• Xiaopan Xu, Yang Liu, Xi Zhang, Qiang Tian, Yuxia Wu, Guopeng Zhang, Jiang Meng, Zengyue Yang, and Hongbing Lu.
• School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China.
• Abdom Radiol (NY). 2017 Jul 1; 42 (7): 1896-1905.

PurposeTo determine radiomic features which are capable of reflecting muscular invasiveness of bladder cancer (BC) and propose a non-invasive strategy for the differentiation of muscular invasiveness preoperatively.MethodsSixty-eight patients with clinicopathologically confirmed BC were included in this retrospective study. A total of 118 cancerous volumes of interest (VOI) were segmented from patients' T2 weighted MR images (T2WI), including 34 non-muscle invasive bladder carcinomas (NMIBCs, stage ResultsFrom each tumor VOI, a total of 63 radiomic features were derived and 30 of them showed significant inter-group differences (P ≤ 0.01). By using the SVM-based feature selection algorithm with rebalanced samples, an optimal subset including 13 radiomic signatures was determined. The area under receiver operating characteristic curve and Youden index were improved to 0.8610 and 0.7192, respectively.Conclusion3D radiomic signatures derived from T2WI and its high-order derivative maps could reflect muscular invasiveness of bladder cancer, and the proposed strategy can be used to facilitate the preoperative prediction of muscular invasiveness in patients with bladder cancer.

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