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Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment
DC Field | Value | Language |
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dc.contributor.author | 김준원 | - |
dc.contributor.author | 박미나 | - |
dc.contributor.author | 서상현 | - |
dc.contributor.author | 안성준 | - |
dc.contributor.author | 이서영 | - |
dc.contributor.author | 주비오 | - |
dc.date.accessioned | 2024-04-11T06:30:04Z | - |
dc.date.available | 2024-04-11T06:30:04Z | - |
dc.date.issued | 2024-01 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/198806 | - |
dc.description.abstract | Purpose/objective(s): Previous deep learning (DL) algorithms for brain metastasis (BM) detection and segmentation have not been commonly used in clinics because they produce false-positive findings, require multiple sequences, and do not reflect physiological properties such as necrosis. The aim of this study was to develop a more clinically favorable DL algorithm (RLK-Unet) using a single sequence reflecting necrosis and apply it to automated treatment response assessment. Methods and materials: A total of 128 patients with 1339 BMs, who underwent BM magnetic resonance imaging using the contrast-enhanced 3D T1 weighted (T1WI) turbo spin-echo black blood sequence, were included in the development of the DL algorithm. Fifty-eight patients with 629 BMs were assessed for treatment response. The detection sensitivity, precision, Dice similarity coefficient (DSC), and agreement of treatment response assessments between neuroradiologists and RLK-Unet were assessed. Results: RLK-Unet demonstrated a sensitivity of 86.9% and a precision of 79.6% for BMs and had a DSC of 0.663. Segmentation performance was better in the subgroup with larger BMs (DSC, 0.843). The agreement in the response assessment for BMs between the radiologists and RLK-Unet was excellent (intraclass correlation, 0.84). Conclusion: RLK-Unet yielded accurate detection and segmentation of BM and could assist clinicians in treatment response assessment. Copyright © 2024 Son, Joo, Park, Suh, Oh, Kim, Lee, Ahn and Lee. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Frontiers Research Foundation | - |
dc.relation.isPartOf | FRONTIERS IN ONCOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiation Oncology (방사선종양학교실) | - |
dc.contributor.googleauthor | Seungyeon Son | - |
dc.contributor.googleauthor | Bio Joo | - |
dc.contributor.googleauthor | Mina Park | - |
dc.contributor.googleauthor | Sang Hyun Suh | - |
dc.contributor.googleauthor | Hee Sang Oh | - |
dc.contributor.googleauthor | Jun Won Kim | - |
dc.contributor.googleauthor | Seoyoung Lee | - |
dc.contributor.googleauthor | Sung Jun Ahn | - |
dc.contributor.googleauthor | Jong-Min Lee | - |
dc.identifier.doi | 10.3389/fonc.2023.1273013 | - |
dc.contributor.localId | A00958 | - |
dc.contributor.localId | A01460 | - |
dc.contributor.localId | A01886 | - |
dc.contributor.localId | A02237 | - |
dc.contributor.localId | A06098 | - |
dc.contributor.localId | A05842 | - |
dc.relation.journalcode | J03512 | - |
dc.identifier.eissn | 2234-943X | - |
dc.identifier.pmid | 38288101 | - |
dc.subject.keyword | brain metastasis | - |
dc.subject.keyword | deep learning algorithm | - |
dc.subject.keyword | detection | - |
dc.subject.keyword | segmentation | - |
dc.subject.keyword | treatment response | - |
dc.contributor.alternativeName | Kim, Jun Won | - |
dc.contributor.affiliatedAuthor | 김준원 | - |
dc.contributor.affiliatedAuthor | 박미나 | - |
dc.contributor.affiliatedAuthor | 서상현 | - |
dc.contributor.affiliatedAuthor | 안성준 | - |
dc.contributor.affiliatedAuthor | 이서영 | - |
dc.contributor.affiliatedAuthor | 주비오 | - |
dc.citation.volume | 13 | - |
dc.citation.startPage | 1273013 | - |
dc.identifier.bibliographicCitation | FRONTIERS IN ONCOLOGY, Vol.13 : 1273013, 2024-01 | - |
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