Deep Learning based NAS Score and Fibrosis Stage Prediction from CT and Pathology Data

• URL: http://arxiv.org/abs/2009.10687v1
• Date: Tue, 22 Sep 2020 17:02:31 GMT
• Title: Deep Learning based NAS Score and Fibrosis Stage Prediction from CT and Pathology Data
• Authors: Ananya Jana, Hui Qu, Puru Rattan, Carlos D. Minacapelli, Vinod Rustgi, Dimitris Metaxas
• Abstract summary: Non-Alcoholic Fatty Liver Disease (NAFLD) is becoming increasingly prevalent in the world population. We propose a novel method to automatically predict NAS score and fibrosis stage from CT data. We also present a method to combine the information from CT and H&E stained pathology data to improve the performance of NAS score and fibrosis stage prediction.
• Score: 4.506304887966763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Non-Alcoholic Fatty Liver Disease (NAFLD) is becoming increasingly prevalent in the world population. Without diagnosis at the right time, NAFLD can lead to non-alcoholic steatohepatitis (NASH) and subsequent liver damage. The diagnosis and treatment of NAFLD depend on the NAFLD activity score (NAS) and the liver fibrosis stage, which are usually evaluated from liver biopsies by pathologists. In this work, we propose a novel method to automatically predict NAS score and fibrosis stage from CT data that is non-invasive and inexpensive to obtain compared with liver biopsy. We also present a method to combine the information from CT and H\&E stained pathology data to improve the performance of NAS score and fibrosis stage prediction, when both types of data are available. This is of great value to assist the pathologists in computer-aided diagnosis process. Experiments on a 30-patient dataset illustrate the effectiveness of our method.

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