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MRI radiomics may predict early tumor recurrence in patients with sinonasal squamous cell carcinoma
DC Field | Value | Language |
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dc.contributor.author | 금기창 | - |
dc.contributor.author | 김진아 | - |
dc.contributor.author | 박채정 | - |
dc.contributor.author | 안성수 | - |
dc.contributor.author | 최서희 | - |
dc.contributor.author | 최은창 | - |
dc.contributor.author | 한경화 | - |
dc.date.accessioned | 2024-07-18T05:01:20Z | - |
dc.date.available | 2024-07-18T05:01:20Z | - |
dc.date.issued | 2024-05 | - |
dc.identifier.issn | 0938-7994 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/199983 | - |
dc.description.abstract | Objectives: Sinonasal squamous cell carcinoma (SCC) follows a poor prognosis with high tendency for local recurrence. We aimed to evaluate whether MRI radiomics can predict early local failure in sinonasal SCC. Methods: Sixty-eight consecutive patients with node-negative sinonasal SCC (January 2005-December 2020) were enrolled, allocated to the training (n = 47) and test sets (n = 21). Early local failure, which occurred within 12 months of completion of initial treatment, was the primary endpoint. For clinical features (age, location, treatment modality, and clinical T stage), binary logistic regression analysis was performed. For 186 extracted radiomic features, different feature selections and classifiers were combined to create two prediction models: (1) a pure radiomics model; and (2) a combined model with clinical features and radiomics. The areas under the receiver operating characteristic curves (AUCs) were calculated and compared using DeLong's method. Results: Early local failure occurred in 38.3% (18/47) and 23.8% (5/21) in the training and test sets, respectively. We identified several radiomic features which were strongly associated with early local failure. In the test set, both the best-performing radiomics model and the combined model (clinical + radiomic features) yielded higher AUCs compared to the clinical model (AUC, 0.838 vs. 0.438, p = 0.020; 0.850 vs. 0.438, p = 0.016, respectively). The performances of the best-performing radiomics model and the combined model did not differ significantly (AUC, 0.838 vs. 0.850, p = 0.904). Conclusion: MRI radiomics integrated with a machine learning classifier may predict early local failure in patients with sinonasal SCC. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Springer International | - |
dc.relation.isPartOf | EUROPEAN RADIOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Adult | - |
dc.subject.MESH | Aged | - |
dc.subject.MESH | Carcinoma, Squamous Cell* / diagnostic imaging | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Magnetic Resonance Imaging* / methods | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Neoplasm Recurrence, Local* / diagnostic imaging | - |
dc.subject.MESH | Paranasal Sinus Neoplasms* / diagnostic imaging | - |
dc.subject.MESH | Predictive Value of Tests | - |
dc.subject.MESH | Prognosis | - |
dc.subject.MESH | Radiomics | - |
dc.subject.MESH | Retrospective Studies | - |
dc.title | MRI radiomics may predict early tumor recurrence in patients with sinonasal squamous cell carcinoma | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiation Oncology (방사선종양학교실) | - |
dc.contributor.googleauthor | Chae Jung Park | - |
dc.contributor.googleauthor | Seo Hee Choi | - |
dc.contributor.googleauthor | Dain Kim | - |
dc.contributor.googleauthor | Si Been Kim | - |
dc.contributor.googleauthor | Kyunghwa Han | - |
dc.contributor.googleauthor | Sung Soo Ahn | - |
dc.contributor.googleauthor | Won Hee Lee | - |
dc.contributor.googleauthor | Eun Chang Choi | - |
dc.contributor.googleauthor | Ki Chang Keum | - |
dc.contributor.googleauthor | Jinna Kim | - |
dc.identifier.doi | 10.1007/s00330-023-10389-6 | - |
dc.contributor.localId | A00272 | - |
dc.contributor.localId | A01022 | - |
dc.contributor.localId | A04942 | - |
dc.contributor.localId | A02234 | - |
dc.contributor.localId | A04867 | - |
dc.contributor.localId | A04161 | - |
dc.contributor.localId | A04267 | - |
dc.relation.journalcode | J00851 | - |
dc.identifier.eissn | 1432-1084 | - |
dc.identifier.pmid | 37926740 | - |
dc.identifier.url | https://link.springer.com/article/10.1007/s00330-023-10389-6 | - |
dc.subject.keyword | Magnetic resonance imaging | - |
dc.subject.keyword | Prognosis | - |
dc.subject.keyword | Radiomics | - |
dc.subject.keyword | Squamous cell carcinoma | - |
dc.contributor.alternativeName | Keum, Ki Chang | - |
dc.contributor.affiliatedAuthor | 금기창 | - |
dc.contributor.affiliatedAuthor | 김진아 | - |
dc.contributor.affiliatedAuthor | 박채정 | - |
dc.contributor.affiliatedAuthor | 안성수 | - |
dc.contributor.affiliatedAuthor | 최서희 | - |
dc.contributor.affiliatedAuthor | 최은창 | - |
dc.contributor.affiliatedAuthor | 한경화 | - |
dc.citation.volume | 34 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 3151 | - |
dc.citation.endPage | 3159 | - |
dc.identifier.bibliographicCitation | EUROPEAN RADIOLOGY, Vol.34(5) : 3151-3159, 2024-05 | - |
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