Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach.

Bladder cancer (BCa) can be divided into muscle-invasive BCa (MIBC) and non-muscle-invasive BCa (NMIBC). Whether the tumor infiltrates the detrusor muscle is a critical determinant of disease management in patients with BCa. However, the current preoperative diagnostic accuracy of muscular invasiveness is less than satisfactory. The authors report a radiomic-clinical nomogram for the individualized preoperative differentiation of MIBC from NMIBC.

In total, 2602 radiomics features were extracted from whole bladder tumors and the basal part of the lesions on T2-weighted magnetic resonance imaging. Then, a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm in the training set (n = 130). Furthermore, a radiomic-clinical nomogram was developed incorporating the radiomics signature and selected clinical predictors based on a multivariable logistic regression analysis. The performance of the nomogram (discrimination, calibration, and clinical usefulness) was assessed and validated in an independent validation set (n = 69).

The radiomics signature, consisting of 23 selected features, showed good discrimination in the training and validation sets (area under the curve [AUC], 0.913 and 0.874, respectively). Incorporating the radiomics signature and magnetic resonance imaging-determined tumor size, the radiomic-clinical nomogram showed favorable calibration and discrimination in the training set with an AUC of 0.922, which was confirmed in the validation set (AUC, 0.876). Decision curve analysis and net reclassification improvement and integrated discrimination improvement indices (net reclassification improvement, 0.338, integrated discrimination improvement, 0.385) demonstrated the clinical usefulness of the nomogram.

The proposed noninvasive radiomic-clinical nomogram can increase the accuracy of preoperatively discriminating MIBC from NMIBC, which may aid in clinical decision making and improve patient prognosis.

Cancer. 2019 Aug 30 [Epub ahead of print]

Junjiong Zheng, Jianqiu Kong, Shaoxu Wu, Yong Li, Jinhua Cai, Hao Yu, Weibin Xie, Haide Qin, Zhuo Wu, Jian Huang, Tianxin Lin

Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China., Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China., Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.

email news signup