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Risk Prediction Model for Lung Cancer Screening
DC Field | Value | Language |
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dc.contributor.author | Kim, TJ | - |
dc.contributor.author | Kim, HY | - |
dc.contributor.author | Goo, JM | - |
dc.contributor.author | Sun, JS | - |
dc.date.accessioned | 2022-01-14T05:18:26Z | - |
dc.date.available | 2022-01-14T05:18:26Z | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 1738-2637 | - |
dc.identifier.uri | http://repository.ajou.ac.kr/handle/201003/20063 | - |
dc.description.abstract | Lung cancer screening in high-risk subjects using low-dose CT can reduce mortality by 20%. Current evidence suggests that the development of a risk prediction model for lung cancer is one of the major advances in lung cancer screening. Herein, we review the technical requirements for evaluating different risk prediction models. Moreover, we describe the major lung cancer risk prediction models reported, and the results of lung cancer screening using these models. | - |
dc.description.abstract | 폐암 고위험군에서 저선량 전산화단층촬영을 이용한 폐암검진은 폐암으로 인한 사망률을 20%까지 줄일 수 있다. 최근까지 보고된 여러 연구 결과들은 폐암 위험예측모델의 개발이 폐암검진의 주요 발전 중 하나임을 시사한다. 본 기고에서는 위험예측모델을 평가하기 위한 기술적 요구 사항을 검토하고, 지금까지 보고된 주요 폐암 위험예측모델과 이 모델을 적용한 폐암검진 결과를 소개하고자 한다. | - |
dc.title | Risk Prediction Model for Lung Cancer Screening | - |
dc.title.alternative | 폐암검진에서의 위험예측모델 | - |
dc.type | Article | - |
dc.subject.keyword | Risk Assessment | - |
dc.subject.keyword | Lung Cancer | - |
dc.subject.keyword | Screening | - |
dc.subject.keyword | Computed Tomography, X-Ray | - |
dc.contributor.affiliatedAuthor | Sun, JS | - |
dc.type.local | Journal Papers | - |
dc.identifier.doi | 10.3348/jksr.2019.80.5.860 | - |
dc.citation.title | Journal of the Korean Radiological Society | - |
dc.citation.volume | 80 | - |
dc.citation.number | 5 | - |
dc.citation.date | 2019 | - |
dc.citation.startPage | 860 | - |
dc.citation.endPage | 871 | - |
dc.identifier.bibliographicCitation | Journal of the Korean Radiological Society, 80(5). : 860-871, 2019 | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.identifier.eissn | 2288-2928 | - |
dc.relation.journalid | J017382637 | - |
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