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Abstract #4330

Tumor Aware Temporal Deep Learning (TAP-DL) for Prediction of Early Recurrence in Hepatocellular Carcinoma Patients after Ablation using MRI

Yuze Li1, Chao An2, and Huijun Chen1
1Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Beijing, China, 2First Affiliated Hospital of Chinese PLA General Hospital, Beijing, China


Hepatocellular carcinoma patients after thermal ablation suffer high recurrence rate. In this study, we proposed a deep learning method to predict the early recurrence in these patients. Compared with other predictive models, two innovations were achieved in our study: 1) integrating interconnected tasks, i.e., tumor segmentation and tumor progression prediction, into a unified model to perform co-optimization; 2) using longitudinal images to take the therapy-induced changes into consideration to explore the temporal information. Results showed that our approach can simultaneously perform tumor segmentation and tumor progression prediction with higher performance than only doing any single one of them.

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