CT-based radiomics to predict the pathological grade of bladder cancer
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CT-based radiomics to predict the pathological grade of bladder cancer Gumuyang Zhang 1 & Lili Xu 1 & Lun Zhao 2 & Li Mao 2 & Xiuli Li 2 & Zhengyu Jin 1 & Hao Sun 1 Received: 6 February 2020 / Revised: 16 March 2020 / Accepted: 14 April 2020 # European Society of Radiology 2020
Abstract Objective To build a CT-based radiomics model to predict the pathological grade of bladder cancer (BCa) preliminarily. Methods Patients with surgically resected and pathologically confirmed BCa and who received CT urography (CTU) in our institution from October 2014 to September 2017 were retrospectively enrolled and randomly divided into training and validation groups. After feature extraction, we calculated the linear dependent coefficient between features to eliminate the collinearity. F-test was then used to identify the best features related to pathological grade. The logistic regression method was used to build the prediction model, and diagnostic performance was analyzed by plotting receiver operating characteristic (ROC) curve and calculating area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results Out of 145 included patients, 108 constituted the training group and 37 the validation group. The AUC value of the radiomics prediction model to diagnose the pathological grade of BCa was 0.950 (95% confidence interval [CI] 0.912–0.988) in the training group and 0.860 (95% CI 0.742–0.979) in the validation group, respectively. In the validation group, the diagnostic accuracy, sensitivity, specificity, PPV, and NPV were 83.8%, 88.5%, 72.7%, 88.5%, and 72.7%, respectively. Conclusions CT-based radiomics model can differentiate high-grade from low-grade BCa with a fairly good diagnostic performance. Key Points •CT-based radiomics model can predict the pathological grade of bladder cancer. •This model has good diagnostic performance to differentiate high-grade and low-grade bladder cancer. •This preoperative and non-invasive prediction method might become an important addition to biopsy. Keywords Urinary bladder neoplasms . Tomography, X-ray computed . Retrospective studies . Pattern recognition . Radiomics
Abbreviations ADC Apparent diffusion coefficient AUC Area under the curve BCa Bladder cancer Gumuyang Zhang and Lili Xu contributed equally to this work. Hao Sun is the first corresponding author and Zhengyu Jin is the second corresponding author of this work. * Zhengyu Jin [email protected] * Hao Sun [email protected] 1
Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Wangfujing Street, Dongcheng District, Beijing 100730, China
2
Deepwise AI Lab, Deepwise Inc., Haidian Avenue No. 8, Sinosteel International Plaza, Beijing 100080, China
CI CT CTTA CTU DWI GLCM GLDM GLRLM GLSZM ICC MIBC NMIBC NPV PPV ROC ROI T2WI TURBT
Confidence interval Computed tomography CT texture analysis CT urography Diffusion-weighted imaging. Gray-level co-occurrenc
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