Abstract
BACKGROUND: Recently, a novel vascular pattern characterized by vessels encapsulating tumor clusters (VETC) was reported to be related to poor clinical outcomes in patients with hepatocellular carcinoma (HCC). The objective of this study was to preliminarily assess the ability of metabolic parameters and radiomics features derived from (18)F-fluorodeoxyglucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) to preoperatively predict VETC and prognosis in HCC patients. METHODS: A total of 149 patients diagnosed with HCC from two institutions (The Third Affiliated Hospital of Sun Yat-sen University and Sun Yat-sen Memorial Hospital) were retrospectively enrolled and subsequently divided into a training cohort (n=103) and a test cohort (n=46) as external validation. The correlation between traditional image features on computed tomography (CT)/magnetic resonance imaging (MRI) and (18)F-FDG PET/CT and VETC status were evaluated and compared. Radiomics features were extracted from (18)F-FDG PET/CT images, followed by calculation of a radiomics score (Radscore). Univariate and multivariate logistic regression analyses were used to screen out the independent indicators. A nomogram model was developed based on Radscore and clinical indicators, and a clinical model was developed based on clinical indicators. The performance of the nomogram, clinical model, Radscore, as well as traditional PET parameter tumor-to-liver ratio (TLR) were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Disease-free survival (DFS) and overall survival (OS) rates were assessed using Kaplan-Meier survival analysis. RESULTS: The difference in FDG parameter TLR between VETC-positive and VETC-negative HCC was found to be statistically significant (P<0.05), which was consistent with traditional CT/MRI imaging features. The Radscore was derived by calculating 13 selected radiomics features, comprising of six PET radiomics features and seven CT radiomics features. The nomogram model exhibited an area under the curve (AUC) of 0.908 [95% confidence interval (CI): 0.852-0.963; sensitivity: 0.855; specificity: 0.833] and 0.762 (95% CI: 0.624-0.900; sensitivity: 0.739; specificity: 0.739) in the training and test cohort, respectively. The disparity in the prediction of VETC status based on the nomogram model between DFS and OS was statistically comparable to that observed between VETC-positive and VETC-negative cases through pathological analysis (P<0.05). CONCLUSIONS: FDG metabolism is significantly associated with VETC status in HCC patients. A comprehensive nomogram model based on PET/CT radiomics and clinical indicators has potential for preoperative prediction of VETC as well as patient prognosis.