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

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.