A Nomogram Based on Features of Ultrasonography and Contrast-Enhanced CT to Predict Vessels Encapsulating Tumor Clusters Pattern of Hepatocellular Carcinoma

Objective: This study aimed to establish a clinical prediction model for vessels encapsulating tumor clusters (VETC) based on preoperative ultrasonography (US) and contrast-enhanced computed tomography (CECT) imaging in patients with hepatocellular carcinoma (HCC). Methods: Data were retrospectively collected from 215 patients who underwent hepatectomy for solitary HCC lesions. They were divided into training and validation cohorts at a ratio of 6:4. Preoperative imaging features were extracted (seven from US and nine from CECT imaging) to explore their relationship with VETC. A VETC prediction model was constructed and graphically depicted as a nomogram. Its performance was evaluated via the receiver operating characteristic (ROC) curve, the calibration curve, and decision curve analysis (DCA). Results: The VETC incidence for all the lesions was 37.7%. The final variables included in the nomogram were "peritumoral enhancement in CECT", "alpha-fetoprotein level > 200 ng/Ml," "halo in US," "capsule enhancement in CECT," and "posterior acoustic enhancement in US." The area under the curve (AUC) values for the training and validation cohorts were 0.824 and 0.725, respectively. The Hosmer-Lemeshow fit test showed no statistical difference (p p = 0.369 and p = 0.067 for the training and validation cohorts, respectively). DCA demonstrated that our nomogram provided clinical benefits to a wide range of patients. According to the nomogram score, the VETC-positive and-negative groups demonstrated significant differences in both the training (p p <0.001) and validation (p p = 0.001) cohorts. Conclusion: Our prediction model based on US and CECT imaging features can accurately predict VETC in HCC.