Qing Xu1, QingQiang Zhu1, Hao Liu2, LuFan Chang2, ShaoFeng Duan3, WeiQiang Dou3, SaiYang Li4, Jing Ye1. 1. Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, China. 2. Yizhun Medical AI, Beijing, China. 3. GE Healthcare, Beijing, China. 4. Department of Urology, The Third Affiliated Hospital of Soochow University, Changzhou, China.
Abstract
BACKGROUND: Differentiating benign from malignant renal tumors is important for selection of the most effective treatment. PURPOSE: To develop magnetic resonance imaging (MRI)-based deep learning (DL) models for differentiation of benign and malignant renal tumors and to compare their discrimination performance with the performance of radiomics models and assessment by radiologists. STUDY TYPE: Retrospective. POPULATION: A total of 217 patients were randomly assigned to a training cohort (N = 173) or a testing cohort (N = 44). FIELD STRENGTH/SEQUENCE: Diffusion-weighted imaging (DWI) and fast spin-echo sequence T2-weighted imaging (T2WI) at 3.0T. ASSESSMENT: A radiologist manually labeled the region of interest (ROI) on each image. Three DL models using ResNet-18 architecture and three radiomics models using random forest were developed using T2WI alone, DWI alone, and a combination of the two image sets to discriminate between benign and malignant renal tumors. The diagnostic performance of two radiologists was assessed based on professional experience. We also compared the performance of each model and the radiologists. STATISTICAL TESTS: The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the performance of each model and the radiologists. P < 0.05 indicated statistical significance. RESULTS: The AUC of the DL models based on T2WI, DWI, and the combination was 0.906, 0.846, and 0.925 in the testing cohorts, respectively. The AUC of the combination DL model was significantly better than that of the models based on individual sequences (0.925 > 0.906, 0.925 > 0.846). The AUC of the radiomics models based on T2WI, DWI, and the combination was 0.824, 0.742, and 0.826 in the testing cohorts, respectively. The AUC of two radiologists was 0.724 and 0.667 in the testing cohorts. CONCLUSION: Thus, the MRI-based DL model is useful for differentiating benign from malignant renal tumors in clinic, and the DL model based on T2WI + DWI had the best performance. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.
BACKGROUND: Differentiating benign from malignant renal tumors is important for selection of the most effective treatment. PURPOSE: To develop magnetic resonance imaging (MRI)-based deep learning (DL) models for differentiation of benign and malignant renal tumors and to compare their discrimination performance with the performance of radiomics models and assessment by radiologists. STUDY TYPE: Retrospective. POPULATION: A total of 217 patients were randomly assigned to a training cohort (N = 173) or a testing cohort (N = 44). FIELD STRENGTH/SEQUENCE: Diffusion-weighted imaging (DWI) and fast spin-echo sequence T2-weighted imaging (T2WI) at 3.0T. ASSESSMENT: A radiologist manually labeled the region of interest (ROI) on each image. Three DL models using ResNet-18 architecture and three radiomics models using random forest were developed using T2WI alone, DWI alone, and a combination of the two image sets to discriminate between benign and malignant renal tumors. The diagnostic performance of two radiologists was assessed based on professional experience. We also compared the performance of each model and the radiologists. STATISTICAL TESTS: The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the performance of each model and the radiologists. P < 0.05 indicated statistical significance. RESULTS: The AUC of the DL models based on T2WI, DWI, and the combination was 0.906, 0.846, and 0.925 in the testing cohorts, respectively. The AUC of the combination DL model was significantly better than that of the models based on individual sequences (0.925 > 0.906, 0.925 > 0.846). The AUC of the radiomics models based on T2WI, DWI, and the combination was 0.824, 0.742, and 0.826 in the testing cohorts, respectively. The AUC of two radiologists was 0.724 and 0.667 in the testing cohorts. CONCLUSION: Thus, the MRI-based DL model is useful for differentiating benign from malignant renal tumors in clinic, and the DL model based on T2WI + DWI had the best performance. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.
Authors: Karl-Friedrich Kowalewski; Luisa Egen; Chanel E Fischetti; Stefano Puliatti; Gomez Rivas Juan; Mark Taratkin; Rivero Belenchon Ines; Marie Angela Sidoti Abate; Julia Mühlbauer; Frederik Wessels; Enrico Checcucci; Giovanni Cacciamani Journal: Asian J Urol Date: 2022-06-18