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Calandrelli R, Panfili M, Onofrj V, Tran HE, Piludu F, Guglielmi V, Colosimo C, Pilato F. Brain atrophy pattern in patients with mild cognitive impairment: MRI study. Transl Neurosci 2022; 13:335-348. [PMID: 36250040 PMCID: PMC9518661 DOI: 10.1515/tnsci-2022-0248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 08/21/2022] [Accepted: 09/02/2022] [Indexed: 11/21/2022] Open
Abstract
We evaluated the accuracy of the quantitative and semiquantitative analysis in detecting regional atrophy patterns and differentiating mild cognitive impairment patients who remain stable (aMCI-S) from patients who develop Alzheimer’s disease (aMCI-AD) at clinical follow-up. Baseline magnetic resonance imaging was used for quantitative and semiquantitative analysis using visual rating scales. Visual rating scores were related to gray matter thicknesses or volume measures of some structures belonging to the same brain regions. Receiver operating characteristic (ROC) analysis was performed to assess measures’ accuracy in differentiating aMCI-S from aMCI-AD. Comparing aMCI-S and aMCI-AD patients, significant differences were found for specific rating scales, for cortical thickness belonging to the middle temporal lobe (MTL), anterior temporal (AT), and fronto-insular (FI) regions, for gray matter volumes belonging to MTL and AT regions. ROC curve analysis showed that middle temporal atrophy, AT, and FI visual scales showed better diagnostic accuracy than quantitative measures also when thickness measures were combined with hippocampal volumes. Semiquantitative evaluation, performed by trained observers, is a fast and reliable tool in differentiating, at the early stage of disease, aMCI patients that remain stable from those patients that may progress to AD since visual rating scales may be informative both about early hippocampal volume loss and cortical thickness reduction.
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Affiliation(s)
- Rosalinda Calandrelli
- Dipartimento di Diagnostica per Immagini, Radioterapia, Oncologia ed Ematologia, Institute of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS , Largo A. Gemelli, 1 , 00168 Rome , Italy
| | - Marco Panfili
- Dipartimento di Diagnostica per Immagini, Radioterapia, Oncologia ed Ematologia, Institute of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS , Largo A. Gemelli, 1 , 00168 Rome , Italy
| | - Valeria Onofrj
- Department of Medical Imaging, Cliniques Universitaires Saint-Luc , Brussels , Belgium
| | - Huong Elena Tran
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS , Largo A. Gemelli, 1 , 00168 Rome , Italy
| | - Francesca Piludu
- Department of Radiology and Diagnostic Imaging, IRCCS Regina Elena National Cancer Institute , Via Elio Chianesi 53 , 00144 Rome , Italy
| | - Valeria Guglielmi
- Institute of Neurology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS , Largo A. Gemelli, 1 , 00168 Rome , Italy
| | - Cesare Colosimo
- Dipartimento di Diagnostica per Immagini, Radioterapia, Oncologia ed Ematologia, Institute of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS , Largo A. Gemelli, 1 , 00168 Rome , Italy
| | - Fabio Pilato
- Department of Medicine, Unit of Neurology, Neurophysiology, Neurobiology, Campus Bio-Medico University , Rome 00128 , Italy
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Wang J, Chen S, Liang H, Zhao Y, Xu Z, Xiao W, Zhang T, Ji R, Chen T, Xiong B, Chen F, Yang J, Lou H. Fully Automatic Classification of Brain Atrophy on NCCT Images in Cerebral Small Vessel Disease: A Pilot Study Using Deep Learning Models. Front Neurol 2022; 13:846348. [PMID: 35401411 PMCID: PMC8989434 DOI: 10.3389/fneur.2022.846348] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 02/24/2022] [Indexed: 11/13/2022] Open
Abstract
Objective Brain atrophy is an important imaging characteristic of cerebral small vascular disease (CSVD). Our study explores the linear measurement application on CT images of CSVD patients and develops a fully automatic brain atrophy classification model. The second aim was to compare it with the end-to-end Convolutional Neural Networks (CNNs) model. Methods A total of 385 subjects such as 107 no-atrophy brain, 185 mild atrophy, and 93 severe atrophy were collected and randomly separated into training set (n = 308) and test set (n = 77). Key slices for linear measurement were manually identified and used to annotate nine linear measurements and a binary classification of cerebral sulci widening. A linear-measurement-based pipeline (2D model) was constructed for two-types (existence/non-existence brain atrophy) or three-types classification (no/mild atrophy/severe atrophy). For comparison, an end-to-end CNN model (3D-deep learning model) for brain atrophy classification was also developed. Furthermore, age and gender were integrated to the 2D and 3D models. The sensitivity, specificity, accuracy, average F1 score, receiver operating characteristics (ROC) curves for two-type classification and weighed kappa for three-type classification of the two models were compared. Results Automated measurement of linear measurements and cerebral sulci widening achieved moderate to almost perfect agreement with manual annotation. In two-type atrophy classification, area under the curves (AUCs) of the 2D model and 3D model were 0.953 and 0.941 with no significant difference (p = 0.250). The Weighted kappa of the 2D model and 3D model were 0.727 and 0.607 according to standard classification they displayed, mild atrophy and severe atrophy, respectively. Applying patient age and gender information improved classification performances of both 2D and 3D models in two-type and three-type classification of brain atrophy. Conclusion We provide a model composed of different modules that can classify CSVD-related brain atrophy on CT images automatically, using linear measurement. It has similar performance and better interpretability than the end-to-end CNNs model and may prove advantageous in the clinical setting.
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Affiliation(s)
- Jincheng Wang
- Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Sijie Chen
- State Key Laboratory of Medical Neurobiology and Collaborative Innovation Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Hui Liang
- Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yilei Zhao
- Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Ziqi Xu
- Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Wenbo Xiao
- Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Tingting Zhang
- Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Renjie Ji
- Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Tao Chen
- Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Bing Xiong
- Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Feng Chen
- Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jun Yang
- Taimei Medical Technology, Shanghai, China
| | - Haiyan Lou
- Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- *Correspondence: Haiyan Lou
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