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Bai Y, An ZC, Li F, Du LF, Xie TW, Zhang XP, Cai YY. Deep learning using contrast-enhanced ultrasound images to predict the nuclear grade of clear cell renal cell carcinoma. World J Urol 2024; 42:184. [PMID: 38512539 DOI: 10.1007/s00345-024-04889-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 02/12/2024] [Indexed: 03/23/2024] Open
Abstract
PURPOSE To assess the effectiveness of a deep learning model using contrastenhanced ultrasound (CEUS) images in distinguishing between low-grade (grade I and II) and high-grade (grade III and IV) clear cell renal cell carcinoma (ccRCC). METHODS A retrospective study was conducted using CEUS images of 177 Fuhrmangraded ccRCCs (93 low-grade and 84 high-grade) from May 2017 to December 2020. A total of 6412 CEUS images were captured from the videos and normalized for subsequent analysis. A deep learning model using the RepVGG architecture was proposed to differentiate between low-grade and high-grade ccRCC. The model's performance was evaluated based on sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Class activation mapping (CAM) was used to visualize the specific areas that contribute to the model's predictions. RESULTS For discriminating high-grade ccRCC from low-grade, the deep learning model achieved a sensitivity of 74.8%, specificity of 79.1%, accuracy of 77.0%, and an AUC of 0.852 in the test set. CONCLUSION The deep learning model based on CEUS images can accurately differentiate between low-grade and high-grade ccRCC in a non-invasive manner.
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Affiliation(s)
- Yun Bai
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85/86 Wujin Road, Shanghai, 200080, China
| | - Zi-Chen An
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85/86 Wujin Road, Shanghai, 200080, China
| | - Fan Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85/86 Wujin Road, Shanghai, 200080, China
| | - Lian-Fang Du
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85/86 Wujin Road, Shanghai, 200080, China
| | - Tian-Wu Xie
- Institute of Radiation Medicine, Fudan University, No. 2094 Xietu Road, Shanghai, 200032, China.
| | - Xi-Peng Zhang
- School of Computer Science and Technology, Taiyuan Normal University, No. 319 Daxue Street, Taiyuan, 030619, China.
| | - Ying-Yu Cai
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85/86 Wujin Road, Shanghai, 200080, China.
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Sun Z, Zhang Y, Xia Y, Ba X, Zheng Q, Liu J, Kuang X, Xie H, Gong P, Shi Y, Mao N, Wang Y, Liu M, Ran C, Wang C, Wang X, Li M, Zhang W, Fang Z, Liu W, Guo H, Ma H, Song Y. Association between CT-based adipose variables, preoperative blood biochemical indicators and pathological T stage of clear cell renal cell carcinoma. Heliyon 2024; 10:e24456. [PMID: 38268833 PMCID: PMC10803934 DOI: 10.1016/j.heliyon.2024.e24456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 01/02/2024] [Accepted: 01/09/2024] [Indexed: 01/26/2024] Open
Abstract
Background Clear cell renal cell carcinoma (ccRCC) is corelated with tumor-associated material (TAM), coagulation system and adipocyte tissue, but the relationships between them have been inconsistent. Our study aimed to explore the cut-off intervals of variables that are non-linearly related to ccRCC pathological T stage for providing clues to understand these discrepancies, and to effectively preoperative risk stratification. Methods This retrospective analysis included 218 ccRCC patients with a clear pathological T stage between January 1st, 2014, and November 30th, 2021. The patients were categorized into two cohorts based on their pathological T stage: low T stage (T1 and T2) and high T stage (T3 and T4). Abdominal and perirenal fat variables were measured based on preoperative CT images. Blood biochemical indexes from the last time before surgery were also collected. The generalized sum model was used to identify cut-off intervals for nonlinear variables. Results In specific intervals, fibrinogen levels (FIB) (2.63-4.06 g/L) and platelet (PLT) counts (>200.34 × 109/L) were significantly positively correlated with T stage, while PLT counts (<200.34 × 109/L) were significantly negatively correlated with T stage. Additionally, tumor-associated material exhibited varying degrees of positive correlation with T stage at different cut-off intervals (cut-off value: 90.556 U/mL). Conclusion Preoperative PLT, FIB and TAM are nonlinearly related to pathological T stage. This study is the first to provide specific cut-off intervals for preoperative variables that are nonlinearly related to ccRCC T stage. These intervals can aid in the risk stratification of ccRCC patients before surgery, allowing for developing a more personalized treatment planning.
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Affiliation(s)
- Zehua Sun
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Yumei Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Yuanhao Xia
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
- Department of Radiology, Binzhou Medical University, Yantai, 264000, Shandong, China
| | - Xinru Ba
- Department of Radiology, Yantaishan Hospital, Yantai, 264000, Shandong, China
| | - Qingyin Zheng
- Department of Otolaryngology-Head & Neck Surgery, Case Western Reserve University, Cleveland, OH, 44106, United States
| | - Jing Liu
- Department of Pediatrics, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Xiaojing Kuang
- School of Basic Medicine, Qingdao University, Qingdao, 266021, Shandong, China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Peiyou Gong
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Yongtao Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Ming Liu
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Chao Ran
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Chenchen Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Xiaoni Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Min Li
- Department of Radiology, Yantai Traditional Chinese Medicine Hospital, Yantai, 264000, Shandong, China
| | - Wei Zhang
- Department of Radiology, Yantai Penglai People's Hospital, Yantai, 265600, Shandong, China
| | - Zishuo Fang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610000, China
| | - Wanchen Liu
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Hao Guo
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University School of Medicine, Yantai, 264000, Shandong, China
| | - Yang Song
- Department of Nutrition and Food Hygiene, School of Public Health, College of Medicine, Qingdao University, Qingdao, 266021, Shandong, China
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