SYSTEMATIC REVIEW article
Front. Oncol.
Sec. Head and Neck Cancer
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1628999
This article is part of the Research TopicBased Models and Machine Learning on CT, MRI and PET-CT in Head and Neck Cancer Diagnosis, Staging and Outcome PredictionView all articles
Identifying sinonasal inverted papilloma by machine learning: a systematic review and meta-analysis
Provisionally accepted
- 1Binzhou Medical University, Yantai, China
- 2Liuzhou Traditional Chinese Medicine Hospital, Liuzhou, China
- 3Yantai Yuhuangding Hospital, Yantai, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background: Sinonasal inverted papilloma (IP) is a benign tumor of the sinonasal mucosa, which may become malignant. Machine learning (ML) has been applied to improve the accuracy in the diagnosis of various diseases, but no studies have evaluated the performance of ML for IP diagnosis. This systematic review and meta-analysis aimed to explore the diagnostic performance of ML for IP. Methods: We systematically searched articles from PubMed, Cochrane, Embase, and Web of Science up to July 22, 2025. The quality assessment of diagnostic accuracy studies tool (QUADAS-2) was used to assess the risk of bias, and the bivariate mixed-effect model was used for meta-analysis. Results: 17 studies involving 3321 participants were included. In the validation set, the sensitivity and specificity of ML constructed based on radiomics for identifying IP and malignant tumors were 0.84 (95%CI: 0.77-0.89) and 0.82 (95% CI: 0.74 ~ 0.88), respectively. The sensitivity and specificity of ML constructed based on radiomics and clinical features for identifying IP and malignant tumors were 0.85 (95%CI: 0.78-0.90) and 0.87 (95% CI: 0.80 ~ 0.92), respectively. Conclusion: Our study shows that ML has a favorable performance in the differential diagnosis of IP. More prospective studies are needed to validate and develop universal tools.
Keywords: machine learning, Meta-analysis, Radiomics, Sinonasal inverted papilloma, Systematic review
Received: 15 May 2025; Accepted: 11 Aug 2025.
Copyright: © 2025 Qin, Shi, Zhao, Zhang, Liu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xueyan Liu, Yantai Yuhuangding Hospital, Yantai, China
Li Wang, Yantai Yuhuangding Hospital, Yantai, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.