Browsing by Yonsei Author : Yun, Mijin

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Name :
Yun, Mijin [윤미진]
orcid http://orcid.org/0000-0002-1712-163X
Department :
College of Medicine (의과대학) - Dept. of Nuclear Medicine (핵의학교실)
Scopus ID :
Scopus (7102397269)

교수 기본정보

세부 전공
핵의학
이메일
yunmijin@yuhs.ac

실험실 기본정보

이름
Nuclear Medicine & Artificial Intelligence Lab
위치
암병원 B501
구분
Wet Lab ( %), Dry Lab (100%)

연구소개

연구 내용
알츠하이머병(AD)의 진단에는 임상평가와 영상검사가 함께 활용되며, 특히 18F-FDG PET은 뇌 대사 저하 양상을 통해 신경퇴행성 치매의 감별에 도움을 준다. 그러나 실제 임상에서는 영상 소견만으로 진단을 확정하기 어렵거나, 경계 사례에서 판독자/환자 특성에 따라 분류 성능이 달라질 수 있다. 따라서 FDG-PET 기반 진단에서 임상 변수(인지기능 및 연령)를 결합했을 때 진단 정확도가 향상되는지 확인하는 것은 임상적 의의가 크다.
본 연구의 목적은 FDG-PET 단독 정보로 구성한 진단모형과 FDG-PET에 MMSE 점수 및 연령을 추가한 모형을 비교하여, 알츠하이머병 진단(또는 AD vs non-AD 치매/경도인지장애)의 분류 성능이 유의하게 향상되는지 평가하는 것이다.
연구는 단일기관 후향적 관찰연구로 계획한다. 일정 기간 동안 인지저하를 주소로 내원하여 FDG-PET을 시행한 환자를 대상으로 하며, 각 환자의 연령, 성별, MMSE(검사 시행 시점), 임상진단(또는 추적 진단/표준진단 기준)을 수집한다. FDG-PET은 정량 지표 또는 판독 결과로 변수화한다.
모형 1은 FDG-PET 변수만 포함하고, 모형 2는 FDG-PET + MMSE + 연령을 포함한다. 두 모형의 성능은 AUC(ROC), 민감도/특이도, 정확도, PPV/NPV 등을 산출하고, AUC 비교 및 필요 시 재분류 지표 등을 통해 추가 변수의 ‘incremental value’를 평가한다. 내부 검증은 k-fold 교차검증 또는 bootstrap을 활용한다.
본 연구를 통해 FDG-PET 기반 AD 진단에서 임상 변수의 결합이 실제로 예측력을 향상시키는지 확인하고, 경계 사례에서 보다 신뢰도 높은 임상 의사결정(검사 선택/추적/감별진단)에 기여할 근거를 마련하고자 한다.

학생 참여 정보

학생들이 참여하면 좋을 연구
  • FDG-PET 기반 알츠하이머병 진단모형에서 MMSE 점수와 연령 변수 추가의 진단 정확도 향상 효과 평가: 후향적 관찰연구
학생 참여 시 사용하게 되는 장비 및 프로그램
  • - 영상 데이터 접근/관리: 병원 EMR
  • - 데이터정리: Excel
  • - 통계/모형 개발 및 검증: R/Python/SPSS 등

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