Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions

作者全名:"He, Quan-Hao; Feng, Jia-Jun; Lv, Fa-Jin; Jiang, Qing; Xiao, Ming-Zhao"

作者地址:"[He, Quan-Hao; Xiao, Ming-Zhao] Chongqing Med Univ, Dept Urol, Affiliated Hosp 1, Chongqing 400016, Peoples R China; [Feng, Jia-Jun] South China Univ Technol, Guangzhou Peoples Hosp 1, Sch Med, Dept Med Imaging, 51000, Guangzhou, Peoples R China; [Lv, Fa-Jin] Chongqing Med Univ, Dept Radiol, Affiliated Hosp 1, Chongqing 400016, Peoples R China; [Jiang, Qing] Chongqing Med Univ, Dept Urol, Affiliated Hosp 2, Chongqing 400010, Peoples R China"

通信作者:"Xiao, MZ (通讯作者),Chongqing Med Univ, Dept Urol, Affiliated Hosp 1, Chongqing 400016, Peoples R China."












关键词:Machine learning; Bosniak-2019 classification; Cystic renal lesions; Radiomics

摘要:"Background The rising prevalence of cystic renal lesions (CRLs) detected by computed tomography necessitates better identification of the malignant cystic renal neoplasms since a significant majority of CRLs are benign renal cysts. Using arterial phase CT scans combined with pathology diagnosis results, a fusion feature-based blending ensemble machine learning model was created to identify malignant renal neoplasms from cystic renal lesions (CRLs). Histopathology results were adopted as diagnosis standard. Pretrained 3D-ResNet50 network was selected for non-handcrafted features extraction and pyradiomics toolbox was selected for handcrafted features extraction. Tenfold cross validated least absolute shrinkage and selection operator regression methods were selected to identify the most discriminative candidate features in the development cohort. Feature's reproducibility was evaluated by intra-class correlation coefficients and inter-class correlation coefficients. Pearson correlation coefficients for normal distribution and Spearman's rank correlation coefficients for non-normal distribution were utilized to remove redundant features. After that, a blending ensemble machine learning model were developed in training cohort. Area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA) were employed to evaluate the performance of the final model in testing cohort. Results The fusion feature-based machine learning algorithm demonstrated excellent diagnostic performance in external validation dataset (AUC = 0.934, ACC = 0.905). Net benefits presented by DCA are higher than Bosniak-2019 version classification for stratifying patients with CRL to the appropriate surgery procedure. Conclusions Fusion feature-based classifier accurately distinguished malignant and benign CRLs which outperformed the Bosniak-2019 version classification and illustrated improved clinical decision-making utility."

基金机构:National Key Research and Development Project [2020YFC2005900]

基金资助正文:"This study Supported by the National Key Research and Development Project, No. 2020YFC2005900"