Predicting microvascular invasion in hepatocellular carcinoma: a deep learning model validated across hospitals

作者：Shu-Cheng Liu, Jesyin Lai, Jhao-Yu Huang, Chia-Fong Cho, Pei Hua Lee, Min-Hsuan Lu, Chun‐Chieh Yeh, Jiaxin Yu, Wei‐Ching Lin · 发表于：Cancer Imaging · 年份：2021 · DOI：10.1186/s40644-021-00425-3 · 被引用次数：73 · 研究领域：Hepatocellular Carcinoma Treatment and Prognosis、Radiomics and Machine Learning in Medical Imaging、Colorectal Cancer Surgical Treatments

BACKGROUND: The accuracy of estimating microvascular invasion (MVI) preoperatively in hepatocellular carcinoma (HCC) by clinical observers is low. Most recent studies constructed MVI predictive models utilizing radiological and/or radiomics features extracted from computed tomography (CT) images. These methods, however, rely heavily on human experiences and require manual tumor contouring. We developed a deep learning-based framework for preoperative MVI prediction by using CT images of arterial phase (AP) with simple tumor labeling and without the need of manual feature extraction. The model was further validated on CT images that were originally scanned at multiple different hospitals. METHODS: CT images of AP were acquired for 309 patients from China Medical University Hospital (CMUH). Images of 164 patients, who took their CT scanning at 54 different hospitals but were referred to CMUH, were also collected. Deep learning (ResNet-18) and machine learning (support vector machine) models were constructed with AP images and/or patients' clinical factors (CFs), and their performance was compared systematically. All models were independently evaluated on two patient cohorts: validation set (within CMUH) and external set (other hospitals). Subsequently, explainability of the best model was visualized using gradient-weighted class activation map (Grad-CAM). RESULTS: The ResNet-18 model built with AP images and patients' clinical factors was superior than other models achieving a ...