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Background: Laparoscopic left hemihepatectomy (LLH) has been shown to be an effective and safe method for treating hepatolithiasis primarily affecting the left hemiliver. However, this procedure still presents challenges. Due to pathological changes in intrahepatic duct stones, safely dissecting the hilar vessels and determining precise resection boundaries remains difficult, even with fluorescent imaging. Our team proposed a new method of augmented reality navigation (ARN) combined with Indocyanine green (ICG) fluorescence imaging for LLH in hepatolithiasis cases. This study aimed to investigate the feasibility of this combined approach in the procedure. Methods: Between May 2021 and September 2023, 16 patients with hepatolithiasis who underwent LLH were included. All patients underwent preoperative 3D evaluation and were then guided using ARN and ICG fluorescence imaging during the procedure. Perioperative and short-term postoperative outcomes were assessed to evaluate the safety and efficacy of the method. Results: All 16 patients successfully underwent LLH. The mean operation time was 380.31 ± 92.17 min, with a mean estimated blood loss of 116.25 ± 64.49 ml. ARN successfully aided in guiding hilar vessel dissection in all patients. ICG fluorescence imaging successfully identified liver resection boundaries in 11 patients (68.8%). In the remaining 5 patients (31.3%) where fluorescence imaging failed, virtual liver segment projection (VLSP) successfully identified their resection boundaries. No major complications occurred in any patients. Immediate stone residual rate, stone recurrence rate, and stone extraction rate through the T-tube sinus tract were 12.5%, 6.3%, and 6.3%, respectively. Conclusion: The combination of ARN and ICG fluorescence imaging enhances the safety and precision of LLH for hepatolithiasis. Moreover, ARN may serve as a safe and effective tool for identifying precise resection boundaries in cases where ICG fluorescence imaging fails. [ABSTRACT FROM AUTHOR]

Background: Segment 2/3 (S2/3) resection, which can preserve more residual liver parenchyma, is a feasible alternative to left lateral sectionectomy. However, it is still challenging to perform anatomical S2/3 resection safely and precisely, especially laparoscopically. This study was designed to evaluate the safety and accuracy of the temporary inflow control of the Glissonean pedicle (TICGL) technique combined with indocyanine green (ICG) fluorescence imaging in laparoscopic anatomical S2/3 resection. Patients and methods: A total of 12 patients recruited at Zhujiang Hospital of Southern Medical University from June 2021 to August 2022 were included in the study. All patients underwent ICG fluorescence imaging guided laparoscopic anatomical S2/3 resection. The TICGL technique was used to control the blood inflow of the target segment. The total time used to control the hepatic inflow of the target segment, the time of hemostasis, the amount of intraoperative blood loss, predicted resected liver volume (PRLV) and actual resected liver volume (ARLV) were used to evaluate the simplicity, safety, and accuracy of the TICGL technique combined ICG fluorescent imaging in guiding laparoscopic anatomical S2/3 resection. Results: Of the 12 included patients, 7 underwent S2 resection and 5 underwent S3 resection. The operation time was 76.92 ± 11.95 min, the intraoperative blood loss was 15.42 ± 5.82 ml, and the time of hepatic blood inflow control was 7.42 ± 2.43 min. There was a strong correlation between PRLV and ARLV (r = 0.903, P < 0.05). Conclusion: The combination of the TICGL technique with ICG negative staining fluorescence imaging is a feasible approach for laparoscopic anatomical S2/3 resection. [ABSTRACT FROM AUTHOR]

Background: Accurate determination of intrahepatic anatomy remains challenging for laparoscopic anatomical hepatectomy (LAH). Laparoscopic augmented reality navigation (LARN) is expected to facilitate LAH of primary liver cancer (PLC) by identifying the exact location of tumors and vessels. The study was to evaluate the safety and effectiveness of our independently developed LARN system in LAH of PLC. Methods: From May 2018 to July 2020, the study included 85 PLC patients who underwent three-dimensional (3D) LAH. According to whether LARN was performed during the operation, the patients were divided into the intraoperative navigation (IN) group and the non-intraoperative navigation (NIN) group. We compared the preoperative data, perioperative results and postoperative complications between the two groups, and introduced our preliminary experience of this novel technology in LAH. Results: There were 44 and 41 PLC patients in the IN group and the NIN group, respectively. No significant differences were found in preoperative characteristics and any of the resection-related complications between the two groups (All P > 0.05). Compared with the NIN group, the IN group had significantly less operative bleeding (P = 0.002), lower delta Hb% (P = 0.039), lower blood transfusion rate (P < 0.001), and reduced postoperative hospital stay (P = 0.003). For the IN group, the successful fusion of simulated surgical planning and operative scene helped to determine the extent of resection. Conclusions: The LARN contributed to the identification of important anatomical structures during LAH of PLC. It reduced vascular injury and accelerated postoperative recovery, showing a potential application prospects in liver surgery. [ABSTRACT FROM AUTHOR]

Background: Extended right posterior sectionectomy (ERPS) preserves more liver parenchyma than right hepatectomy when hepatocellular carcinoma (HCC) is in the right posterior section (RPS) and part of the right anterior section (RAS), but the difficulty lies in the precise determination of the cutting plane, especially under laparoscopy.[Torzilli et al. in Annals of surgery. 247:603–611, 2008] If the right hepatic vein (RHV) is not invaded by the tumor, it can help to divide the ventral and dorsal plane (VP, DP) as surgical landmark.[Makuuchi in International Journal of Surgery. 11:S47–S49, 2013] (Fig. 1) This study presented a laparoscopic modular ERPS (LMERPS) guided by projection plane extension from the RHV. Methods: A 56-year-old man was seen with HCC in the (RPS) and segment 8 following two laparotomies. After releasing intraperitoneal adhesions, the short hepatic veins were severed to expose the inferior vena cava (IVC). The right posterior Glission pedicle (RPGP) was clamped to control RPS inflow and allow determination of the demarcation line (DL) between the RPS and RAS using ICG fluorescence staining.[Chen et al. in Annals of surgical oncology. 29:2034–2040, 2022] Intraoperative ultrasound identified the RHV projection to satisfy the requirements of oncologic treatment. The VP and DP were incised along the DL and RHV projection. The RHV was exposed fully on the cutting plane and the tumor was completely removed finally. Results: The operation was completed in 265 min, with a blood loss of 50 ml. The diagnosis was HCC with a negative resection margin. The patient was discharged on postoperative day 8 without any complications. Conclusion: LMERPS guided by a projection plane extending from the RHV is feasible and effective. [ABSTRACT FROM AUTHOR]

• Augmented reality techniques can help surgeons to see the internal anatomy from laparoscopic video images. • Deep learning methods are successfully used in dense stereo reconstructions of liver surfaces and in liver segmentations of preoperative CT images. • The augmented reality prototype system was validated ex vivo and in vivo and the accuracy is comparable to the state-of-the-art. Understanding the three-dimensional (3D) spatial position and orientation of vessels and tumor(s) is vital in laparoscopic liver resection procedures. Augmented reality (AR) techniques can help surgeons see the patient's internal anatomy in conjunction with laparoscopic video images. In this paper, we present an AR-assisted navigation system for liver resection based on a rigid stereoscopic laparoscope. The stereo image pairs from the laparoscope are used by an unsupervised convolutional network (CNN) framework to estimate depth and generate an intraoperative 3D liver surface. Meanwhile, 3D models of the patient's surgical field are segmented from preoperative CT images using V-Net architecture for volumetric image data in an end-to-end predictive style. A globally optimal iterative closest point (Go-ICP) algorithm is adopted to register the pre- and intraoperative models into a unified coordinate space; then, the preoperative 3D models are superimposed on the live laparoscopic images to provide the surgeon with detailed information about the subsurface of the patient's anatomy, including tumors, their resection margins and vessels. The proposed navigation system is tested on four laboratory ex vivo porcine livers and five operating theatre in vivo porcine experiments to validate its accuracy. The ex vivo and in vivo reprojection errors (RPE) are 6.04 ± 1.85 mm and 8.73 ± 2.43 mm, respectively. Both the qualitative and quantitative results indicate that our AR-assisted navigation system shows promise and has the potential to be highly useful in clinical practice. [ABSTRACT FROM AUTHOR]