비소세포폐암 환자의 재발 예측을 위한 흉부 CT 영상 패치 기반 CNN 분류 및 시각화
Chest CT Image Patch-Based CNN Classification and Visualization for Predicting Recurrence of Non-Small Cell Lung Cancer Patients
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초록
비소세포폐암(NSCLC)은 전체 폐암 중 85%의 높은 비중을 차지하며 사망률(22.7%)이 다른 암에 비해 현저히 높은 암으로비소세포폐암 환자의 수술 후 예후에 대한 예측은 매우 중요하다. 본 연구에서는 종양을 관심영역으로 갖는 비소세포폐암환자의 수술 전 흉부 CT 영상 패치의 종류를 종양 관련 정보에 따라 총 다섯 가지로 다양화하고, 이를 입력데이터로 갖는사전 학습 된 ResNet 과 EfficientNet CNN 네트워크를 사용하여 단일 모델과 간접 투표 방식을 이용한 앙상블 모델, 그리고3 개의 입력 채널을 활용한 앙상블 모델에서의 실험 결과 및 성능을 오분류의 사례와 Grad-CAM 시각화를 통해 비교분석한다. 실험 결과, 종양 주변부 패치를 학습한 ResNet152 단일 모델과 EfficientNet-b7 단일 모델은 각각 87.93%와81.03%의 정확도를 보였다. 또한 ResNet152 에서 총 3 개의 입력 채널에 각각 영상 패치, 종양 주변부 패치, 형상 집중 종양내부 패치를 넣어 앙상블 모델을 구성한 경우에는 정확도 87.93%를, EfficientNet-b7 에서 간접 투표 방식으로 영상 패치와종양 주변부 패치 학습 모델을 앙상블 한 경우에는 정확도 84.48%를 도출하며 안정적인 성능을 보였다.
Abstract
Non-small cell lung cancer (NSCLC) accounts for a high proportion of 85% among all lung cancer and has a significantly higher mortality rate (22.7%) compared to other cancers. Therefore, it is very important to predict the prognosis after surgery in patients with non-small cell lung cancer. In this study, the types of preoperative chest CT image patches for non-small cell lung cancer patients with tumor as a region of interest are diversified into five types according to tumor-related information, and performance of single classifier model, ensemble classifier model with soft-voting method, and ensemble classifier model using 3 input channels for combination of three different patches using pre-trained ResNet and EfficientNet CNN networks are analyzed through misclassification cases and Grad-CAM visualization. As a result of the experiment, the ResNet152 single model and the EfficientNet-b7 single model trained on the peritumoral patch showed accuracy of 87.93% and 81.03%, respectively. In addition, ResNet152 ensemble model using the image, peritumoral, and shape-focused intratumoral patches which were placed in each input channels showed stable performance with an accuracy of 87.93%. Also, EfficientNet-b7 ensemble classifier model with soft-voting method using the image and peritumoral patches showed accuracy of 84.48%.
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