Cited 9 times in
Radiomics and Deep Learning in Brain Metastases: Current Trends and Roadmap to Future Applications
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박예원 | - |
dc.contributor.author | 안성수 | - |
dc.contributor.author | 이승구 | - |
dc.contributor.author | 장종희 | - |
dc.date.accessioned | 2022-09-14T01:55:13Z | - |
dc.date.available | 2022-09-14T01:55:13Z | - |
dc.date.issued | 2021-12 | - |
dc.identifier.issn | 2384-1095 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/190669 | - |
dc.description.abstract | Advances in radiomics and deep learning (DL) hold great potential to be at the forefront of precision medicine for the treatment of patients with brain metastases. Radiomics and DL can aid clinical decision-making by enabling accurate diagnosis, facilitating the identification of molecular markers, providing accurate prognoses, and monitoring treatment response. In this review, we summarize the clinical background, unmet needs, and current state of research of radiomics and DL for the treatment of brain metastases. The promises, pitfalls, and future roadmap of radiomics and DL in brain metastases are addressed as well. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Korean Society of Magnetic Resonance in Medicine | - |
dc.relation.isPartOf | Investigative Magnetic Resonance Imaging | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Radiomics and Deep Learning in Brain Metastases: Current Trends and Roadmap to Future Applications | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Yae Won Park | - |
dc.contributor.googleauthor | Narae Lee | - |
dc.contributor.googleauthor | Sung Soo Ahn | - |
dc.contributor.googleauthor | Jong Hee Chang | - |
dc.contributor.googleauthor | Seung-Koo Lee | - |
dc.identifier.doi | 10.13104/imri.2021.25.4.266 | - |
dc.contributor.localId | A05330 | - |
dc.contributor.localId | A02234 | - |
dc.contributor.localId | A02912 | - |
dc.contributor.localId | A03470 | - |
dc.relation.journalcode | J01186 | - |
dc.identifier.eissn | 2384-1109 | - |
dc.subject.keyword | Artificial intelligence | - |
dc.subject.keyword | Brain metastases | - |
dc.subject.keyword | Deep learning | - |
dc.subject.keyword | Machine learning | - |
dc.subject.keyword | Radiomics | - |
dc.contributor.alternativeName | Park, Yae-Won | - |
dc.contributor.affiliatedAuthor | 박예원 | - |
dc.contributor.affiliatedAuthor | 안성수 | - |
dc.contributor.affiliatedAuthor | 이승구 | - |
dc.contributor.affiliatedAuthor | 장종희 | - |
dc.citation.volume | 25 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 266 | - |
dc.citation.endPage | 280 | - |
dc.identifier.bibliographicCitation | Investigative Magnetic Resonance Imaging, Vol.25(4) : 266-280, 2021-12 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.