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

TL;DR

This paper introduces a deep learning approach to non-invasively predict NAFLD activity score and fibrosis stage from CT and pathology data, aiding diagnosis and reducing reliance on invasive biopsies.

Contribution

It presents a novel deep learning method for predicting liver disease scores from imaging and pathology data, combining both data types for improved accuracy.

Findings

Effective prediction of NAS score and fibrosis stage from CT data.

Combining CT and pathology data improves prediction accuracy.

Method demonstrates promising results on a 30-patient dataset.

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.