Prediction of prognosis in T4 or N3 locally advanced nasopharyngeal carcinoma receiving chemoradiotherapy using machine learning methods

TL;DR

This study uses machine learning to predict survival outcomes for patients with advanced nasopharyngeal cancer undergoing chemoradiotherapy.

Contribution

A novel survival prediction model using LASSO and Cox regression for T4 or N3 nasopharyngeal carcinoma patients is developed and validated.

Findings

The model achieved high discrimination with AUC values of 0.802, 0.709, and 0.686 at 1, 2, and 3 years.

N stage and Epstein-Barr virus levels were identified as key prognostic factors.

Abstract

This study aims to develop and validate a survival prediction model for T4 or N3 locally advanced nasopharyngeal carcinoma (NPC) patients undergoing chemoradiotherapy (CRT) using machine learning methods.

A total of 293 patients with locally advanced NPC (T4 or N3 stage) treated with CRT were included in the study. The cohort was divided into a training set (173 patients) and a validation set (120 patients). LASSO regression was used to identify significant prognostic factors, and Cox regression analysis was performed to assess the independent impact of these factors on progression-free survival (PFS). A nomogram was constructed based on the identified prognostic factors to predict 1-, 2-, and 3-year PFS. Model performance was validated using ROC curves, calibration curves, and decision curve analysis (DCA).

The training cohort showed 1-, 2-, and 3-year PFS rates of 92.4%, 81.3%, and 75.2%, respectively. In the validation cohort, the 1-, 2-, and 3-year PFS rates were 90.1%, 83.5%, and 76.0%, respectively, with no significant differences between the groups (P = 0.94). The LASSO-Cox model identified N stage and Epstein-Barr virus (EBV) levels as key prognostic factors. The nomogram demonstrated good discrimination with AUC values of 0.802, 0.709, and 0.686 at 1, 2, and 3 years, respectively. The ROC curve shows the model’s performance with AUC values at 1 year (0.802), 2 years (0.709), and 3 years (0.686), demonstrating the model’s ability to distinguish between different survival outcomes. The calibration curves and DCA confirmed the model’s good agreement with observed outcomes and its clinical net benefit across different risk thresholds.

The survival prediction model based on LASSO and Cox regression provides a robust and interpretable tool for predicting PFS in patients with T4 or N3 locally advanced NPC undergoing CRT.

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