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19.10.2023 | Review Article

Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis

verfasst von: Rata Rokhshad, Seyyede Niloufar Salehi, Amirmohammad Yavari, Parnian Shobeiri, Mahdieh Esmaeili, Nisha Manila, Saeed Reza Motamedian, Hossein Mohammad-Rahimi

Erschienen in: Oral Radiology | Ausgabe 1/2024

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Abstract

Purpose

This study aims to review deep learning applications for detecting head and neck cancer (HNC) using magnetic resonance imaging (MRI) and radiographic data.

Methods

Through January 2023, a PubMed, Scopus, Embase, Google Scholar, IEEE, and arXiv search were carried out. The inclusion criteria were implementing head and neck medical images (computed tomography (CT), positron emission tomography (PET), MRI, Planar scans, and panoramic X-ray) of human subjects with segmentation, object detection, and classification deep learning models for head and neck cancers. The risk of bias was rated with the quality assessment of diagnostic accuracy studies (QUADAS-2) tool. For the meta-analysis diagnostic odds ratio (DOR) was calculated. Deeks’ funnel plot was used to assess publication bias. MIDAS and Metandi packages were used to analyze diagnostic test accuracy in STATA.

Results

From 1967 studies, 32 were found eligible after the search and screening procedures. According to the QUADAS-2 tool, 7 included studies had a low risk of bias for all domains. According to the results of all included studies, the accuracy varied from 82.6 to 100%. Additionally, specificity ranged from 66.6 to 90.1%, sensitivity from 74 to 99.68%. Fourteen studies that provided sufficient data were included for meta-analysis. The pooled sensitivity was 90% (95% CI 0.820.94), and the pooled specificity was 92% (CI 95% 0.87–0.96). The DORs were 103 (27–251). Publication bias was not detected based on the p-value of 0.75 in the meta-analysis.

Conclusion

With a head and neck screening deep learning model, detectable screening processes can be enhanced with high specificity and sensitivity.

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Titel: Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis
verfasst von: Rata Rokhshad
Seyyede Niloufar Salehi
Amirmohammad Yavari
Parnian Shobeiri
Mahdieh Esmaeili
Nisha Manila
Saeed Reza Motamedian
Hossein Mohammad-Rahimi
Publikationsdatum: 19.10.2023
Verlag: Springer Nature Singapore
Erschienen in: Oral Radiology / Ausgabe 1/2024
Print ISSN: 0911-6028
Elektronische ISSN: 1613-9674
DOI: https://doi.org/10.1007/s11282-023-00715-5

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Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis