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Added prognostic value of 3D deep learning-derived features from preoperative MRI for adult-type diffuse gliomas
DC Field | Value | Language |
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dc.contributor.author | 안성수 | - |
dc.date.accessioned | 2024-05-23T03:19:57Z | - |
dc.date.available | 2024-05-23T03:19:57Z | - |
dc.date.issued | 2024-03 | - |
dc.identifier.issn | 1522-8517 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/199204 | - |
dc.description.abstract | Background. To investigate the prognostic value of spatial features from whole-brain MRI using a three-dimensional (3D) convolutional neural network for adult-type diffuse gliomas. Methods. In a retrospective, multicenter study, 1925 diffuse glioma patients were enrolled from 5 datasets: SNUH (n = 708), UPenn (n = 425), UCSF (n = 500), TCGA (n = 160), and Severance (n = 132). The SNUH and Severance datasets served as external test sets. Precontrast and postcontrast 3D T1-weighted, T2-weighted, and T2-FLAIR images were processed as multichannel 3D images. A 3D-adapted SE-ResNeXt model was trained to predict overall survival. The prognostic value of the deep learning-based prognostic index (DPI), a spatial feature-derived quantitative score, and established prognostic markers were evaluated using Cox regression. Model evaluation was performed using the concordance index (C-index) and Brier score. Results: The MRI-only median DPI survival prediction model achieved C-indices of 0.709 and 0.677 (BS = 0.142 and 0.215) and survival differences (P < 0.001 and P = 0.002; log-rank test) for the SNUH and Severance datasets, respectively. Multivariate Cox analysis revealed DPI as a significant prognostic factor, independent of clinical and molecular genetic variables: hazard ratio = 0.032 and 0.036 (P < 0.001 and P = 0.004) for the SNUH and Severance datasets, respectively. Multimodal prediction models achieved higher C-indices than models using only clinical and molecular genetic variables: 0.783 vs. 0.774, P = 0.001, SNUH; 0.766 vs. 0.748, P = 0.023, Severance. Conclusions: The global morphologic feature derived from 3D CNN models using whole-brain MRI has independent prognostic value for diffuse gliomas. Combining clinical, molecular genetic, and imaging data yields the best performance. © The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Oxford University Press | - |
dc.relation.isPartOf | NEURO-ONCOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Adult | - |
dc.subject.MESH | Brain Neoplasms* / diagnostic imaging | - |
dc.subject.MESH | Brain Neoplasms* / genetics | - |
dc.subject.MESH | Deep Learning* | - |
dc.subject.MESH | Glioma* / diagnostic imaging | - |
dc.subject.MESH | Glioma* / genetics | - |
dc.subject.MESH | Glioma* / surgery | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Magnetic Resonance Imaging / methods | - |
dc.subject.MESH | Prognosis | - |
dc.subject.MESH | Retrospective Studies | - |
dc.title | Added prognostic value of 3D deep learning-derived features from preoperative MRI for adult-type diffuse gliomas | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Jung Oh Lee | - |
dc.contributor.googleauthor | Sung Soo Ahn | - |
dc.contributor.googleauthor | Kyu Sung Choi | - |
dc.contributor.googleauthor | Junhyeok Lee | - |
dc.contributor.googleauthor | Joon Jang | - |
dc.contributor.googleauthor | Jung Hyun Park | - |
dc.contributor.googleauthor | Inpyeong Hwang | - |
dc.contributor.googleauthor | Chul-Kee Park | - |
dc.contributor.googleauthor | Sung Hye Park | - |
dc.contributor.googleauthor | Jin Wook Chung | - |
dc.contributor.googleauthor | Seung Hong Choi | - |
dc.identifier.doi | 10.1093/neuonc/noad202 | - |
dc.contributor.localId | A02234 | - |
dc.relation.journalcode | J02346 | - |
dc.identifier.eissn | 1523-5866 | - |
dc.identifier.pmid | 37855826 | - |
dc.identifier.url | https://academic.oup.com/neuro-oncology/article/26/3/571/7322058 | - |
dc.subject.keyword | Deep learning | - |
dc.subject.keyword | Glioblastoma | - |
dc.subject.keyword | Isocitrate dehydrogenase | - |
dc.subject.keyword | Magnetic resonance imaging | - |
dc.subject.keyword | Survival analysis | - |
dc.contributor.alternativeName | Ahn, Sung Soo | - |
dc.contributor.affiliatedAuthor | 안성수 | - |
dc.citation.volume | 26 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 571 | - |
dc.citation.endPage | 580 | - |
dc.identifier.bibliographicCitation | NEURO-ONCOLOGY, Vol.26(3) : 571-580, 2024-03 | - |
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