Oh N, et al. Ann Surg Treat Res. 2023 May;104(5):269-273. https://doi.org/10.4174/astr.2023.104.5.269

Background The feasibility and learning curve of laparoscopic living donor right hepatectomy was assessed. Methods Donors who underwent right hepatectomy performed by a single surgeon were reviewed. Comparisons between open and laparoscopy regarding operative outcomes, including number of bile duct openings in the graft, were performed using propensity score matching. Results From 2014 to 2018, 103 and 96 donors underwent laparoscopic and open living donor right hepatectomy respectively, of whom 64 donors from each group were matched. Mean(s.d.) duration of operation (252·2(41·9) versus 304·4(66·5) min; P < 0·001) and median duration of hospital stay (8 versus 10 days; P = 0·002) were shorter in the laparoscopy group. There was no difference in complication rates of donors (P = 0·298) or recipients (P = 0·394) between the two groups. Total time for laparoscopy decreased linearly (R² = 0·407, β = –0·914, P = 0·001), with the decrease starting after approximately 50 procedures when cases were divided into four quartiles (2nd versus 3rd quartile, P = 0·001; 3rd versus 4th quartile, P = 0·023). Although grafts with bile duct openings were more abundant in the laparoscopy group (P = 0·022), no difference was found in the last two quartiles (P = 0·207). Conclusion Laparoscopic living donor right hepatectomy is feasible and an experience of approximately 50 cases may surpass the learning curve.

Recent advancements in deep learning have facilitated significant progress in medical image analysis. However, there is lack of studies specifically addressing the needs of surgeons in terms of practicality and precision for surgical planning. Accurate understanding of anatomical structures, such as the liver and its intrahepatic structures, is crucial for preoperative planning from a surgeon’s standpoint. This study proposes a deep learning model for automatic segmentation of liver parenchyma, vascular and biliary structures, and tumor mass in hepatobiliary phase liver MRI to improve preoperative planning and enhance patient outcomes. A total of 120 adult patients who underwent liver resection due to hepatic mass and had preoperative gadoxetic acid-enhanced MRI were included in the study. A 3D residual U-Net model was developed for automatic segmentation of liver parenchyma, tumor mass, hepatic vein (HV), portal vein (PV), and bile duct (BD). The model’s performance was assessed using Dice similarity coefficient (DSC) by comparing the results with manually delineated structures. The model achieved high accuracy in segmenting liver parenchyma (DSC 0.92 ± 0.03), tumor mass (DSC 0.77 ± 0.21), hepatic vein (DSC 0.70 ± 0.05), portal vein (DSC 0.61 ± 0.03), and bile duct (DSC 0.58 ± 0.15). The study demonstrated the potential of the 3D residual U-Net model to provide a comprehensive understanding of liver anatomy and tumors for preoperative planning, potentially leading to improved surgical outcomes and increased patient safety.

Oh JH, et al. Ann Surg Treat Res. 2020 Oct;99(4):238-246. https://doi.org/10.4174/astr.2020.99.4.238

Diagnostic and Interventional Radiology