Toward More Accessible Fully Automated 3D Volumetric MRI Decision Trees for the Differential Diagnosis of Multiple System Atrophy, Related Disorders, and Age-Matched Healthy Subjects

• Published in: Cerebellum (London, England) Vol. 22; no. 6; pp. 1098 - 1108
• Main Authors: Kim, Jisoo, Young, Geoffrey S., Willett, Ashlan S., Pitaro, Ariana T., Crotty, Grace F., Mesidor, Merlyne, Jones, Kristie A., Bay, Camden, Zhang, Min, Feany, Mel B., Xu, Xiaoyin, Qin, Lei, Khurana, Vikram
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2023; Springer Nature B.V
• Subjects: Accuracy; Atrophy; Automation; Autopsies; Autopsy; Biomedical and Life Sciences; Biomedicine; Brain stem; Cerebellum; Decision Trees; Diagnosis, Differential; Differential diagnosis; Healthy Volunteers; Humans; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical diagnosis; Mesencephalon; Movement disorders; Multiple System Atrophy - diagnostic imaging; Neostriatum; Neurobiology; Neurology; Neurosciences; Original Article; Parkinson Disease - diagnostic imaging; Patients; Pons; Prospective Studies; Putamen; Retrospective Studies; Substantia alba; Superior cerebellar peduncle; Supranuclear Palsy, Progressive - diagnosis; Thalamus; Volumetric analysis; MSA; Atypical parkinsonism; PD; PSP
• ISSN: 1473-4230; 1473-4222; 1473-4230
• DOI: 10.1007/s12311-022-01472-7

Differentiating multiple system atrophy (MSA) from related neurodegenerative movement disorders (NMD) is challenging. MRI is widely available and automated decision-tree analysis is simple, transparent, and resistant to overfitting. Using a retrospective cohort of heterogeneous clinical MRIs broadly sourced from a tertiary hospital system, we aimed to develop readily translatable and fully automated volumetric diagnostic decision-trees to facilitate early and accurate differential diagnosis of NMDs. 3DT1 MRI from 171 NMD patients (72 MSA, 49 PSP, 50 PD) and 171 matched healthy subjects were automatically segmented using Freesurfer6.0 with brainstem module. Decision trees employing substructure volumes and a novel volumetric pons-to-midbrain ratio (3D-PMR) were produced and tenfold cross-validation performed. The optimal tree separating NMD from healthy subjects selected cerebellar white matter, thalamus, putamen, striatum, and midbrain volumes as nodes. Its sensitivity was 84%, specificity 94%, accuracy 84%, and kappa 0.69 in cross-validation. The optimal tree restricted to NMD patients selected 3D-PMR, thalamus, superior cerebellar peduncle (SCP), midbrain, pons, and putamen as nodes. It yielded sensitivities/specificities of 94/84% for MSA, 72/96% for PSP, and 73/92% PD, with 79% accuracy and 0.62 kappa. There was correct classification of 16/17 MSA, 5/8 PSP, 6/8 PD autopsy-confirmed patients, and 6/8 MRIs that preceded motor symptom onset. Fully automated decision trees utilizing volumetric MRI data distinguished NMD patients from healthy subjects and MSA from other NMDs with promising accuracy, including autopsy-confirmed and pre-symptomatic subsets. Our open-source methodology is well-suited for widespread clinical translation. Assessment in even more heterogeneous retrospective and prospective cohorts is indicated.