Deep Radiomics for Autism Diagnosis and Age Prediction

Abstract

Radiomics combined with deep learning is an emerging field within biomedical engineering that aims to extract important characteristics from medical images to develop a predictive model that can support clinical decision-making. This method could be used in the realm of brain disorders, particularly autism spectrum disorder (ASD), to facilitate prompt identification. We propose a novel radiomic features [deep radiomic features (DTF)], involving the use of principal component analysis to encode convolutional neural network (CNN) features, thereby capturing distinctive features related to brain regions in subjects with ASD subjects and their age. Using these features in random forest (RF) models, we explore two scenarios, such as site-specific radiomic analysis and feature extraction from unaffected brain regions to alleviate site-related variations. Our experiments involved comparing the proposed method with standard radiomics (SR) and 2-D/3-D CNNs for the classification of ASD versus healthy control (HC) individuals and different age groups (below median and above median). When using the RF model with DTF, the analysis at individual sites revealed an area under the receiver operating characteristic (ROC) curve (AUC) range of 79%–85% for features, such as the left lateral-ventricle, cerebellum-white-matter, and pallidum, as well as the right choroid-plexus and vessel. In the context of fivefold cross validation with the RF model, the combined features (DTF from 3-D CNN, ResNet50, DarketNet53, and NasNet_large with SR) achieved the highest AUC value of 76.67%. Furthermore, our method also showed notable AUC values for predicting age in subjects with ASD (80.91%) and HC (75.64%). The results indicate that DTFs consistently exhibit predictive value in classifying ASD from HC subjects and in predicting age.