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

Ananya Jana, Hui Qu, Puru Rattan, Carlos D Minacapelli, Vinod K Rustgi, Dimitris N. Metaxas

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.