라디오믹스 기반 직장암 수술 위험도 예측을 위한 MRI 반자동 선택 바이오마커 검증 연구
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 백영서 | - |
dc.contributor.author | 김영재 | - |
dc.contributor.author | 전영배 | - |
dc.contributor.author | 황태식 | - |
dc.contributor.author | 백정흠 | - |
dc.contributor.author | 김광기 | - |
dc.date.accessioned | 2023-02-24T14:40:05Z | - |
dc.date.available | 2023-02-24T14:40:05Z | - |
dc.date.created | 2023-02-24 | - |
dc.date.issued | 2023-02 | - |
dc.identifier.issn | 1229-0807 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/86932 | - |
dc.description.abstract | Currently, studies to predict the risk of rectal cancer surgery select MRI image slices based on the clinical experience of surgeons. The purpose of this study is to semi-automatically select and classify 2D MRI image slides to predict the risk of rectal cancer surgery using biomarkers. The data used were retrospectively collected MRI imag- ing data of 50 patients who underwent laparoscopic surgery for rectal cancer at Gachon University Gil Medical Center. Expert-selected MRI image slices and non-selected slices were screened and radiomics was used to extract a total of 102 features. A total of 16 approaches were used, combining 4 classifiers and 4 feature selection methods. The combination of Random Forest and Ridge performed with a sensitivity of 0.83, a specificity of 0.88, an accuracy of 0.85, and an AUC of 0.89±0.09. Differences between expert-selected MRI image slices and non-selected slices were analyzed by extracting the top five significant features. Selected quantitative features help expedite decision making and improve efficiency in studies to predict risk of rectal cancer surgery. | - |
dc.language | 한국어 | - |
dc.language.iso | ko | - |
dc.publisher | 대한의용생체공학회 | - |
dc.relation.isPartOf | 의공학회지 | - |
dc.title | 라디오믹스 기반 직장암 수술 위험도 예측을 위한 MRI 반자동 선택 바이오마커 검증 연구 | - |
dc.title.alternative | A Study on MRI Semi-Automatically Selected Biomarkers for Predicting Risk of Rectal Cancer Surgery Based on Radiomics | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 2 | - |
dc.identifier.doi | 10.9718/JBER.2023.44.1.11 | - |
dc.identifier.bibliographicCitation | 의공학회지, v.44, no.1, pp.11 - 18 | - |
dc.identifier.kciid | ART002931072 | - |
dc.description.isOpenAccess | N | - |
dc.citation.endPage | 18 | - |
dc.citation.startPage | 11 | - |
dc.citation.title | 의공학회지 | - |
dc.citation.volume | 44 | - |
dc.citation.number | 1 | - |
dc.contributor.affiliatedAuthor | 백영서 | - |
dc.contributor.affiliatedAuthor | 김영재 | - |
dc.contributor.affiliatedAuthor | 전영배 | - |
dc.contributor.affiliatedAuthor | 황태식 | - |
dc.contributor.affiliatedAuthor | 백정흠 | - |
dc.contributor.affiliatedAuthor | 김광기 | - |
dc.subject.keywordAuthor | Rectal cancer | - |
dc.subject.keywordAuthor | Radiomics | - |
dc.subject.keywordAuthor | Biomarker | - |
dc.subject.keywordAuthor | Quantitative | - |
dc.description.journalRegisteredClass | kci | - |
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