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

IF 1.6 3区医学 Q3 DENTISTRY, ORAL SURGERY & MEDICINE

Oral Radiology Pub Date : 2024-01-01 Epub Date: 2023-10-19 DOI:10.1007/s11282-023-00715-5

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

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引用次数: 0

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.

Abstract Image

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通过放射学数据诊断头颈癌的深度学习：一项系统综述和荟萃分析。

目的：本研究旨在回顾利用磁共振成像（MRI）和放射学数据检测癌症（HNC）的深度学习应用。方法：至2023年1月，进行PubMed、Scopus、Embase、Google Scholar、IEEE和arXiv搜索。纳入标准是实施人类受试者的头颈部医学图像（计算机断层扫描（CT）、正电子发射断层扫描（PET）、MRI、平面扫描和全景X射线），以及头颈癌的分割、对象检测和分类深度学习模型。使用诊断准确性研究质量评估（QUADAS-2）工具对偏倚风险进行评级。对于荟萃分析，计算诊断优势比（DOR）。Deeks漏斗图用于评估发表偏倚。MIDAS和Metandi软件包用于分析STATA中诊断测试的准确性。结果：从1967项研究中，有32项在经过搜索和筛选程序后符合条件。根据QUADAS-2工具，7项纳入的研究在所有领域都具有较低的偏倚风险。根据所有纳入研究的结果，准确率在82.6%到100%之间。此外，特异性在66.6%至90.1%之间，敏感性在74%至99.68%之间。14项提供足够数据的研究被纳入荟萃分析。合并敏感性为90%（95%CI 0.820.94），合并特异性为92%（CI 95%0.87-0.96）。DORs为103（27-251）。基于荟萃分析中0.75的p值，未检测到发表偏倚。结论：使用头颈部筛查深度学习模型，可以以高特异性和敏感性增强可检测的筛查过程。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Oral Radiology DENTISTRY, ORAL SURGERY & MEDICINE-

CiteScore

4.20

自引率

13.60%

发文量

审稿时长

>12 weeks

期刊介绍： As the official English-language journal of the Japanese Society for Oral and Maxillofacial Radiology and the Asian Academy of Oral and Maxillofacial Radiology, Oral Radiology is intended to be a forum for international collaboration in head and neck diagnostic imaging and all related fields. Oral Radiology features cutting-edge research papers, review articles, case reports, and technical notes from both the clinical and experimental fields. As membership in the Society is not a prerequisite, contributions are welcome from researchers and clinicians worldwide.