Amino acid metabolomics and machine learning-driven assessment of future liver remnant growth after hepatectomy in livers of various backgrounds.

Accurate assessment of future liver remnant growth after partial hepatectomy (PH) in patients with different liver backgrounds is a pressing clinical issue. Amino acid (AA) metabolism plays a crucial role in liver regeneration. In this study, we combined metabolomics and machine learning (ML) to develop a generalized future liver remnant assessment model for multiple liver backgrounds. The liver index was calculated at 0, 6, 24, 48, 72 and 168 h after 70 % PH in healthy mice and mice with nonalcoholic steatohepatitis or liver fibrosis. The serum levels of 39 amino acids (AAs) were measured using UPLC-MS/MS. The dataset was randomly divided into training and testing sets at a 2:1 ratio, and orthogonal partial least squares regression (OPLS) and minimally biased variable selection in R (MUVR) were used to select a metabolite signature of AAs. To assess liver remnant growth, nine ML models were built, and evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The post-Pareto technique for order preference by similarity to the ideal solution (TOPSIS) was employed for ranking the ML algorithms, and a stacking technique was utilized to establish consensus among the superior algorithms. Compared with those of OPLS, the signature AAs set identified by MUVR (Thr, Arg, EtN, Phe, Asa, 3MHis, Abu, Asp, Tyr, Leu, Ser, and bAib) are more concise. Post-Pareto TOPSIS ranking demonstrated that the majority of ML algorithm in combinations with MUVR outperformed those with OPLS. The established SVM-KNN consensus model performed best, with an R2 of 0.79, an MAE of 0.0029, and an RMSE of 0.0035 for the testing set. This study identified a metabolite signature of 12 AAs and constructed an SVM-KNN consensus model to assess future liver remnant growth after PH in mice with different liver backgrounds. Our preclinical study is anticipated to establish an alternative and generalized assessment method for liver regeneration.

Laparoscopic hepatectomy for hepatocellular carcinoma in patients with clinically significant portal hypertension: a systematic review and meta-analysis.

OBJECTIVE: To compare the effects of laparoscopic hepatectomy (LH) on the short-term and long-term outcomes in hepatocellular carcinoma (HCC) patients with and without clinically significant portal hypertension (CSPH). METHODS: A systematic literature search of the PubMed, EMBASE, and Cochrane databases was performed for articles published from inception to March 1, 2023. Meta-analysis of surgical and oncological outcomes was performed using a random effects model. Data were summarized as mean difference and risk ratio with 95% confidence intervals. RESULTS: Five cohort studies with a total of 310 HCC patients were included (CSPH 143; Non-CSPH 167). In terms of surgical outcomes, estimated blood loss and the length of hospital stay were significantly lower in the Non-CSPH group than in the CSPH group. There were no significant differences between the two groups regarding other surgical outcomes, including the operative time, ratio of conversion to open surgery, and overall complication rate. In addition, there were also no significant differences between the two groups regarding the oncological outcomes, such as 1-, 3-, and 5-year overall survival. CONCLUSIONS: HCC patients with and without CSPH who underwent LH had comparable surgical and oncological outcomes. LH is a safe and effective treatment for HCC patients with CSPH under the premise of rational screening of patients.

Robotic versus laparoscopic liver resection for liver malignancy: a systematic review and meta-analysis of propensity score-matched studies.

OBJECTIVE: How different surgical procedures, including the robotic-assisted liver resection (RLR) and laparoscopic liver resection (LLR), can affect the prognosis of patients with liver malignancies is unclear. Thus, in this study, we compared the effects of RLR and LLR on the surgical and oncological outcomes in patients with liver malignancies through propensity score-matched cohort studies. METHODS: The PubMed, Embase, and Cochrane databases were searched using Medical Subject Headings terms and keywords from inception until May 31, 2023. The quality of the included studies was assessed using the Newcastle-Ottawa quality assessment scale. The mean difference with 95% confidence interval (95% CI) was used for analysis of continuous variables; the risk ratio with 95% CI was used for dichotomous variables; and the hazard ratio with 95% CI was used for survival-related variables. Meta-analysis was performed using a random-effects model. RESULTS: Five high-quality cohort studies with 986 patients were included (370 and 616 cases for RLR and LLR, respectively). In terms of surgical outcomes, there were no significant differences in the operation time, conversion rate to open surgery, overall complication rate, major complication rate, and length of hospital stay between the RLR and LLR groups. In terms of oncological outcomes, there were no significant differences in the 5-year overall survival and disease-free survival between the two groups. CONCLUSION: Surgical and oncological outcomes are comparable between RLR and LLR on patients with liver malignancies. Therefore, the benefits of applying RLR in patients with liver malignancies need to be further explored.