Predicting microvascular invasion in hepatocellular carcinoma: a deep learning model validated across hospitals
| Autor: | Pei Hua Lee, Chia-Fong Cho, Jhao-Yu Huang, Min-Hsuan Lu, Jiaxin Yu, Wei-Ching Lin, Jesyin Lai, Chun-Chieh Yeh, Shu-Cheng Liu |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
medicine.medical_specialty Carcinoma Hepatocellular Hepatocellular carcinoma Feature extraction R895-920 Medical physics. Medical radiology. Nuclear medicine medicine Humans Neoplasm Invasiveness Radiology Nuclear Medicine and imaging Generalizability theory RC254-282 Aged Retrospective Studies Contouring Radiological and Ultrasound Technology business.industry Deep learning Liver Neoplasms Neoplasms. Tumors. Oncology. Including cancer and carcinogens General Medicine Middle Aged medicine.disease University hospital Hospitals External validation Support vector machine Oncology Female Neural Networks Computer Artificial intelligence Radiology business Research Article Arterial phase Microvascular invasion |
| Zdroj: | Cancer Imaging, Vol 21, Iss 1, Pp 1-16 (2021) Cancer Imaging |
| ISSN: | 1470-7330 |
| Popis: | 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 highest AUC of 0.845. When evaluating on the external set, the model produced an AUC of 0.777, approaching its performance on the validation set. Model interpretation with Grad-CAM revealed that MVI relevant imaging features on CT images were captured and learned by the ResNet-18 model. Conclusions This framework provide evidence showing the generalizability and robustness of ResNet-18 in predicting MVI using CT images of AP scanned at multiple different hospitals. Attention heatmaps obtained from model explainability further confirmed that ResNet-18 focused on imaging features on CT overlapping with the conditions used by radiologists to estimate MVI clinically. |
| Databáze: | OpenAIRE |
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