Journal
HEPATOLOGY INTERNATIONAL
Volume -, Issue -, Pages -Publisher
SPRINGER
DOI: 10.1007/s12072-023-10585-y
Keywords
Hepatocellular carcinoma; Radiofrequency ablation; Transformer; Prognosis
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This study developed a transformer model to predict overall survival in HCC patients treated with RFA and compared its performance with a deep learning model. The transformer-based model showed better discrimination performance and the ability to stratify patients into different risk groups. It also provided personalized cumulative recurrence prediction for each patient.
Introduction Radiofrequency ablation (RFA) is a widely accepted, minimally invasive treatment modality for patients with hepatocellular carcinoma (HCC). Accurate prognosis prediction is important to identify patients at high risk for cancer progression/recurrence after RFA. Recently, state-of-the-art transformer models showing improved performance over existing deep learning- based models have been developed in several fields. This study was aimed at developing and validating a transformer model to predict the overall survival in HCC patients with treated by RFA. Methods We enrolled a total of 1778 treatment-naive HCC patients treated by RFA as the first-line treatment. We developed a transformer-based machine learning model to predict the overall survival in the HCC patients treated by RFA and compared its predictive performance with that of a deep learning-based model. Model performance was evaluated by determining the Harrel's c-index and validated externally by the split-sample method. Results The Harrel's c-index of the transformer-based model was 0.69, indicating its better discrimination performance than that of the deep learning model (Harrel's c-index, 0.60) in the external validation cohort. The transformer model showed a high discriminative ability for stratifying the external validation cohort into two or three different risk groups (p < 0.001 for both risk groupings). The model also enabled output of a personalized cumulative recurrence prediction curve for each patient. Conclusions We developed a novel transformer model for personalized prediction of the overall survival in HCC patients after RFA treatment. The current model may offer a personalized survival prediction schema for patients with HCC undergoing RFA treatment.
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