A novel 3D deep learning model to automatically demonstrate renal artery segmentation and its validation in nephron-sparing surgery

作者：Shaobo Zhang, Guanyu Yang, Jian Qian, Xiaomei Zhu, Jie Li, Pu Li, Yuting He, Yi Xu, Pengfei Shao, Zengjun Wang · 发表于：Frontiers in Oncology · 年份：2022 · DOI：10.3389/fonc.2022.997911 · 被引用次数：7 · 研究领域：Renal cell carcinoma treatment、Renal and Vascular Pathologies、Pediatric Urology and Nephrology Studies

Purpose: Nephron-sparing surgery (NSS) is a mainstream treatment for localized renal tumors. Segmental renal artery clamping (SRAC) is commonly used in NSS. Automatic and precise segmentations of renal artery trees are required to improve the workflow of SRAC in NSS. In this study, we developed a tridimensional kidney perfusion (TKP) model based on deep learning technique to automatically demonstrate renal artery segmentation, and verified the precision and feasibility during laparoscopic partial nephrectomy (PN). Methods: The TKP model was established based on convolutional neural network (CNN), and the precision was validated in porcine models. From April 2018 to January 2020, TKP model was applied in laparoscopic PN in 131 patients with T1a tumors. Demographics, perioperative variables, and data from the TKP models were assessed. Indocyanine green (ICG) with near-infrared fluorescence (NIRF) imaging was applied after clamping and dice coefficient was used to evaluate the precision of the model. Results: The precision of the TKP model was validated in porcine models with the mean dice coefficient of 0.82. Laparoscopic PN was successfully performed in all cases with segmental renal artery clamping (SRAC) under TKP model's guidance. The mean operation time was 100.8 min; the median estimated blood loss was 110 ml. The ischemic regions recorded in NIRF imaging were highly consistent with the perfusion regions in the TKP models (mean dice coefficient = 0.81). Multivariate analy...