Huanjun Wang1, Daokun Hu2,3, Haohua Yao4, Maodong Chen2,3, Shurong Li1, Haolin Chen2,3, Junhang Luo4, Yanqiu Feng5,6, Yan Guo7. 1. Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China. 2. School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, People's Republic of China. 3. Key Laboratory of Mental Health of the Ministry of Education, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, People's Republic of China. 4. Department of Urology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China. 5. School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, People's Republic of China. foree@fimmu.com. 6. Key Laboratory of Mental Health of the Ministry of Education, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, People's Republic of China. foree@fimmu.com. 7. Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China. dr.guoyan@163.com.
Abstract
OBJECTIVES: To develop and validate an MRI-based radiomics strategy for the preoperative estimation of pathological grade in bladder cancer (BCa) tumors. METHODS: A primary cohort of 70 patients (31 high-grade BCa and 39 low-grade BCa) with BCa were retrospectively enrolled. Three sets of radiomics features were separately extracted from tumor volumes on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. Two sets of multimodal features were separately generated by the maxout and concatenation of the above mentioned single-modality features. Each feature set was subjected to a two-sample t test and the least absolute shrinkage and selection operator (LASSO) algorithm for feature selection. Multivariable logistic regression (LR) analysis was used to obtain five corresponding radiomics models. The diagnostic abilities of the radiomics models were evaluated using receiver operating characteristic (ROC) curve analysis and compared using the DeLong test. Validation was performed on a time-independent cohort containing 30 consecutive patients. RESULTS: The areas under the ROC curves (AUCs) of single-modality T2WI, DWI, and ADC models in the training cohort were 0.7933 (95% confidence interval [CI] 0.7471-0.8396), 0.8083 (95% CI 0.7565-0.8601), and 0.8350 (95% CI 0.7924-0.8776), respectively. Both multimodality models achieved higher AUCs (maxout 0.9233, 95% CI 0.9001-0.9466; concatenation 0.9233, 95% CI 0.9001-0.9466) than single-modality models. The AUCs of the maxout and concatenation models in the validation cohort were 0.9186 and 0.9276, respectively. CONCLUSIONS: The MRI-based multiparametric radiomics approach has the potential to be used as a noninvasive imaging tool for preoperative grading of BCa tumors. Multicenter validation is needed to acquire high-level evidence for its clinical application. KEY POINTS: • Multiparametric MRI may help in the preoperative grading of BCa tumors. • The Joint_Model established from T2WI, DWI, and ADC feature subsets demonstrated a high diagnostic accuracy for preoperative prediction of pathological grade in BCa tumors. • The radiomics approach has the potential to preoperatively assess tumor grades in BCa and avoid subjectivity.
OBJECTIVES: To develop and validate an MRI-based radiomics strategy for the preoperative estimation of pathological grade in bladder cancer (BCa) tumors. METHODS: A primary cohort of 70 patients (31 high-grade BCa and 39 low-grade BCa) with BCa were retrospectively enrolled. Three sets of radiomics features were separately extracted from tumor volumes on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. Two sets of multimodal features were separately generated by the maxout and concatenation of the above mentioned single-modality features. Each feature set was subjected to a two-sample t test and the least absolute shrinkage and selection operator (LASSO) algorithm for feature selection. Multivariable logistic regression (LR) analysis was used to obtain five corresponding radiomics models. The diagnostic abilities of the radiomics models were evaluated using receiver operating characteristic (ROC) curve analysis and compared using the DeLong test. Validation was performed on a time-independent cohort containing 30 consecutive patients. RESULTS: The areas under the ROC curves (AUCs) of single-modality T2WI, DWI, and ADC models in the training cohort were 0.7933 (95% confidence interval [CI] 0.7471-0.8396), 0.8083 (95% CI 0.7565-0.8601), and 0.8350 (95% CI 0.7924-0.8776), respectively. Both multimodality models achieved higher AUCs (maxout 0.9233, 95% CI 0.9001-0.9466; concatenation 0.9233, 95% CI 0.9001-0.9466) than single-modality models. The AUCs of the maxout and concatenation models in the validation cohort were 0.9186 and 0.9276, respectively. CONCLUSIONS: The MRI-based multiparametric radiomics approach has the potential to be used as a noninvasive imaging tool for preoperative grading of BCa tumors. Multicenter validation is needed to acquire high-level evidence for its clinical application. KEY POINTS: • Multiparametric MRI may help in the preoperative grading of BCa tumors. • The Joint_Model established from T2WI, DWI, and ADC feature subsets demonstrated a high diagnostic accuracy for preoperative prediction of pathological grade in BCa tumors. • The radiomics approach has the potential to preoperatively assess tumor grades in BCa and avoid subjectivity.
Entities:
Keywords:
Magnetic resonance imaging; ROC curve; Regression analysis; Urinary bladder
Authors: Xiaopan Xu; Huanjun Wang; Yan Guo; Xi Zhang; Baojuan Li; Peng Du; Yang Liu; Hongbing Lu Journal: Front Oncol Date: 2021-07-15 Impact factor: 6.244