대한영상의학회지 (Journal of the Korean Society of Radiology)

  • 제83권6호
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  • Pages.1219-1228
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  • 2022
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  • 1738-2637(pISSN)
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  • 2288-2928(eISSN)
대한영상의학회 (The Korean Society of Radiology)
권호 표지
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예측모형의 머신러닝 방법론과 통계학적 방법론의 비교: 영상의학 연구에서의 적용

Machine Learning vs. Statistical Model for Prediction Modelling: Application in Medical Imaging Research

  • 유리하 (연세대학교 일반대학원 의학전산통계학협동과정) ;
  • 한경화 (연세대학교 의과대학 영상의학교실, 방사선의과학연구소, 의료영상데이터사이언스센터)
  • Leeha Ryu (Department of Biostatistics and Computing, Yonsei University Graduate School) ;
  • Kyunghwa Han (Department of Radiology, Research Institute of Radiological Science, Yonsei University College of Medicine)
  • 투고 : 2022.08.09
  • 심사 : 2022.11.13
  • 발행 : 2022.11.01
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초록

최근 영상의학 연구 분야에서 영상 인자를 포함한 임상 예측 모형의 수요가 증가하고 있고, 특히 라디오믹스 연구가 활발하게 이루어지면서 기존의 전통적인 회귀 모형뿐만 아니라 머신러닝을 사용하는 연구들이 많아지고 있다. 본 종설에서는 영상의학 분야에서 예측 모형 연구에 사용된 통계학적 방법과 머신 러닝 방법들을 조사하여 정리하고, 각 방법론에 대한 설명과 장단점을 살펴보고자 한다. 마지막으로 예측 모형 연구에서 분석 방법 선택에서의 고려사항을 정리해 보고자 한다.

Clinical prediction models has been increasingly published in radiology research. In particular, as a radiomics research is being actively conducted, the prediction model is developed based on the traditional statistical model, as well as machine learning, to account for the high-dimensional data. In this review, we investigated the statistical and machine learning methods used in clinical prediction model research, and briefly summarized each analytical method for statistical model, machine learning, and statistical learning. Finally, we discussed several considerations for choosing the prediction modeling method.

키워드

과제정보

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1I1A1A01059893).

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