Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer.
Zongtai Zheng, Zhuoran Gu, Feijia Xu, Niraj Maskey, Yanyan He, Yang Yan, Tianyuan Xu, Shenghua Liu, Xudong Yao
Author Information
Zongtai Zheng: Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
Zhuoran Gu: Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
Feijia Xu: Department of Radiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Niraj Maskey: Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
Yanyan He: Department of Pathology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Yang Yan: Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
Tianyuan Xu: Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
Shenghua Liu: Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China. drfelixliu@163.com.
Xudong Yao: Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China. yaoxudong1967@163.com.
PURPOSE: The Ki67 expression is associated with the advanced clinicopathological features and poor prognosis in bladder cancer (BCa). We aimed to develop and validate magnetic resonance imaging (MRI)-based radiomics signatures to preoperatively predict the Ki67 expression status in BCa. METHODS AND MATERIALS: We retrospectively collected 179 BCa patients with Ki67 expression and preoperative MRI. Radiomics features were extracted from T2-weighted (T2WI) and dynamic contrast-enhancement (DCE) images. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (low Ki67 expression group) in the training set. Minimum redundancy maximum relevance was used to identify the best features associated with Ki67 expression. Support vector machine and Least Absolute Shrinkage and Selection Operator algorithms (LASSO) were used to construct radiomics signatures in training and SMOTE-training sets, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. The decision curve analyses (DCA) and calibration curve and were used to investigate the clinical usefulness and calibration of radiomics signatures, respectively. The Kaplan-Meier test was performed to investigate the prognostic value of radiomics-predicted Ki67 expression status. RESULTS: 1218 radiomics features were extracted from T2WI and DCE images, respectively. The SMOTE-LASSO model based on nine features achieved the best predictive performance in the SMOTE-training (AUC, 0.859; accuracy, 80.3%) and validation sets (AUC, 0.819; accuracy, 81.5%) with a good calibration performance and clinical usefulness. Immunohistochemistry-based high Ki67 expression and radiomics-predicted high Ki67 expression based on the SMOTE-LASSO model were significantly associated with poor disease-free survival in training and validation sets (all P < 0.05). CONCLUSIONS: The SMOTE-LASSO model could predict the Ki67 expression status and was associated with survival outcomes of the BCa patients, thereby may aid in clinical decision-making.
