Integration of Multimodal Computed Tomography Radiomic Features of Primary Tumors and the Spleen to Predict Early Recurrence in Patients with Postoperative Adjuvant Transarterial Chemoembolization.

Abstract

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies in the world. Patients with HCC choose postoperative adjuvant transarterial chemoembolization (PA-TACE) after surgical resection to reduce the risk of recurrence. However, many of them have recurrence within a short period. In this retrospective analysis, a total of 173 patients who underwent PA-TACE between September 2016 and March 2020 were recruited. Radiomic features were derived from the arterial and venous phases of each patient. Early recurrence (ER)-related radiomics features of HCC and the spleen were selected to build two rad-scores using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Logistic regression was applied to establish the Radiation (Rad)_score by combining the two regions. We constructed a nomogram containing clinical information and dual-region rad-scores, which was evaluated in terms of discrimination, calibration, and clinical usefulness. All three radiological scores showed good performance for ER prediction. The combined Rad_score performed the best, with an area under the curve (AUC) of 0.853 (95% confidence interval [CI], 0.783-0.908) in the training set and 0.929 (95% CI, 0.789-0.988) in the validation set. Multivariate analysis identified total bilirubin (TBIL) and the combined Rad_score as independent prognostic factors for ER. The nomogram was found to be clinically valuable, as determined by the decision curves (DCA) and clinical impact curves (CIC). A multimodal dual-region radiomics model combining HCC and the spleen is an independent prognostic tool for ER. The combination of dual-region radiomics features and clinicopathological factors has a good clinical application value.