospective validation before clinical individualized treatment decisions and follow-up planning.
SN - 1471-2342
ER -