Identifying sinonasal inverted papilloma by machine learning: a systematic review and meta-analysis
BackgroundSinonasal 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 systemati...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1628999/full |
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| author | Xianfei Qin Xianfei Qin Jinping Shi Xiangkun Zhao Yu Zhang Xueyan Liu Li Wang |
| author_facet | Xianfei Qin Xianfei Qin Jinping Shi Xiangkun Zhao Yu Zhang Xueyan Liu Li Wang |
| author_sort | Xianfei Qin |
| collection | DOAJ |
| description | BackgroundSinonasal 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.MethodsWe 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.Results17 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.ConclusionOur 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.Systemic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42023430417, identifier CRD42023430417. |
| format | Article |
| id | doaj-art-e14b373959614e7c92f54c6a166de27a |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-e14b373959614e7c92f54c6a166de27a2025-08-26T04:12:48ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-08-011510.3389/fonc.2025.16289991628999Identifying sinonasal inverted papilloma by machine learning: a systematic review and meta-analysisXianfei Qin0Xianfei Qin1Jinping Shi2Xiangkun Zhao3Yu Zhang4Xueyan Liu5Li Wang6The Second School of Clinical Medicine, Binzhou Medical University, Yantai, Shandong, ChinaLiuzhou Traditional Chinese Medicine Hospital, Liuzhou, Guangxi, ChinaOtorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Yantai, Shandong, ChinaThe Second School of Clinical Medicine, Binzhou Medical University, Yantai, Shandong, ChinaOtorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Yantai, Shandong, ChinaOtorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Yantai, Shandong, ChinaOtorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Yantai, Shandong, ChinaBackgroundSinonasal 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.MethodsWe 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.Results17 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.ConclusionOur 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.Systemic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42023430417, identifier CRD42023430417.https://www.frontiersin.org/articles/10.3389/fonc.2025.1628999/fullmachine learningmeta-analysisradiomicssinonasal inverted papillomasystematic review |
| spellingShingle | Xianfei Qin Xianfei Qin Jinping Shi Xiangkun Zhao Yu Zhang Xueyan Liu Li Wang Identifying sinonasal inverted papilloma by machine learning: a systematic review and meta-analysis Frontiers in Oncology machine learning meta-analysis radiomics sinonasal inverted papilloma systematic review |
| title | Identifying sinonasal inverted papilloma by machine learning: a systematic review and meta-analysis |
| title_full | Identifying sinonasal inverted papilloma by machine learning: a systematic review and meta-analysis |
| title_fullStr | Identifying sinonasal inverted papilloma by machine learning: a systematic review and meta-analysis |
| title_full_unstemmed | Identifying sinonasal inverted papilloma by machine learning: a systematic review and meta-analysis |
| title_short | Identifying sinonasal inverted papilloma by machine learning: a systematic review and meta-analysis |
| title_sort | identifying sinonasal inverted papilloma by machine learning a systematic review and meta analysis |
| topic | machine learning meta-analysis radiomics sinonasal inverted papilloma systematic review |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1628999/full |
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