Quantitative Identification of Nonmuscle-Invasive and Muscle-Invasive Bladder Carcinomas: A Multiparametric MRI Radiomics Analysis

Abstract

Background: Preoperative discrimination between nonmuscle-invasive bladder carcinomas (NMIBC) and the muscle-invasive ones (MIBC) is very crucial in the management of patients with bladder cancer (BC).

Purpose: To evaluate the discriminative performance of multiparametric MRI radiomics features for precise differentiation of NMIBC from MIBC, preoperatively.

Study type: Retrospective, radiomics.

Population: Fifty-four patients with postoperative pathologically proven BC lesions (24 in NMIBC and 30 in MIBC groups) were included.

Field strength/sequence: 3.0T MRI/T₂ -weighted (T₂ W) and multi-b-value diffusion-weighted (DW) sequences.

Assessment: A total of 1104 radiomics features were extracted from carcinomatous regions of interest on T₂ W and DW images, and the apparent diffusion coefficient maps. Support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were used to construct an optimal discriminative model, and its performance was evaluated and compared with that of using visual diagnoses by experts.

Statistical tests: Chi-square test and Student's t-test were applied on clinical characteristics to analyze the significant differences between patient groups.

Results: Of the 1104 features, an optimal subset involving 19 features was selected from T₂ W and DW sequences, which outperformed the other two subsets selected from T₂ W or DW sequence in muscle invasion discrimination. The best performance for the differentiation task was achieved by the SVM-RFE+SMOTE classifier, with averaged sensitivity, specificity, accuracy, and area under the curve of receiver operating characteristic of 92.60%, 100%, 96.30%, and 0.9857, respectively, which outperformed the diagnostic accuracy by experts.

Data conclusion: The proposed radiomics approach has potential for the accurate differentiation of muscle invasion in BC, preoperatively. The optimal feature subset selected from multiparametric MR images demonstrated better performance in identifying muscle invasiveness when compared with that from T₂ W sequence or DW sequence only.

Level of evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1489-1498.

Keywords: bladder cancer; multiparametric MRI; muscle invasion prediction; support vector machine-based recursive feature elimination (SVM-RFE); synthetic minority oversampling technique (SMOTE); visual assessment.