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Proceedings Paper

Survival prediction of liver cancer patients from CT images using deep learning and radiomic feature-based regression
Author(s): Hansang Lee; Helen Hong; Jinsil Seong; Jin Sung Kim; Junmo Kim
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Paper Abstract

Prediction of survival period for patients with hepatocellular carcinoma (HCC) provides important information for treatment planning such as radiotherapy. However, the task is known to be challenging due to the similarity of tumor imaging characteristics from patients with different survival periods. In this paper, we propose a survival prediction method using deep learning and radiomic features from CT images with support vector machine (SVM) regression. First, to extract the deep features, the convolutional neural network (CNN) is trained for the task of classifying the patients for 24-month survival. Second, the radiomic features including texture and shape are extracted from the patient images. After concatenating the radiomic features and the deep features, the SVM regressor is trained to predict the survival period of the patients. The experiment was performed on the CT scans of 171 HCC patients with 5-fold cross validation. In the experiments, the proposed method showed an accuracy of 86.5%, a root-mean-squared-error (RMSE) of 11.6, and a Spearman rank coefficient of 0.11. In comparisons with the deep feature-only- and radiomic feature-only regression results, the proposed method showed improved accuracy and RMSE than both, but lower rank coefficient than the radiomic feature-only regression. It can be observed that (1) the deep learning of CT images has a promising potential for predicting the survival period of HCC patients, and (2) the radiomic feature analysis provides useful information to strengthen the power of deep learning-based survival prediction.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143L (16 March 2020);
Show Author Affiliations
Hansang Lee, KAIST (Korea, Republic of)
Helen Hong, Seoul Women's Univ. (Korea, Republic of)
Jinsil Seong, Yonsei Univ. College of Medicine (Korea, Republic of)
Jin Sung Kim, Yonsei Univ. College of Medicine (Korea, Republic of)
Junmo Kim, KAIST (Korea, Republic of)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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