Predicting muscle invasion in bladder cancer based on MRI: A comparison of radiomics, and single-task and multi-task deep learning. (May 2023)

• Record Type: Journal Article
• Authors: Li, Jianpeng; Qiu, Zhengxuan; Cao, Kangyang; Deng, Lei; Zhang, Weijing; Xie, Chuanmiao; Yang, Shuiqing; Yue, Peiyan; Zhong, Jian; Lyu, Jiegeng; Huang, Xiang; Zhang, Kunlin; Zou, Yujian; Huang, Bingsheng
• Is Part Of: Computer methods and programs in biomedicine. Volume 233(2023)
• Publication Date: 2023-05
• Subjects: Bladder cancer -- Muscle invasion -- Radiomics -- Deep learning -- Multi-task learning -- Magnetic resonance imaging; BCa Bladder cancer -- NMIBC Non-muscle-invasive bladder cancer -- MIBC Muscle-invasive bladder cancer -- TURBT Transurethral resection of the bladder tumour -- MRI Magnetic resonance imaging -- VI-RADS Vesical Imaging-Reporting and Data System -- T2WI T2-weighted imaging -- DWI Diffusion-weighted imaging -- DCE Dynamic contrast-enhanced -- DL Deep learning -- CT Computed tomography -- CNN Convolutional neural network -- LASSO Least absolute shrinkage and selection operator -- SVM Support vector machine -- ROIs Regions of interest -- Grad-CAM Gradient-weighted class activation mapping -- ROC Receiver operating characteristic -- CI Confidence interval; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2023.107466
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The radiomics, single-task and multi-task models show high diagnostic performance in predicting MIBC. Compared with the radiomics model, single and multi-task DL models have the advantage of saving time and effort. Compared with the single-task DL model, the multi-task DL model has the advantage of being more lesion-focused and more reliable for clinical reference. Abstract: Background and objectives: Radiomics and deep learning are two popular technologies used to develop computer-aided detection and diagnosis schemes for analysing medical images. This study aimed to compare the effectiveness of radiomics, single-task deep learning (DL) and multi-task DL methods in predicting muscle-invasive bladder cancer (MIBC) status based on T2-weighted imaging (T2WI). Methods: A total of 121 tumours (93 for training, from Centre 1; 28 for testing, from Centre 2) were included. MIBC was confirmed with pathological examination. A radiomics model, a single-task model, and a multi-task model based on T2WI were constructed in the training cohort with five-fold cross-validation, and validation was conducted in the external test cohort. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of each model. DeLong's test and a permutation test were used to compare the performance of the models. Results: The area under the ROC curve (AUC) values of the radiomics, single-task and multi-task models in the training cohort were: 0.920, 0.933 and 0.932, respectively; and were 0.844, 0.884 and 0.932, respectively, in the test cohort. The multi-task model achieved better performance in the test cohort than did the other models. No statistically significant differences in AUC values and Kappa coefficients were observed between pairwise models, in either the training or test cohorts. According to the Grad-CAM feature visualization results, the multi-task model focused more on the diseased tissue area in some samples of the test cohort compared with the single-task model. Conclusions: The T2WI-based radiomics, single-task, and multi-task models all exhibited good diagnostic performance in preoperatively predicting MIBC, in which the multi-task model had the best diagnostic performance. Compared with the radiomics method, our multi-task DL method had the advantage of saving time and effort. Compared with the single-task DL method, our multi-task DL method had the advantage of being more lesion-focused and more reliable for clinical reference.