Liver imaging features by convolutional neural network to predict the metachronous liver metastasis in stage I-III color
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RESEARCH
Open Access
Liver imaging features by convolutional neural network to predict the metachronous liver metastasis in stage I-III colorectal cancer patients based on preoperative abdominal CT scan Sangwoo Lee1†, Eun Kyung Choe2,3†, So Yeon Kim3,4, Hua Sun Kim5, Kyu Joo Park6† and Dokyoon Kim3,7*† From The 18th Asia Pacific Bioinformatics Conference Seoul, Korea. 18-20 August 2020
* Correspondence: dokyoon.kim@ pennmedicine.upenn.edu † Sangwoo Lee, Eun Kyung Choe, Kyu Joo Park and Dokyoon Kim contributed equally to this work. 3 Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104-6116, USA 7 Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA Full list of author information is available at the end of the article
Abstract Background: Introducing deep learning approach to medical images has rendered a large amount of un-decoded information into usage in clinical research. But mostly, it has been focusing on the performance of the prediction modeling for diseaserelated entity, but not on the clinical implication of the feature itself. Here we analyzed liver imaging features of abdominal CT images collected from 2019 patients with stage I – III colorectal cancer (CRC) using convolutional neural network (CNN) to elucidate its clinical implication in oncological perspectives. Results: CNN generated imaging features from the liver parenchyma. Dimension reduction was done for the features by principal component analysis. We designed multiple prediction models for 5-year metachronous liver metastasis (5YLM) using combinations of clinical variables (age, sex, T stage, N stage) and top principal components (PCs), with logistic regression classification. The model using “1st PC (PC1) + clinical information” had the highest performance (mean AUC = 0.747) to predict 5YLM, compared to the model with clinical features alone (mean AUC = 0.709). The PC1 was independently associated with 5YLM in multivariate analysis (beta = − 3.831, P < 0.001). For the 5-year mortality rate, PC1 did not contribute to an improvement to the model with clinical features alone. For the PC1, Kaplan-Meier plots showed a significant difference between PC1 low vs. high group. The 5YLM-free survival of low PC1 was 89.6% and the high PC1 was 95.9%. In addition, PC1 had a significant correlation with sex, body mass index, alcohol consumption, and fatty liver status. (Continued on next page)
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