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

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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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Availability of data and materials

All patients’ data were collected from The First Affiliated Hospital of Xiamen University. We will not disclose the information of patients due to the need of privacy protection, but researchers could obtain relative data by contacting Chen Yang (yc924844040@foxmail.com).

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Funding

This study was supported by the Natural Science Foundation of Fujian Province [Grant Nos. 2020J011220 and 2020J011236], the Key Medical and Health Projects in Xiamen (Grant No. 3502Z20209002), and the National Natural Science Foundation of China [Grant No. 81772893].

Author information

Authors and Affiliations

  1. Department of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China

    Chen Yang, Luchao Zhu & Qin Lin

  2. Department of Computer Science, Xiamen University, Xiamen, Fujian, China

    Yuan Chen & Liansheng Wang

Authors
  1. Chen Yang
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  2. Yuan Chen
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  3. Luchao Zhu
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Contributions

CY, QL, and LSW conceived the idea. CY and YC designed the study. LCZ collected the data. YC finished the content analysis. QL and YC helped editing the article pictures. CY and YC drafted the manuscript. QL and LSW reviewed and corrected the manuscript. CY and YC have contributed equally to this work.

Corresponding authors

Correspondence to Liansheng Wang or Qin Lin.

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Conflict of interest

All authors claim that there are no potential conflicts of interest in the study.

Ethical approval and consent to participate

The study was approved by The First Affiliated Hospital of Xiamen University Ethical Review Committee (XMCTRC-2022-01), and informed consent was not required due to its retrospective nature.

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All authors have given their consent for publication.

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Yang, C., Chen, Y., Zhu, L. et al. A deep learning MRI-based signature may provide risk-stratification strategies for nasopharyngeal carcinoma. Eur Arch Otorhinolaryngol 280, 5039–5047 (2023). https://doi.org/10.1007/s00405-023-08084-9

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