Gumuyang Zhang1, Lili Xu1, Lun Zhao2, Li Mao2, Xiuli Li2, Zhengyu Jin3, Hao Sun4. 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. 3. 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. jinzy@pumch.cn. 4. 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. sunhao_robert@126.com.
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.
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.
Authors: Lei Ye; Yuntian Chen; Hui Xu; Zhaoxiang Wang; Haixia Li; Jin Qi; Jing Wang; Jin Yao; Jiaming Liu; Bin Song Journal: Front Cell Dev Biol Date: 2022-02-25
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