Application of CT-based radiomics combined with laboratory tests such as AFP and PIVKA-II in preoperative prediction of pathologic grade of hepatocellular carcinoma.

Background: To investigate how effectively clinical features and CT-based radiomic features predict the pathological grade of hepatocellular carcinoma (HCC).

Methods: We retrospectively analyzed 108 patients diagnosed with hepatocellular carcinoma who underwent pathological examination between May 2020 and May 2024 at the Second Hospital of Jilin University. All patients underwent laboratory tests and contrast-enhanced computed tomography (CECT) scanning of the liver within one month prior to pathological examination. First, we analyzed laboratory tests, such as alpha fetoprotein (AFP) and des-γ-carboxy prothrombin (PIVKA-II), to identify risk factors associated with the pathological grading of HCC. Then, we built and evaluated the performance of the clinical model. Next, we imported the arterial-phase and venous-phase images of the CECT images into the uAI Research Portal research platform for 'one-stop' processing, which included semiautomatic ROI outlining, feature extraction, dimensionality reduction, model construction and evaluation. To evaluate the model's diagnostic effectiveness, receiver operating characteristic (ROC) curves were produced, and the related accuracy, sensitivity, specificity, and area under the curve (AUC) were computed. The models were compared using the Delong test, and the clinical value of the predictive model was assessed via the use of calibration curves and decision curve analysis (DCA) to quantify the agreement between the model and the actual outcomes.

Results: Poorly differentiated hepatocellular carcinoma (pHCC) is associated with risk variables such as hepatitis C virus antibodies(HCVAb), PIVKA-II, and sex. In the training and validation cohorts, the AUC values of the clinical model were 0.719 and 0.692, respectively; those of the AP model were 0.843 and 0.773; those of the VP model were 0.806 and 0.804; those of the AP + VP model were 0.953 and 0.844; and those of the AP + VP + clinical model were 0.926 (95% CI: 0.88-0.995) and 0.863 (95% CI: 0.711-1). The DCA curves revealed that the overall net benefit of the AP + VP + clinical model was greater than that of the other models and that it had the best diagnostic results.

Conclusions: CT-based radiomic modeling combined with clinical features (sex) and laboratory tests (e.g., AFP and PIVKA-II) can reliably predict the pathological grade of HCC patients prior to surgery.