To construct a Nomogram model for predicting the survival of patients with hepatocellular carcinoma (HCC) based on preoperative CT imaging data.

Clinical imaging data of 243 patients with HCC who underwent surgical treatment in the Affiliated Hospital of Qingdao University from January 2018 to December 2020 were retrospectively analyzed. Among them, 206 patients were male and 37 female, aged from 49 to 70 years, with a median age of 59 years. Multi-modal data of preoperative contrast-enhanced CT were collected. Multi-dimensional quantitative radiomic features were extracted by Pyradiomics, including tumor morphological features, signal intensity distribution, texture features and signal intensity-volume histogram, etc. All patient datasets were divided into the training (n=158) and validation sets (n=85). Univariate and multivariate Cox regression analyses were used to screen the independent factors affecting the survival and prognosis of HCC patients. Nomogram model was constructed based on these factors.

Based on the standardized feature data, 30 key imaging features were eventually determined. Cox regression analysis identified three independent key radiomic features including glcm_MCC, glszm_Zone Percentage and shape_Sphericity. The area under the ROC curve (AUC) of the training set in Cox regression model was 0.82. All patients were divided into the high-risk and low-risk groups based on the risk score of multivariate Cox regression analysis. Kaplan-Meier survival analysis showed that the difference in the survival between the low-risk and high-risk groups in the training set was statistically significant (χ²=7.353, P<0.05). A Nomogram model of survival and prognosis of patients with liver cancer was constructed. The AUC of the Nomogram model in the validation set was 0.78. The difference in the survival between the low-risk and high-risk groups in the validation set was statistically significant (χ²=2.38, P<0.05), which further validated the effectiveness and reliability of the Nomogram model.

Nomogram model constructed based on preoperative CT imaging data can effectively predict the survival of HCC patients, which can effectively assist early prediction and provide decision-making support for clinicians.