Predicting radiation-induced hypothyroidism in nasopharyngeal carcinoma patients using a deep learning model

IF 2.7 3区 医学 Q3 ONCOLOGY Clinical and Translational Radiation Oncology Pub Date : 2025-03-13 DOI:10.1016/j.ctro.2025.100946
Yichen Mao , Mingjun Ding , Dan Zong , Zhongde Mu , Xia He
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Abstract

Background

Radiation-induced hypothyroidism (RIHT) is a common complication in nasopharyngeal carcinoma patients. Predicting its onset is crucial for effective management and early intervention. This study aims to develop a model based on deep learning survival analysis to predict RIHT in nasopharyngeal carcinoma patients.

Methods

This retrospective study included 535 nasopharyngeal carcinoma patients between January 2015 and October 2020. Cox regression, LASSO-Cox analyses and Spearman correlation test were employed to identify significant predictors. Two deep learning and two machine learning algorithms were trained, tuned, and compared against traditional Cox and NTCP models by C-index, Brier score, and decision curve analysis.

Results

The study observed a 41.7 % incidence of RIHT, with a median time to onset of 15 months. AJCC N stage, thyroid volume and specific dose-volume parameters were identified as potential predictors. DeepSurv model outperformed traditional ones (C-index: DeepSurv 0.75, traditional models ≤ 0.63). While other models were competitive at early post-treatment intervals, deep learning models demonstrated superior performance over time. Calibration and decision curve analysis corroborated the enhanced predictive capability of DeepSurv. Feature importance analysis highlighted thyroid V30 and V50 as the most significant predictors.

Conclusions

DeepSurv demonstrated superior predictive performance for RIHT in nasopharyngeal carcinoma patients compared to traditional models. Deep learning-based predictions offer high accuracy, which may enable personalized patient management and have great potentials in mitigating the risk of RIHT. These findings suggested that incorporating such model into clinical practice could be beneficial for the management of RITH.
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背景放射诱发甲状腺功能减退症(RIHT)是鼻咽癌患者常见的并发症。预测其发病对于有效管理和早期干预至关重要。本研究旨在开发一种基于深度学习生存分析的模型,以预测鼻咽癌患者的 RIHT。方法这项回顾性研究纳入了 2015 年 1 月至 2020 年 10 月间的 535 例鼻咽癌患者。采用 Cox 回归、LASSO-Cox 分析和 Spearman 相关性检验来确定重要的预测因素。对两种深度学习算法和两种机器学习算法进行了训练、调整,并通过 C 指数、Brier 评分和决策曲线分析与传统的 Cox 模型和 NTCP 模型进行了比较。AJCC N分期、甲状腺体积和特定剂量-体积参数被确定为潜在的预测因素。DeepSurv 模型的表现优于传统模型(C 指数:DeepSurv 0.75,传统模型 ≤ 0.63)。虽然其他模型在治疗后的早期区间具有竞争力,但随着时间的推移,深度学习模型表现出更优越的性能。校准和决策曲线分析证实了 DeepSurv 预测能力的增强。结论与传统模型相比,DeepSurv 对鼻咽癌患者 RIHT 的预测性能更优越。基于深度学习的预测具有很高的准确性,可以实现个性化的患者管理,在降低 RIHT 风险方面具有很大的潜力。这些研究结果表明,将此类模型纳入临床实践可能有利于RITH的管理。
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来源期刊
Clinical and Translational Radiation Oncology
Clinical and Translational Radiation Oncology Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
5.30
自引率
3.20%
发文量
114
审稿时长
40 days
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