Development and validation of the NTAA prognostic model for BCLC stage B hepatocellular carcinoma undergoing transarterial chemoembolization: a retrospective multicenter cohort study.

Objective: Our aim was to establish a valid new prognostic predictive tool for assessing the outcomes after TACE treatment in hepatocellular carcinoma (HCC) patients with Child-Pugh A at Barcelona clinical liver cancer stage B (BCLC B) undergoing transarterial chemoembolization (TACE).

Methods And Materials: We retrospectively analyzed 2529 HBV-related BCLC B HCC patients with Child-Pugh grade A who received initial TACE treatment, with 1075 in the primary cohort, 1076 in the internal validation cohort, and the remaining 378 in the multicenter external validation cohort. The NTAA prognostic model were constructed by Cox proportional hazards regression. The prognostic prediction performance of NTAA prognostic model with AJCC staging and other criterion were compared by time-dependent ROC and C-index. In addition, we included 305 patients of non-HBV-related BCLC stage B HCC who received TACE treatment in our center to further validate the predictive performance of the NTAA model.

Results: Tumor size, tumor number, alpha-fetoprotein level, and albumin-bilirubin grade were found to be independent factors affecting overall survival (OS) after TACE in these patients. The NTAA prognostic model established accordingly divided patients into low-risk, intermediate-risk, and high-risk groups, with a median survival of 68.1, 35.7 and 15.4 months, respectively. Time-dependent ROC and C-index showed that the NTAA prognostic model was better than other existing criterion and AJCC staging in predicting OS, especially in predicting early OS rates. Furthermore, the NTAA criteria was validated in both internal validation cohort, multi-center external validation cohort, and non-HBV-related HCC cohort.

Conclusion: The proposed NTAA prognostic prediction model provide more accurate prognosis prediction of BCLC B HCC patients with Child-Pugh A who received TACE. The implementation of this model promises to improve the clinical decision-making process and provide more personalized treatment options for patients.