A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers

Abstract

We propose a novel approach to develop a computer-aided decision support system for radiologists to help them classify brain degeneration process as physiological or pathological, aiding in early prognosis of brain degenerative diseases. Our approach applies computational and mathematical formulations to extract quantitative information from biomedical images. Our study explores the longitudinal OASIS-3 dataset, which consists of 4096 brain MRI scans collected over a period of 15 years. We perform feature extraction using Pyradiomics python package that quantizes brain MRI images using different texture analysis methods. Studies indicate that Radiomics has rarely been used for analysis of brain cognition; hence, our study is also a novel effort to determine the efficiency of Radiomics features extracted from structural MRI scans for classification of brain degenerative diseases and to create awareness about Radiomics. For classification tasks, we explore various ensemble learning classification algorithms such as random forests, bagging-based ensemble classifiers, and gradient-boosted ensemble classifiers such as XGBoost and AdaBoost. Such ensemble learning classifiers have not been used for biomedical image classification. We also propose a novel texture analysis matrix, Decreasing Gray-Level Matrix or DGLM. The features extracted from this filter helped to further improve the accuracy of our decision support system. The proposed system based on XGBoost ensemble learning classifiers achieves an accuracy of 97.38%, with sensitivity 99.82% and specificity 97.01%.

Radiomics-Based Artificial Intelligence Differentiation of Neurodegenerative Diseases with Reference to the Volumetry

“…GLRLM relates to the coarseness and complexity, which reflect the degree of association between different pixels in the whole brain [41]. Our results were coherent with Jain et al, 2021 study, which suggested GLSZM, GLDM and GLRLM were promising features utilized in the whole brain for dementia classification [43].…”

Asymmetry of radiomics features in the white matter of patients with primary progressive aphasia

“…In recent years, several studies have highlighted the potentialities of radiomics in the clinical and diagnostic work-up of patients with neurodegenerative diseases as an approach that allows capturing from radiological images non-trivial and complex features (compared to classical morphological approaches) associated with clinical and biological outcomes (Abbasian Ardakani et al, 2022). The radiomicbased analytical framework combines features of global and local regions with machine and deep learning algorithms aiming at unveiling higher-order information underlying specific disorders (Wu et al, 2019;Xiao et al, 2019;Cao et al, 2020;Feng and Ding, 2020;Liu et al, 2020;Jain et al, 2021;Kim et al, 2021;Zhou et al, 2021;Cheung et al, 2022). Studies that used radiomics on gray matter and white matter regions in patients with Alzheimer's (AD) and Parkinson's diseases (PD) showed promising results in terms of patients' characterization by merging the high-throughput extraction of pattern-based information over specific brain regions with clinical data.…”

How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?

“…In recent years, the use of radiomics has extended to brain pathologies other than tumours. There are articles on the use of radiomic features for prognostic prediction in acute ischemic stroke (AIS) [14], for predicting degenerative diseases [15], or even for predicting aneurysm rupture [16].…”