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A deep learning-based model predicts survival for patients with laryngeal squamous cell carcinoma: a large population-based study

  • Laryngology
  • Published:
European Archives of Oto-Rhino-Laryngology Aims and scope Submit manuscript

Abstract

Objectives

To assess the performance of DeepSurv, a deep learning-based model in the survival prediction of laryngeal squamous cell carcinoma (LSCC) using the Surveillance, Epidemiology, and End Results (SEER) database.

Methods

In this large population-based study, we developed and validated a deep learning survival neural network using pathologically diagnosed patients with LSCC from the SEER database between January 2010 and December 2018. Totally 13 variables were included in this network, including patients baseline characteristics, stage, grade, site, tumor extension and treatment details. Based on the total risk score derived from this algorithm, a three-knot restricted cubic spline was plotted to exhibit the difference of survival benefits from two treatment modalities.

Results

Totally 6316 patients with LSCC were included in the study, of which 4237 cases diagnosed between 2010 and 2015 were selected as the development cohort, and the rest (2079 cases diagnosed from 2016 to 2018) were the validation cohort. A state-of-the-art deep learning-based model based on 23 features (i.e., 13 variables) was generated, which showed more superior performance in the prediction of overall survival (OS) than the tumor, node, and metastasis (TNM) staging system (C-index for DeepSurv vs TNM staging = 0.71; 95% CI 0.69–0.74 vs 0.61; 95% CI 0.60–0.63). Interestingly, a significantly nonlinear association between total risk score and treatment effectiveness was observed. When the total risk score ranges 0.1–1.5, surgical treatment brought more survival benefits than nonsurgical one for LSCC patients, especially in 70.5% of patients staged III–IV.

Conclusions

The deep learning-based model shows more potential benefits in survival estimation for patients with LSCC, which may potentially serve as an auxiliary approach to provide reliable treatment recommendations.

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Funding

This research was funded by the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences under Grant 2017PT32013.

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Authors and Affiliations

  1. Sichuan Provincial Center for Mental Health, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu, 610072, China

    Fang Liao & Jinyu Wang

  2. Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu, 610072, China

    Fang Liao & Jinyu Wang

  3. West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, China

    Wei Wang

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  1. Fang Liao
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  2. Wei Wang
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Correspondence to Jinyu Wang.

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Liao, F., Wang, W. & Wang, J. A deep learning-based model predicts survival for patients with laryngeal squamous cell carcinoma: a large population-based study. Eur Arch Otorhinolaryngol 280, 789–795 (2023). https://doi.org/10.1007/s00405-022-07627-w

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  • DOI: https://doi.org/10.1007/s00405-022-07627-w

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