Diagnostic Performance of Automated MRI Volumetry by icobrain dm for Alzheimer’s Disease in a Clinical Setting: A REMEMBER Study

• Published in: Journal of Alzheimer's disease Vol. 83; no. 2; pp. 623 - 639
• Main Authors: Wittens, Mandy Melissa Jane, Sima, Diana Maria, Houbrechts, Ruben, Ribbens, Annemie, Niemantsverdriet, Ellis, Fransen, Erik, Bastin, Christine, Benoit, Florence, Bergmans, Bruno, Bier, Jean-Christophe, De Deyn, Peter Paul, Deryck, Olivier, Hanseeuw, Bernard, Ivanoiu, Adrian, Lemper, Jean-Claude, Mormont, Eric, Picard, Gaëtane, de la Rosa, Ezequiel, Salmon, Eric, Segers, Kurt, Sieben, Anne, Smeets, Dirk, Struyfs, Hanne, Thiery, Evert, Tournoy, Jos, Triau, Eric, Vanbinst, Anne-Marie, Versijpt, Jan, Bjerke, Maria, Engelborghs, Sebastiaan
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2021
• Subjects: Aged; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - pathology; Brain - pathology; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - pathology; Female; Hippocampus - pathology; Humans; Image Processing, Computer-Assisted - statistics & numerical data; Magnetic Resonance Imaging; Male; Retrospective Studies; Software; magnetic resonance imaging; mild cognitive impairment; Alzheimer’s disease; automated volumetry; biomarkers
• ISSN: 1387-2877; 1875-8908
• DOI: 10.3233/JAD-210450

Background: Magnetic resonance imaging (MRI) has become important in the diagnostic work-up of neurodegenerative diseases. icobrain dm, a CE-labeled and FDA-cleared automated brain volumetry software, has shown potential in differentiating cognitively healthy controls (HC) from Alzheimer’s disease (AD) dementia (ADD) patients in selected research cohorts. Objective: This study examines the diagnostic value of icobrain dm for AD in routine clinical practice, including a comparison to the widely used FreeSurfer software, and investigates if combined brain volumes contribute to establish an AD diagnosis. Methods: The study population included HC (n = 90), subjective cognitive decline (SCD, n = 93), mild cognitive impairment (MCI, n = 357), and ADD (n = 280) patients. Through automated volumetric analyses of global, cortical, and subcortical brain structures on clinical brain MRI T1w (n = 820) images from a retrospective, multi-center study (REMEMBER), icobrain dm’s (v.4.4.0) ability to differentiate disease stages via ROC analysis was compared to FreeSurfer (v.6.0). Stepwise backward regression models were constructed to investigate if combined brain volumes can differentiate between AD stages. Results: icobrain dm outperformed FreeSurfer in processing time (15–30 min versus 9–32 h), robustness (0 versus 67 failures), and diagnostic performance for whole brain, hippocampal volumes, and lateral ventricles between HC and ADD patients. Stepwise backward regression showed improved diagnostic accuracy for pairwise group differentiations, with highest performance obtained for distinguishing HC from ADD (AUC = 0.914; Specificity 83.0%; Sensitivity 86.3%). Conclusion: Automated volumetry has a diagnostic value for ADD diagnosis in routine clinical practice. Our findings indicate that combined brain volumes improve diagnostic accuracy, using real-world imaging data from a clinical setting.