An Optimal Ablative Margin of Small Single Hepatocellular Carcinoma Treated with Image-Guided Percutaneous Thermal Ablation and Local Recurrence Prediction Base on the Ablative Margin: A Multicenter Study.

Objective: To explore the best ablative margin (AM) for single hepatocellular carcinoma (HCC) patients with image-guided percutaneous thermal ablation (IPTA) based on MRI-MRI fusion imaging, and to develop and validate a local tumor progression (LTP) predictive model based on the recommended AM.

Methods: Between March 2014 and August 2019, 444 treatment-naïve patients with single HCC (diameter ≤3 cm) who underwent IPTA as first-line treatment from three hospitals were included, which were randomly divided into training (n= 296) and validation (n = 148) cohorts. We measured the ablative margin (AM) by MRI-MRI fusion imaging based on pre-ablation and post-ablation images. Then, we followed up their LPT and verified the optimal AM. Risk factors related to LTP were explored through Cox regression models, the nomogram was developed to predict the LTP risk base on the risk factors, and subsequently validated. The predictive performance and discrimination were assessed and compared with conventional indices.

Results: The median follow-up was 19.9 months (95% CI 18.0-21.8) for the entire cohort. The results revealed that the tumor size (HR: 2.16; 95% CI 1.25-3.72; P = 0.003) and AM (HR: 0.72; 95% CI, 0.61-0.85; P < 0.001) were independent prognostic factors for LTP. The AM had a pronounced nonlinear impact on LTP, and a cut-off value of 5-mm was optimal. We developed and validated an LTP predictive model based on the linear tumor size and nonlinear AM. The model showed good predictive accuracy and discrimination (training set, concordance index [C-index] of 0.751; validation set, C-index of 0.756) and outperformed other conventional indices.

Conclusion: The 5-mm AM is recommended for the best IPTA candidates with single HCC (diameter ≤3 cm). We provided an LTP predictive model that exhibited adequate performance for individualized prediction and risk stratification.