Journal of the Korean Society of Radiology (대한영상의학회지)

The Korean Society of Radiology (대한영상의학회)
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Prediction of Amyloid β-Positivity with both MRI Parameters and Cognitive Function Using Machine Learning

뇌 MRI와 인지기능평가를 이용한 아밀로이드 베타 양성 예측 연구

  • Hye Jin Park (Department of Radiology, Hanyang University Hospital, Hanyang University College of Medicine) ;
  • Ji Young Lee (Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Jin-Ju Yang (Department of Biomedical Engineering, Hanyang University College of Medicine) ;
  • Hee-Jin Kim (Department of Neurology, Hanyang University Hospital, Hanyang University College of Medicine) ;
  • Young Seo Kim (Department of Neurology, Hanyang University Hospital, Hanyang University College of Medicine) ;
  • Ji Young Kim (Department of Nuclear Medicine, Hanyang University Guri Hospital, Hanyang University College of Medicine) ;
  • Yun Young Choi (Department of Nuclear Medicine, Hanyang University Hospital, Hanyang University College of Medicine)
  • 박혜진 (한양대학교 의과대학 한양대학교병원 영상의학과) ;
  • 이지영 (가톨릭대학교 의과대학 서울성모병원 영상의학과) ;
  • 양진주 (한양대학교 공과대학 바이오메디컬공학과) ;
  • 김희진 (한양대학교 의과대학 한양대학교병원 신경과) ;
  • 김영서 (한양대학교 의과대학 한양대학교병원 신경과) ;
  • 김지영 (한양대학교 의과대학 구리한양대학교병원 핵의학과) ;
  • 최윤영 (한양대학교 의과대학 한양대학교병원 핵의학과)
  • Received : 2022.06.02
  • Accepted : 2022.10.02
  • Published : 2023.05.01
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Abstract

Purpose To investigate the MRI markers for the prediction of amyloid β (Aβ)-positivity in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to evaluate the differences in MRI markers between Aβ-positive (Aβ [+]) and -negative groups using the machine learning (ML) method. Materials and Methods This study included 139 patients with MCI and AD who underwent amyloid PET-CT and brain MRI. Patients were divided into Aβ (+) (n = 84) and Aβ-negative (n = 55) groups. Visual analysis was performed with the Fazekas scale of white matter hyperintensity (WMH) and cerebral microbleeds (CMB) scores. The WMH volume and regional brain volume were quantitatively measured. The multivariable logistic regression and ML using support vector machine, and logistic regression were used to identify the best MRI predictors of Aβ-positivity. Results The Fazekas scale of WMH (p = 0.02) and CMB scores (p = 0.04) were higher in Aβ (+). The volumes of hippocampus, entorhinal cortex, and precuneus were smaller in Aβ (+) (p < 0.05). The third ventricle volume was larger in Aβ (+) (p = 0.002). The logistic regression of ML showed a good accuracy (81.1%) with mini-mental state examination (MMSE) and regional brain volumes. Conclusion The application of ML using the MMSE, third ventricle, and hippocampal volume is helpful in predicting Aβ-positivity with a good accuracy.

목적 경도인지장애와 알츠하이머 치매 환자에서 아밀로이드베타 양성을 예측할 수 있는 MRI 특징을 알아보고 머신러닝으로 아밀로이드베타 양성 예측 모형의 성능을 알아보고자 하였다. 대상과 방법 후향적 및 단면조사연구로 경도인지장애와 알츠하이머 치매 총 139명의 환자를 대상으로 하였다. 이들은 모두 뇌 MRI와 아밀로이드 PET-CT를 시행하였다. 대상자는 아밀로이드 베타 양성군(n = 84)과 아밀로이드 베타 음성군(n = 55)으로 분류하였다. 시각적 분석으로는 뇌백질 고신호 병변의 Fazekas 척도와 뇌미세출혈 개수를 시행하였다. 정량분석으로 뇌백질 고신호 병변의 부피와 국소뇌부피를 측정하였다. 다중 로지스틱 회귀분석과 머신러닝 기법으로 아밀로이드베타 양성을 가장 잘 예측할 수 있는 MRI 특징을 확인하였다. 결과 시각적분석에서 아밀로이드베타 양성군은 뇌백질 고신호 병변의 Fazekas 척도(p = 0.02)와 뇌미세출혈 개수(p = 0.04)가 유의미하게 높았다. 해마, 내후각피질, 설전부의 국소뇌부피들은 아밀로이드베타 양성군에서 유의미하게 작았다(p < 0.05). 제3뇌실(p = 0.002)의 부피는 아밀로이드베타 양성군에서 유의미하게 컸다. 간이 정신 상태 검사와 국소뇌부피를 이용하여 머신러닝기법을 이용했을 때 좋은 정확도를 보였다(81.1%). 결론 간이 정신 상태 검사, 제3뇌실과 해마 부피를 이용한 머신러닝의 적용은 아밀로이드베타 양성을 예측하는데 활용될 수 있다.

Keywords

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