Exploring the prognostic value of EBV DNA in advanced nasopharyngeal carcinoma treated with chemoradiotherapy using AI-based modeling.

IF 3.5 3区医学 Q2 ONCOLOGY

Frontiers in Oncology Pub Date : 2025-09-12 eCollection Date: 2025-01-01 DOI:10.3389/fonc.2025.1650377

Yang Yang, Ningchuan Shang, Shun Lu, Lintao Li, Peng Xu, Xianliang Wang, Fan Li, Yue Su, Yuan Qin, Jinyi Lang, Jie Zhou

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引用次数: 0

Abstract

Background: Epstein-Barr virus (EBV) DNA is a well-established biomarker in nasopharyngeal carcinoma (NPC), but its integration into artificial intelligence (AI)-based prognostic tools remains limited. This study aimed to develop and validate AI models incorporating EBV DNA load levels to predict progression-free survival (PFS) in patients with advanced NPC treated with concurrent chemoradiotherapy (CRT).

Methods: A retrospective multicenter cohort of 503 patients was divided into training (n = 301) and validation (n = 202) sets. Four machine learning algorithms-Cox regression, LASSO, RSF, and GBM-were applied to predict 1- and 1.5-year PFS in patients with advanced NPC. Model performance was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC), decision curve analysis (DCA), and interpretability tools such as SHAP values and partial dependence plots (PDP).

Results: The 1-, 3-, and 5-year PFS rates were 100.0%, 91.5%, and 88.6% in the EBV = 0 group; 99.4%, 91.2%, and 88.5% in the > 0 and < 1500 group; and 92.3%, 81.0%, and 75.7% in the ≥ 1500 group, respectively, with statistically significant differences among the three groups (P = 0.0024). The RSF model outperformed other models with the highest C-index (0.778) and area under the ROC curve of 0.810 and 0.634 at 1 and 1.5 years, respectively. EBV DNA emerged as the most influential predictor across all interpretability analyses. Patients with EBV DNA ≥1500 copies/ml had the poorest predicted survival, showing a distinct threshold effect in the PDP.

Conclusions: High EBV DNA levels were associated with poorer PFS in advanced NPC. Among the models evaluated, the RSF model demonstrated the best predictive performance and interpretability. EBV-informed AI modeling represents a promising approach for enhancing individualized risk prediction and clinical decision-making in NPC.

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利用人工智能模型探讨EBV DNA在晚期鼻咽癌放化疗中的预后价值。

背景：eb病毒（EBV） DNA是鼻咽癌（NPC）中一种公认的生物标志物，但其与基于人工智能（AI）的预后工具的整合仍然有限。本研究旨在开发和验证结合EBV DNA负荷水平的AI模型，以预测接受同步放化疗（CRT）治疗的晚期鼻咽癌患者的无进展生存期（PFS）。方法：回顾性多中心队列研究503例患者，分为训练组（n = 301）和验证组（n = 202）。四种机器学习算法——cox回归、LASSO、RSF和gbm——被用于预测晚期鼻咽癌患者1年和1.5年的PFS。采用一致性指数（C-index）、时间依赖的受试者工作特征（ROC）、决策曲线分析（DCA）和SHAP值和部分依赖图（PDP）等可解释性工具来评估模型的性能。结果：EBV = 0组1、3、5年PFS分别为100.0%、91.5%、88.6%；> 0和< 1500组分别为99.4%、91.2%和88.5%；≥1500组分别为92.3%、81.0%、75.7%，三组间差异有统计学意义（P = 0.0024）。RSF模型在1年和1.5年时的c指数最高（0.778），ROC曲线下面积最高（0.810），0.634，优于其他模型。EBV DNA成为所有可解释性分析中最具影响力的预测因子。EBV DNA≥1500拷贝/ml的患者预测生存率最低，在PDP中显示出明显的阈值效应。结论：EBV DNA水平高与晚期鼻咽癌患者较差的PFS相关。在评价的模型中，RSF模型的预测性能和可解释性最好。基于ebv的人工智能模型代表了一种有前途的方法，可以增强鼻咽癌的个体化风险预测和临床决策。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Oncology Biochemistry, Genetics and Molecular Biology-Cancer Research

CiteScore

6.20

自引率

10.60%

发文量

6641

审稿时长

14 weeks

期刊介绍： Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.