In:
Hepatology International, Springer Science and Business Media LLC
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1936-0533
,
1936-0541
DOI:
10.1007/s12072-023-10585-y
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2023
detail.hit.zdb_id:
2270316-0
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