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Simple Summary: Sarcopenia, which is defined as a loss of skeletal muscle mass, function and strength, is the result of major metabolic changes often observed in advanced liver disease. Its evaluation mirrors the nutritional and functional status of the patients, and thus has been recently implicated as an outcome predictor of patients with liver diseases and hepatocellular carcinoma. This study provides evidence that sarcopenia, as assessed by the skeletal muscle index, is associated with age and body mass index in liver surgery candidates. More importantly, it is associated with higher rates of major complications (Clavien-Dindo grade III or IV) in patients with compensated advanced chronic liver disease and/or portal hypertension undergoing liver resection for primary hepatocellular carcinoma. The burden of post-operative complications of patients undergoing liver resection for hepatocellular carcinoma (HCC) is a cause of morbidity and mortality. Recently, sarcopenia has been reported to influence the outcome of patients with cirrhosis. We aimed to assess factors associated with sarcopenia and its prognostic role in liver surgery candidates. We included all patients with compensated advanced chronic liver disease (cACLD) undergoing liver resection for primary HCC consecutively referred to the University of Bologna from 2014 to 2019 with an available preoperative abdominal CT-scan performed within the previous three months. A total of 159 patients were included. The median age was 68 years, and 80.5% of the patients were male. Sarcopenia was present in 82 patients (51.6%). Age and body mass index (BMI) were associated with the presence of sarcopenia at multivariate analysis. Thirteen (8.2%) patients developed major complications and 14 (8.9%) presented PHLF grade B-C. The model for end-stage liver disease score was associated with the development of major complications, whereas cACLD presence, thrombocytopenia, portal hypertension (PH), Child-Pugh score and Albumin-Bilirubin score were found to be predictors of clinically significative PHLF. The rate of major complications was 11.8% in sarcopenic patients with cACLD compared with no complications (0%) in patients without sarcopenia and cACLD (p = 0.032). The rate of major complications was significantly higher in patients with (16.3%) vs. patients without (0%) sarcopenia (p = 0.012) in patients with PH. In conclusion, sarcopenia, which is associated with age and BMI, may improve the risk stratification of post-hepatectomy major complications in patients with cACLD and PH. [ABSTRACT FROM AUTHOR]

Simple Summary: Microvascular invasion (MVI) is a universally recognised predictor of hepatocellular carcinoma (HCC) recurrence after curative treatments, whose diagnosis, nowadays, is still postponed to surgery, through histopathological specimen investigation. This retrospective study aims to exploit a radiomic approach to pre-procedural diagnosis MVI in early-stage HCC, with a diameter ≤ 3 cm. The main novelty of the study is the use of the zone of transition (ZOT), crossing tumour and peritumour, detected adaptively through a standardized procedure based on the analysis of image gradients. After generating radiomics features from ZOT and tumour core in arterial and venous computed tomography phases, a classifier is trained and validated using a signature of four features only. The achieved radiomic model enables the early diagnosis of small HCC (≤3 cm) showing MVI with high specificity (82%) and sensitivity (79%), perspectively providing an effective tool to detect the best candidates for surgical treatment and liver transplantation (negative predictive value = 87%). Background: Microvascular invasion (MVI) is a consolidated predictor of hepatocellular carcinoma (HCC) recurrence after treatments. No reliable radiological imaging findings are available for preoperatively diagnosing MVI, despite some progresses of radiomic analysis. Furthermore, current MVI radiomic studies have not been designed for small HCC nodules, for which a plethora of treatments exists. This study aimed to identify radiomic MVI predictors in nodules ≤3.0 cm by analysing the zone of transition (ZOT), crossing tumour and peritumour, automatically detected to face the uncertainties of radiologist's tumour segmentation. Methods: The study considered 117 patients imaged by contrast-enhanced computed tomography; 78 patients were finally enrolled in the radiomic analysis. Radiomic features were extracted from the tumour and the ZOT, detected using an adaptive procedure based on local image contrast variations. After data oversampling, a support vector machine classifier was developed and validated. Classifier performance was assessed using receiver operating characteristic (ROC) curve analysis and related metrics. Results: The original 89 HCC nodules (32 MVI+ and 57 MVI−) became 169 (62 MVI+ and 107 MVI−) after oversampling. Of the four features within the signature, three are ZOT heterogeneity measures regarding both arterial and venous phases. On the test set (19MVI+ and 33MVI−), the classifier predicts MVI+ with area under the curve of 0.86 (95%CI (0.70–0.93), p∼10 − 5 ), sensitivity = 79% and specificity = 82%. The classifier showed negative and positive predictive values of 87% and 71%, respectively. Conclusions: The classifier showed the highest diagnostic performance in the literature, disclosing the role of ZOT heterogeneity in predicting the MVI+ status. [ABSTRACT FROM AUTHOR]