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European Archives of Oto-Rhino-Laryngology
Published in: European Archives of Oto-Rhino-Laryngology 11/2023

26-06-2023 | Head and Neck

A deep learning MRI-based signature may provide risk-stratification strategies for nasopharyngeal carcinoma

Authors: Chen Yang, Yuan Chen, Luchao Zhu, Liansheng Wang, Qin Lin

Published in: European Archives of Oto-Rhino-Laryngology | Issue 11/2023

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Abstract

Objective

As the prognosis of nasopharyngeal carcinoma (NPC) is influenced by various factors, making it difficult for clinical physicians to predict the outcome, the objective of this study was to develop a deep learning-based signature for risk stratification in NPC patients.

Methods

A total of 293 patients were enrolled in the study and divided into training, validation, and testing groups with a ratio of 7:1:2. MRI scans and corresponding clinical information were collected, and the 3-year disease-free survival (DFS) was chosen as the endpoint. The Res-Net18 algorithm was used to develop two deep learning (DL) models and another solely based on clinical characteristics developed by multivariate cox analysis. The performance of both models was evaluated using the area under the curve (AUC) and the concordance index (C-index). Discriminative performance was assessed using Kaplan–Meier survival analysis.

Results

The deep learning approach identified DL prognostic models. The MRI-based DL model showed significantly better performance compared to the traditional model solely based on clinical characteristics (AUC: 0.8861 vs 0.745, p = 0.04 and C-index: 0.865 vs 0.727, p = 0.03). The survival analysis showed significant survival differences between the risk groups identified by the MRI-based model.

Conclusion

Our study highlights the potential of MRI in predicting the prognosis of NPC through DL algorithm. This approach has the potential to become a novel tool for prognosis prediction and can help physicians to develop more valid treatment strategies in the future.
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Metadata
Title
A deep learning MRI-based signature may provide risk-stratification strategies for nasopharyngeal carcinoma
Authors
Chen Yang
Yuan Chen
Luchao Zhu
Liansheng Wang
Qin Lin
Publication date
26-06-2023
Publisher
Springer Berlin Heidelberg
Published in
European Archives of Oto-Rhino-Laryngology / Issue 11/2023
Print ISSN: 0937-4477
Electronic ISSN: 1434-4726
DOI
https://doi.org/10.1007/s00405-023-08084-9

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