Artificial intelligence-driven CT radiomics model predicts prognosis in TACE-refractory hepatocellular carcinoma.

[PURPOSE] To develop an integrated predictive model combining radiomics, clinical risk factors, and machine learning for prognostic assessment in hepatocellular carcinoma (HCC) patients receiving continued transarterial chemoembolization (TACE) after developing TACE resistance.

[MATERIALS AND METHODS] In this retrospective study, 108 HCC patients with TACE resistance treated between September 2015 and September 2024 were analyzed. The dataset was randomly divided into training (70%) and test (30%) cohorts. Radiomics features were extracted from both intratumoral and peritumoral regions, with margins of 3 mm, 6 mm, and 10 mm. Subsequently, radiomics scores (rad-scores) were computed. Multiple machine learning algorithms were evaluated for model performance.

[RESULTS] Multivariate analysis identified Barcelona Clinic Liver Cancer (BCLC) stage and lymphocyte-to-monocyte ratio (LMR) as independent prognostic factors. The support vector machine (SVM)-based combined model showed the highest predictive accuracy, with an area under the curve (AUC) of 0.956 (95% CI: [0.910-1.000]) in the training cohort and 0.897 (95% CI: [0.772-1.000]) in the test cohort, significantly surpassing models using only clinical or radiomics data. Survival analysis revealed a longer median survival in the low rad-score group (< 3.05) compared to the high rad-score group (33 vs. 22 months; p = 0.009) and in the high-LMR group versus the low-LMR group (32 vs. 15 months; p < 0.001).

[CONCLUSION] The machine learning-based combined model can effectively predict the prognosis of HCC patients who continue TACE after developing TACE resistance. Rad-score, BCLC stage, and LMR are potential prognostic indicators for repeated TACE following TACE refractoriness.