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A deep learning and radiomics fusion model based on contrast-enhanced computer tomography improves preoperative identification of cervical lymph node metastasis of oral squamous cell carcinoma

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Abstract

Objectives

In this study, we constructed and validated models based on deep learning and radiomics to facilitate preoperative diagnosis of cervical lymph node metastasis (LNM) using contrast-enhanced computed tomography (CECT).

Materials and methods

CECT scans of 100 patients with OSCC (217 metastatic and 1973 non-metastatic cervical lymph nodes: development set, 76 patients; internally independent test set, 24 patients) who received treatment at the Peking University School and Hospital of Stomatology between 2012 and 2016 were retrospectively collected. Clinical diagnoses and pathological findings were used to establish the gold standard for metastatic cervical LNs. A reader study with two clinicians was also performed to evaluate the lymph node status in the test set. The performance of the proposed models and the clinicians was evaluated and compared by measuring using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE).

Results

A fusion model combining deep learning with radiomics showed the best performance (ACC, 89.2%; SEN, 92.0%; SPE, 88.9%; and AUC, 0.950 [95% confidence interval: 0.908–0.993, P < 0.001]) in the test set. In comparison with the clinicians, the fusion model showed higher sensitivity (92.0 vs. 72.0% and 60.0%) but lower specificity (88.9 vs. 97.5% and 98.8%).

Conclusion

A fusion model combining radiomics and deep learning approaches outperformed other single-technique models and showed great potential to accurately predict cervical LNM in patients with OSCC.

Clinical relevance

The fusion model can complement the preoperative identification of LNM of OSCC performed by the clinicians.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We would like to thank Dawei Wang and Weixiong Tan of Huafang Hanying Medical Technology Co., Ltd (Beijing, PR China) for artificial intelligence modeling technical support, and we also thank the native English speaking scientists of Elixigen Company (Huntington Beach, California) for editing our manuscript.

Funding

This work was supported by Program of the new clinical techniques of Peking University School and Hospital of Stomatology [Grant Number: PKUSSNCT-20A05].

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Author notes

  1. Wenbo Zhang and Xin Peng contributed to the work equally and should be regarded as co-corresponding authors.

Authors and Affiliations

  1. Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People’s Republic of China

    Zhen Chen, Yao Yu, Shuo Liu, Wen Du, Leihao Hu, Congwei Wang, Jiaqi Li, Wenbo Zhang & Xin Peng

  2. Huafang Hanying Medical Technology Co., Ltd, No.19, West Bridge Road, Miyun District, Beijing, 101520, People’s Republic of China

    Jianbo Liu

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  1. Zhen Chen
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Contributions

Zhen Chen, Wenbo Zhang, and Xin Peng contributed to the conception and design of the study. Yao Yu, Shuo Liu, Congwei Wang, and Jiaqi Li collected the data and performed the statistical analysis. Jianbo Liu, Wen Du, and Leihao Hu performed the literature search and interpretation of data collected. Zhen Chen performed drafting of article and critical revision. All authors have approved the final version to be published.

Corresponding author

Correspondence to Xin Peng.

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Competing interests

The authors declare no competing interests.

Ethical approval

The study was reviewed and approved by the Institutional Review Board of Peking University School of Stomatology. All procedures performed were in accordance with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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The authors declare no competing interests.

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Chen, Z., Yu, Y., Liu, S. et al. A deep learning and radiomics fusion model based on contrast-enhanced computer tomography improves preoperative identification of cervical lymph node metastasis of oral squamous cell carcinoma. Clin Oral Invest 28, 39 (2024). https://doi.org/10.1007/s00784-023-05423-2

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