Deep Learning based NAS Score and Fibrosis Stage Prediction from CT and Pathology Data
This work addresses the need for non-invasive and inexpensive diagnosis of NAFLD to assist pathologists, though it is incremental as it builds on existing deep learning approaches for medical imaging.
The paper tackles the problem of predicting NAFLD activity score (NAS) and fibrosis stage from CT and pathology data, proposing a novel method that achieves effective results on a 30-patient dataset.
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.