CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study

Cancer Imaging

Research Article

Feng Hou¹  Shifeng Yang²  Hexiang Wang³  Bo Wang³  Hongzheng Song³  Rui Sun³  Meng Zhang³  Pei Nie³  Na Li⁴  Yonghua Huang⁵  Chencui Huang⁶  Boyang Yu⁷ 

[1] Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China;Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China;Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China;Department of Radiology, The People’s Hospital of Zhangqiu Area, Jinan, Shandong, China;Department of Radiology, The Puyang Oilfield General Hospital, Puyang, Henan, China;Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China;Qingdao No.58 High School of Shandong Province, Qingdao, Shandong, China;

关键词: Urinary bladder neoplasms;    Computed tomography;    Deep learning;    Radiomics;    Nomogram;   

DOI  :  10.1186/s40644-023-00609-z

 received in 2023-05-27, accepted in 2023-09-10,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundTo construct and assess a computed tomography (CT)-based deep learning radiomics nomogram (DLRN) for predicting the pathological grade of bladder cancer (BCa) preoperatively.MethodsWe retrospectively enrolled 688 patients with BCa (469 in the training cohort, 219 in the external test cohort) who underwent surgical resection. We extracted handcrafted radiomics (HCR) features and deep learning (DL) features from three-phase CT images (including corticomedullary-phase [C-phase], nephrographic-phase [N-phase] and excretory-phase [E-phase]). We constructed predictive models using 11 machine learning classifiers, and we developed a DLRN by combining the radiomic signature with clinical factors. We assessed performance and clinical utility of the models with reference to the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).ResultsThe support vector machine (SVM) classifier model based on HCR and DL combined features was the best radiomic signature, with AUC values of 0.953 and 0.943 in the training cohort and the external test cohort, respectively. The AUC values of the clinical model in the training cohort and the external test cohort were 0.752 and 0.745, respectively. DLRN performed well on both data cohorts (training cohort: AUC = 0.961; external test cohort: AUC = 0.947), and outperformed the clinical model and the optimal radiomic signature.ConclusionThe proposed CT-based DLRN showed good diagnostic capability in distinguishing between high and low grade BCa.

【 授权许可】

CC BY   
© International Cancer Imaging Society (ICIS) 2023

【 图 表 】

Fig. 6

Fig. 5

Fig. 5

Fig. 4

13690_2023_1170_Article_IEq30.gif

Fig. 1