[논문]라디오믹스 기반 직장암 수술 위험도 예측을 위한 MRI 반자동 선택 바이오마커 검증 연구

백영서; 김영재; 전영배; 황태식; 백정흠; 김광기

doi:10.9718/jber.2023.44.1.11

라디오믹스 기반 직장암 수술 위험도 예측을 위한 MRI 반자동 선택 바이오마커 검증 연구
A Study on MRI Semi-Automatically Selected Biomarkers for Predicting Risk of Rectal Cancer Surgery Based on Radiomics 원문보기

Journal of biomedical engineering research : the official journal of the Korean Society of Medical & Biological Engineering, v.44 no.1, 2023년, pp.11 - 18

백영서 (가천대길병원 의료기기 R&D센터) , 김영재 (가천대길병원 의료기기 R&D센터) , 전영배 (가천대학교 의과대학 길병원 외과학교실 대장항문외과) , 황태식 (가천대학교 의과대학 길병원 외과학교실 대장항문외과) , 백정흠 (가천대학교 의과대학 길병원 외과학교실 대장항문외과) , 김광기 (가천대길병원 의료기기 R&D센터)

Abstract ▼ AI-Helper

Currently, studies to predict the risk of rectal cancer surgery select MRI image slices based on the clinical experience of surgeons. The purpose of this study is to semi-automatically select and classify 2D MRI image slides to predict the risk of rectal cancer surgery using biomarkers. The data used were retrospectively collected MRI imaging data of 50 patients who underwent laparoscopic surgery for rectal cancer at Gachon University Gil Medical Center. Expert-selected MRI image slices and non-selected slices were screened and radiomics was used to extract a total of 102 features. A total of 16 approaches were used, combining 4 classifiers and 4 feature selection methods. The combination of Random Forest and Ridge performed with a sensitivity of 0.83, a specificity of 0.88, an accuracy of 0.85, and an AUC of 0.89±0.09. Differences between expert-selected MRI image slices and non-selected slices were analyzed by extracting the top five significant features. Selected quantitative features help expedite decision making and improve efficiency in studies to predict risk of rectal cancer surgery.

주제어

표/그림 (6)

그림 그림 1. MRI 영상 슬라이스 데이터 처리 결과 (a) 원본 (b) 관심 영역 이미지 (c) 마스크 이미지 Fig. 1. Result of MRI image slice data processing. (a) Original (b) ROI image (c) Mask image
그림 그림 2. 라디오믹스를 통한 직장암 MRI 영상 슬라이스 선택 분류 연구 흐름도 Fig. 2. The flowchart for rectal cancer MRI image slice selection classification using radiomics
그림 그림 3. MRI 영상 슬라이스 선택 분류 성능을 확인하기 위한 성능 평가 분석 결과. 제시된 수치는 분류 성능의 AUC. Fig. 3. Performance evaluation analysis results to confirm the MRI image slice selection classification performance. Presented numerical values indicate AUC score.
그림 그림 4. RF 분류와 Ridge 특징 선택 기법 조합의 특징 중요도 분석 결과 Fig. 4. Feature importance analysis results of RF classifier using Ridge feature selection method
표 표 1. RF와 Ridge, LASSO, SFS, RFE 조합의 재현율, 특이도, 정밀도, AUC의 분석결과 Table 1. Recall, specificity, precision, and AUC analysis results for RF and Ridge, LASSO, SFS, and RFE combinations
그림 그림 5. MRI 영상 슬라이스 선택 분류에서 RF와 Ridge 조합의 유의한 특징 차이 분포 Fig. 5. Analysis of Significant Feature Differences of RF and Ridge Combinations in MRI Image Slice Selection Classification (p<0.001).

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