1. Development of a transformer model for predicting the prognosis of patients with hepatocellular carcinoma after radiofrequency ablation.
- Author
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Sato M, Moriyama M, Fukumoto T, Yamada T, Wake T, Nakagomi R, Nakatsuka T, Minami T, Uchino K, Enooku K, Nakagawa H, Shiina S, Koike K, Fujishiro M, and Tateishi R
- Subjects
- Humans, Neoplasm Recurrence, Local surgery, Prognosis, Retrospective Studies, Treatment Outcome, Carcinoma, Hepatocellular pathology, Liver Neoplasms pathology, Catheter Ablation methods, Radiofrequency Ablation
- Abstract
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-naïve 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., (© 2023. The Author(s).)
- Published
- 2024
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