Application of deep learning radiomics in oral squamous cell carcinoma-Extracting more information from medical images using advanced feature analysis.

Autor: Li C; Oncological Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Xinjiang Medical University, School / Hospital of Stomatology. Urumqi 830054, PR China; Stomatological Research Institute of Xinjiang Uygur Autonomous Region. Urumqi 830054, PR China; Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, School of Stomatology, Tongji Medical College, Union Hospital, Huazhong University of Science and Technology, Wuhan 430022, PR China. Electronic address: lichenxiuke@gmail.com., Chen X; College of Information Science and Engineering, Xinjiang University. Urumqi 830008, PR China., Chen C; College of Software, Xinjiang University. Urumqi 830046, PR China., Gong Z; Oncological Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Xinjiang Medical University, School / Hospital of Stomatology. Urumqi 830054, PR China; Stomatological Research Institute of Xinjiang Uygur Autonomous Region. Urumqi 830054, PR China. Electronic address: gzc740904@xjmu.edu.cn., Pataer P; Oncological Department of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Xinjiang Medical University, School / Hospital of Stomatology. Urumqi 830054, PR China., Liu X; Department of Maxillofacial Surgery, Hospital of Stomatology, Key Laboratory of Dental-Maxillofacial Reconstruction and Biological Intelligence Manufacturing of Gansu Province, Faculty of Dentistry, Lanzhou University. Lanzhou 730013, PR China., Lv X; College of Information Science and Engineering, Xinjiang University. Urumqi 830008, PR China; College of Software, Xinjiang University. Urumqi 830046, PR China.
Jazyk: angličtina
Zdroj: Journal of stomatology, oral and maxillofacial surgery [J Stomatol Oral Maxillofac Surg] 2024 Jun; Vol. 125 (3S), pp. 101840. Date of Electronic Publication: 2024 Mar 26.
DOI: 10.1016/j.jormas.2024.101840
Abstrakt: Objective: To conduct a systematic review with meta-analyses to assess the recent scientific literature addressing the application of deep learning radiomics in oral squamous cell carcinoma (OSCC).
Materials and Methods: Electronic and manual literature retrieval was performed using PubMed, Web of Science, EMbase, Ovid-MEDLINE, and IEEE databases from 2012 to 2023. The ROBINS-I tool was used for quality evaluation; random-effects model was used; and results were reported according to the PRISMA statement.
Results: A total of 26 studies involving 64,731 medical images were included in quantitative synthesis. The meta-analysis showed that, the pooled sensitivity and specificity were 0.88 (95 %CI: 0.87∼0.88) and 0.80 (95 %CI: 0.80∼0.81), respectively. Deeks' asymmetry test revealed there existed slight publication bias (P = 0.03).
Conclusions: The advances in the application of radiomics combined with learning algorithm in OSCC were reviewed, including diagnosis and differential diagnosis of OSCC, efficacy assessment and prognosis prediction. The demerits of deep learning radiomics at the current stage and its future development direction aimed at medical imaging diagnosis were also summarized and analyzed at the end of the article.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Masson SAS. All rights reserved.)
Databáze: MEDLINE
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