Predicting the early therapeutic response to hepatic artery infusion chemotherapy in patients with unresectable HCC using a contrast-enhanced computed tomography-based habitat radiomics model: a multi-center retrospective study.

OBJECTIVE

Predicting the therapeutic response before initiation of hepatic artery infusion chemotherapy (HAIC) with fluorouracil, leucovorin, and oxaliplatin (FOLFOX) remains challenging for patients with unresectable hepatocellular carcinoma (HCC). Herein, we investigated the potential of a contrast-enhanced CT-based habitat radiomics model as a novel approach for predicting the early therapeutic response to HAIC-FOLFOX in patients with unresectable HCC.

METHODS

A total of 148 patients with unresectable HCC who received HAIC-FOLFOX combined with targeted therapy or immunotherapy at three tertiary care medical centers were enrolled retrospectively. Tumor habitat features were extracted from subregion radiomics based on CECT at different phases using k-means clustering. Logistic regression was used to construct the model. This CECT-based habitat radiomics model was verified by bootstrapping and compared with a model based on clinical variables. Model performance was evaluated using the area under the curve (AUC) and a calibration curve.

RESULTS

Three intratumoral habitats with high, moderate, and low enhancement were identified to construct a habitat radiomics model for therapeutic response prediction. Patients with a greater proportion of high-enhancement intratumoral habitat showed better therapeutic responses. The AUC of the habitat radiomics model was 0.857 (95% CI: 0.798-0.916), and the bootstrap-corrected concordance index was 0.842 (95% CI: 0.785-0.907), resulting in a better predictive value than the clinical variable-based model, which had an AUC of 0.757 (95% CI: 0.679-0.834).

CONCLUSION

The CECT-based habitat radiomics model is an effective, visualized, and noninvasive tool for predicting the early therapeutic response of patients with unresectable HCC to HAIC-FOLFOX treatment and could guide clinical management and decision-making.