Global burden and future trends of head and neck cancer: a deep learning-based analysis (1980–2030)

The current work offers a detailed analysis of the global burden of HNC based on the GBD 2021 dataset and demonstrates the accuracy of the HNCP-T model in predicting future trends. Significant regional and gender-based differences are found, with incidence rates rising, especially among women and in low-middle SDI regions. Furthermore, the results underscore the value of deep learning models in disease burden prediction, which can outperform traditional methods.

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Publication Details

Journal
PLoS ONE
Published
2025-04-09
DOI
https://doi.org/10.1371/journal.pone.0320184
Citations
9
Primary Topic
Global Cancer Incidence and Screening
Type
article
Field-Weighted Citation Impact
9.41
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Global burden and future trends of head and neck cancer: a deep learning-based analysis (1980–2030)

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9 citations
PLoS ONE
Global Cancer Incidence and Screening
9.41
article

Global burden and future trends of head and neck cancer: a deep learning-based analysis (1980–2030)

Shuai Lv, Jinyu Mei, Xinyu Wang, Wei Gong, Linqiang Pan, Qiancheng Hu
article en
9 citations

Abstract

The current work offers a detailed analysis of the global burden of HNC based on the GBD 2021 dataset and demonstrates the accuracy of the HNCP-T model in predicting future trends. Significant regional and gender-based differences are found, with incidence rates rising, especially among women and in low-middle SDI regions. Furthermore, the results underscore the value of deep learning models in disease burden prediction, which can outperform traditional methods.

PLoS ONEVol. 20(4)
University of Science and Technology of China (CN), Anhui Medical University (CN), Second Affiliated Hospital of Anhui Medical University (CN)
Openalex Percentile: Top 2%
Global Cancer Incidence and Screening
9.41
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