Development and validation of a Decision Support System for the automatic diagnosis of medical images from brain MRI studies

Decision Support Systems (DSS) for assisted medical diagnosis are computer-based systems designed to assist clinicians with decision-making tasks by automatically determining diagnosis or improving diagnostic confidence. This could allow to perform early and differential diagnosis of neurological diseases, such as Alzheimer’s Disease (AD) and Parkinson’s Disease (PD), for which definite diagnosis still remains a crucial issue. Multivariate Machine Learning (ML) methods are gaining popularity within the neuroimaging community. Among these, supervised ML methods are able to automatically extract multiple information from image sets without requiring prior knowledge of where information may be coded. These methods have been proposed as a revolutionary approach for identifying sensitive biomarkers allowing for automatic classification of individual subjects. The aim of this thesis was to implement, optimize and validate a ML method able to perform automatic diagnosis of medical images by structural Magnetic Resonance Imaging data (sMRI). This method consists of 3 phases: 1) image preprocessing, mainly devoted to the co-registration of data from different patients to a common reference system; 2) feature extraction and selection, performed through Principal Components Analysis and Fisher’s Discriminant Ratio, with the aim of extracting and selecting the most discriminative features; 3) classification, performed by Support Vector Machine, with the aim of computing a predictive model for the diagnosis of new subjects. Moreover, I implemented a method for the generation of pattern distribution maps of brain structural differences, reflecting the importance of each voxel for classification. These maps could allow to identify new MR-related biomarkers for the diagnosis of neurological diseases. In order to test the feasibility of the implemented method, I applied it to the diagnosis of 3 pathologies: AD, PD and Eating Disorders (ED). Regarding PD, we acquired T1-weighted brain sMRI of 28 PD, 28 PSP (Progressive Supranuclear Palsy) and 28 healthy controls (CN). Classification performance in terms of accuracy (specificity/sensitivity) (%) was 94(91/97) for PD vs CN, 92(93/92) for PSP vs CN, 92(91/94) for PSP vs PD. Voxels influencing differential diagnosis of PD were localized in midbrain, pons, corpus callosum and thalamus, four critical regions involved in the pathophysiological mechanisms of PD. Regarding AD, I enrolled 509 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, obtaining T1-weighted brain sMRI of 137 AD, 76 Mild Cognitive Impairment (MCI) patients who converted to AD (MCIc), 134 MCI who did not convert to AD (MCInc) within 18 months, and 162 CN. Classification performance (%) was 76±11 for AD vs CN, 72±12 for MCIc vs CN, 66±16 for MCIc vs MCInc. Voxels influencing the classification of AD vs CN were localized in the temporal pole, hippocampus, entorhinal cortex, amygdala, thalamus, putamen, caudate, insula, gyrus rectus, frontal and orbitofrontal cortices, anterior cingulate cortex, precuneus, posterior cerebellar lobule. Voxels influencing the classification of MCIc vs CN and MCIc vs MCInc were similar to those found for AD. Regarding ED, we acquired T1-weighted brain sMRI of 17 ED and 17 CN. The classifier allowed ED vs CN diagnosis with accuracy (specificity/sensitivity) of 85(73/93)%. Pattern distribution maps showed that voxels influencing ED vs CN discrimination were localized in the occipital cortex, posterior cerebellar lobule, precuneus, sensorimotor and premotor cortices, anterior cingulate and orbitofrontal cortices, all brain regions involved in the regulation of appetite and emotional processing. Results of this work were published in 7 ISI international papers, 3 indexed international papers, 1 international book chapter, 5 international conference proceedings and 1 national conference proceedings.