Radiomics nomogram for preoperative prediction of progression-free survival using diffusion-weighted imaging in patients with muscle-invasive bladder cancer

Bladder Cancer

Eur J Radiol. 2020 Aug 26;131:109219. doi: 10.1016/j.ejrad.2020.109219. Online ahead of print.


PURPOSE: To develop a radiomics signature using diffusion-weighted imaging (DWI) for predicting progression-free survival (PFS) in muscle-invasive bladder cancer (MIBC) patients and to assess its incremental value over traditional staging system.

METHOD: 210 MIBC patients undergoing preoperative DWI were enrolled. A radiomics signature was built using LASSO model. A radiomics nomogram was generated to assess the incremental value of the radiomics signature over existing risk factors in PFS estimation in terms of calibration, discrimination, reclassification and clinical usefulness. Kaplan-Meier analysis was used to assess the association of the radiomics signature with PFS. C-index was used as a discrimination measure. Net reclassification improvement (NRI) was calculated to evaluate the usefulness improvement added by the radiomics signature. Decision curve analysis was performed to evaluate the clinical usefulness of the nomograms.

RESULTS: The radiomics signature was significantly associated with PFS (log-rank P = 0.0073) and was independent with clinicopathological factors (P = 0.0004). The radiomics nomogram achieved better performance in PFS prediction (C-index: 0.702, 95 % confidence interval [CI]: 0.602, 0.802) than either clinicopathological nomogram (C-index: 0.682, 95 % CI: 0.575, 0.788) or radiomics signature (C-index: 0.612, 95 % CI: 0.493, 0.731), and achieved better calibration and classification (NRI: 0.226, 95 % CI: 0.016, 0.415, P = 0.038). Decision curve analysis demonstrated the better clinical usefulness of the radiomics nomogram.

CONCLUSIONS: The DWI-based radiomics signature was an independent predictor of PFS in MIBC patients. Combining the radiomics signature, clinical staging and other clinicopathological factors achieved better performance in individual PFS prediction.