Safety and benefit of modified mesohepatectomy in the treatment of bilobar involvement congenital biliary dilatation.

BACKGROUND: The diseased bile duct in bilobar congenital biliary dilatation is extensive and often requires major hepatectomy or liver transplantation associated with a higher risk. We aimed to evaluate the safety and benefit of modified mesohepatectomy, in comparison with trisectionectomy, to treat bilobar congenital biliary dilatation. METHODS: This study included 28 patients with type IV and V bilobar congenital biliary dilatation. An innovative mesohepatectomy comprising the hepatectomy technique beyond the P/U point and bile duct shaping was applied to 14 patients to address the extensively diseased bile duct and difficulty in hepaticojejunostomy. Another 14 patients received trisectionectomy. The perioperative and long-term outcomes of these patients were compared. RESULTS: The ratio of residual liver volume to standard liver volume in the mesohepatectomy group was higher (78.68% vs. 40.90%, p = 0.005), while the resection rate of the liver parenchyma was lower (28.25% vs. 63.97%, p = 0.000), than that in trisectionectomy group. The mesohepatectomy group had a lower severe complication (>Clavein III, 0% vs. 57.70%, p = 0.019) and incidence of posthepatectomy liver failure (7.14% vs. 42.86%, p = 0.038). No significant difference was observed in blood loss and bile leakage (p > 0.05). All the patients in the mesohepatectomy group achieved optimal results in the long-term follow-up. CONCLUSIONS: mesohepatectomy provides an efficient treatment option for bilobar congenital biliary dilatation and can achieve radical resection, retain more liver parenchyma, and reduce the difficulty of hepaticojejunostomy, especially for patients that are not eligible for major hepatectomy and liver transplantation.

Predicting the outcomes of hepatocellular carcinoma downstaging with the use of clinical and radiomics features.

BACKGROUND: Downstaging of hepatocellular carcinoma (HCC) makes it possible for patients beyond the criteria to have the chance of liver transplantation (LT) and improved outcomes. Thus, a procedure to predict the prognosis of the treatment is an urgent requisite. The present study aimed to construct a comprehensive framework with clinical information and radiomics features to accurately predict the prognosis of downstaging treatment. METHODS: Specifically, three-dimensional (3D) tumor segmentation from contrast-enhanced computed tomography (CT) is employed to extract spatial information of the lesions. Then, the radiomics features within the segmented region are calculated. Combining radiomics features and clinical data prompts the development of feature selection to enhance the robustness and generalizability of the model. Finally, we adopt the support vector machine (SVM) algorithm to establish a classification model for predicting HCC downstaging outcomes. RESULTS: Herein, a comparative study was conducted on three different models: a radiomics features-based model (R model), a clinical features-based model (C model), and a joint radiomics clinical features-based model (R-C model). The average accuracy of the three models was 0.712, 0.792, and 0.844, and the average area under the receiver-operating characteristic (AUROC) of the three models was 0.775, 0.804, and 0.877, respectively. CONCLUSIONS: The novel and practical R-C model accurately predicted the downstaging outcomes, which could be utilized to guide the HCC downstaging toward LT treatment.