Whether bladder cancer (BCa) invades muscle is a determinant of management. However, the accuracy of preoperative diagnosis of muscle invasion is not satisfactory.
To investigate the value of multi-sequence and multi-regional magnetic resonance imaging (MRI)-based radiomics nomogram for assessing muscle invasion of BCa.
342 BCa patients, divided into a training set (239 cases), a validation set (68 cases), and a test set (35 cases).
3.0 T/T2 -weighted image, diffusion-weighted imaging, and dynamic contrast-enhanced imaging.
Patients were divided into muscle-invasive (79 cases) and non-muscle-invasive (263 cases). Two radiologists delineated the whole tumor, tumor body, and muscle layer of BCa, respectively, and extracted radiomic features.
Recursive feature elimination, Pearson correlation coefficient, logistic regression, least absolute shrinkage and selection operator (Lasso) regression analysis, and 5-fold cross-validation were used to screen features and build a radiomics model. The clinical data were collected to construct a clinical model and a radiomics-clinical nomogram.
23,688 features were extracted. After screening, the radiomics scoring model was constructed using nine radiomics features with area under curve (AUC) values of 0.933, 0.913, and 0.931 in the training, validation, and test sets, respectively. The clinical model was constructed using five clinical independent risk factors; the AUC values in the training, validation, and test set were 0.876, 0.859, and 0.824, respectively. After logistic regression analysis, the AUC values of the radiomics-clinical nomogram were made up of four clinical independent risk factors and radiomics scores were 0.955, 0.922, and 0.935 for the training, validation, and test sets, respectively. The DeLong test between clinical model and radiomics-clinical nomogram shows P < 0.001.
Multi-sequence and multi-regional MRI-based radiomics models could effectively assess the state of BCa muscular invasion. The radiomics-clinical nomogram is superior to clinical model for assessing BCa muscular invasion.
4 TECHNICAL EFFICACY: Stage 2.
Journal of magnetic resonance imaging : JMRI. 2022 Oct 27 [Epub ahead of print]
Lu Zhang, Xiaoyang Li, Li Yang, Ying Tang, Junting Guo, Ding Li, Shuo Li, Yan Li, Le Wang, Ying Lei, Hong Qiao, Guoqiang Yang, Xiaochun Wang
College of Medical Imaging, Shanxi Medical University, Taiyuan, China., Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, China.