대한의용생체공학회:의공학회지 (Journal of Biomedical Engineering Research)

  • 제42권3호
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  • Pages.71-79
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  • 2021
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  • 1229-0807(pISSN)
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  • 2288-9396(eISSN)
대한의용생체공학회 (The Korean Society of Medical and Biological Engineering)
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컴퓨터 단층 촬영 영상에서의 전이성 척추 종양의 정량적 분류를 위한 라디오믹스 기반의 머신러닝 기법

Radiomics-based Machine Learning Approach for Quantitative Classification of Spinal Metastases in Computed Tomography

  • 이은우 (가천대학교 보건과학대학 의용생체공학과) ;
  • 임상헌 (가천대학교 의과대학 의공학과교실) ;
  • 전지수 (가천대학교 의과대학 의공학과교실) ;
  • 강혜원 (가천대학교 보건과학대학 의용생체공학과) ;
  • 김영재 (가천대학교 보건과학대학 의용생체공학과) ;
  • 전지영 (가천대학교 길병원 영상의학과) ;
  • 김광기 (가천대학교 보건과학대학 의용생체공학과)
  • Lee, Eun Woo (Department of Biomedical Engineering, College of Health Science, Gachon University) ;
  • Lim, Sang Heon (Department of Biomedical Engineering, Gil Medical Center, College of Medicine, Gachon University) ;
  • Jeon, Ji Soo (Department of Biomedical Engineering, Gil Medical Center, College of Medicine, Gachon University) ;
  • Kang, Hye Won (Department of Biomedical Engineering, College of Health Science, Gachon University) ;
  • Kim, Young Jae (Department of Biomedical Engineering, College of Health Science, Gachon University) ;
  • Jeon, Ji Young (Department of Radiology, Gil Medical Center, Gachon University College of Medicine) ;
  • Kim, Kwang Gi (Department of Biomedical Engineering, College of Health Science, Gachon University)
  • 투고 : 2021.03.11
  • 심사 : 2021.06.18
  • 발행 : 2021.06.30
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초록

Currently, the naked eyes-based diagnosis of bone metastases on CT images relies on qualitative assessment. For this reason, there is a great need for a state-of-the-art approach that can assess and follow-up the bone metastases with quantitative biomarker. Radiomics can be used as a biomarker for objective lesion assessment by extracting quantitative numerical values from digital medical images. In this study, therefore, we evaluated the clinical applicability of non-invasive and objective bone metastases computer-aided diagnosis using radiomics-based biomarkers in CT. We employed a total of 21 approaches consist of three-classifiers and seven-feature selection methods to predict bone metastases and select biomarkers. We extracted three-dimensional features from the CT that three groups consisted of osteoblastic, osteolytic, and normal-healthy vertebral bodies. For evaluation, we compared the prediction results of the classifiers with the medical staff's diagnosis results. As a result of the three-class-classification performance evaluation, we demonstrated that the combination of the random forest classifier and the sequential backward selection feature selection approach reached AUC of 0.74 on average. Moreover, we confirmed that 90-percentile, kurtosis, and energy were the features that contributed high in the classification of bone metastases in this approach. We expect that selected quantitative features will be helpful as biomarkers in improving the patient's survival and quality of life.

키워드

과제정보

This research was supported by the Gil Medical Center (FRD2019-11-02), and by the GRRC program of Gyeonggi Province (No. GRRC Gachon 2020-B01).

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