射频消融（RFA）是一种广泛接受的微创治疗方式，适用于肝细胞肝癌（HCC）患者。准确的预后预测对于识别RFA后肿瘤进展/复发风险较高的患者至关重要。最近，在几个领域中，最新的Transformer模型显示出比现有的基于深度学习模型更好的性能。本研究旨在开发并验证一种Transformer模型，以预测接受RFA治疗的HCC患者的总体生存率。我们纳入了总共1778例首次接受RFA治疗的既往未治疗HCC患者。我们开发了一种基于Transformer的机器学习模型，用于预测接受RFA治疗的HCC患者的总体生存率，并将其预测性能与基于深度学习的模型进行了比较。通过确定Harrel’s c-index来评估模型性能，并通过分样本方法进行外部验证。Transformer模型的Harrel’s c-index为0.69，表明其在外部验证队列中具有更好的区分性能，优于深度学习模型（Harrel’s c-index为0.60）。Transformer模型能够有效将外部验证队列分为两个或三个不同的风险组（两种风险分组的p < 0.001）。该模型还能够为每位患者输出个性化的累积复发预测曲线。我们开发了一种新型的Transformer模型，用于个性化预测接受RFA治疗的HCC患者的总体生存率。目前的模型可以为接受RFA治疗的HCC患者提供个性化的生存预测模式。© 2023. The Author(s).

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.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.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.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).