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Mayfield JD, Murtagh R, Ciotti J, Robertson D, Naqa IE. Time-Dependent Deep Learning Prediction of Multiple Sclerosis Disability. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01031-y. [PMID: 38871944 DOI: 10.1007/s10278-024-01031-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/05/2024] [Accepted: 01/23/2024] [Indexed: 06/15/2024]
Abstract
The majority of deep learning models in medical image analysis concentrate on single snapshot timepoint circumstances, such as the identification of current pathology on a given image or volume. This is often in contrast to the diagnostic methodology in radiology where presumed pathologic findings are correlated to prior studies and subsequent changes over time. For multiple sclerosis (MS), the current body of literature describes various forms of lesion segmentation with few studies analyzing disability progression over time. For the purpose of longitudinal time-dependent analysis, we propose a combinatorial analysis of a video vision transformer (ViViT) benchmarked against traditional recurrent neural network of Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architectures and a hybrid Vision Transformer-LSTM (ViT-LSTM) to predict long-term disability based upon the Extended Disability Severity Score (EDSS). The patient cohort was procured from a two-site institution with 703 patients' multisequence, contrast-enhanced MRIs of the cervical spine between the years 2002 and 2023. Following a competitive performance analysis, a VGG-16-based CNN-LSTM was compared to ViViT with an ablation analysis to determine time-dependency of the models. The VGG16-LSTM predicted trinary classification of EDSS score in 6 years with 0.74 AUC versus the ViViT with 0.84 AUC (p-value < 0.001 per 5 × 2 cross-validation F-test) on an 80:20 hold-out testing split. However, the VGG16-LSTM outperformed ViViT when patients with only 2 years of MRIs (n = 94) (0.75 AUC versus 0.72 AUC, respectively). Exact EDSS classification was investigated for both models using both classification and regression strategies but showed collectively worse performance. Our experimental results demonstrate the ability of time-dependent deep learning models to predict disability in MS using trinary stratification of disability, mimicking clinical practice. Further work includes external validation and subsequent observational clinical trials.
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Affiliation(s)
- John D Mayfield
- USF Health Department of Radiology, 2 Tampa General Circle, STC 6103, Tampa, FL, 33612, USA.
| | - Ryan Murtagh
- USF Health Department of Radiology, 2 Tampa General Circle, STC 6103, Tampa, FL, 33612, USA
| | - John Ciotti
- Department of Neurology, University of South Florida, Morsani College of Medicine, USF Multiple Sclerosis Center, 13330 USF Laurel Drive, Tampa, FL, 33612, USA
| | - Derrick Robertson
- Department of Neurology, James A. Haley VA Medical Center, 13000 Bruce B Downs Blvd, Tampa, FL, 33612, USA
| | - Issam El Naqa
- University of South Florida, College of Engineering, 12902 USF Magnolia Drive, Tampa, FL, 33612, USA
- H. Lee Moffitt Cancer Center Department of Machine Learning, Tampa, FL, 33612, USA
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Bhimavarapu U, Chintalapudi N, Battineni G. Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier. Bioengineering (Basel) 2024; 11:266. [PMID: 38534540 DOI: 10.3390/bioengineering11030266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/28/2024] [Accepted: 03/04/2024] [Indexed: 03/28/2024] Open
Abstract
There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study's commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification.
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Affiliation(s)
- Usharani Bhimavarapu
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India
| | - Nalini Chintalapudi
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Gopi Battineni
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
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Haki M, AL-Biati HA, Al-Tameemi ZS, Ali IS, Al-hussaniy HA. Review of multiple sclerosis: Epidemiology, etiology, pathophysiology, and treatment. Medicine (Baltimore) 2024; 103:e37297. [PMID: 38394496 PMCID: PMC10883637 DOI: 10.1097/md.0000000000037297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 01/25/2024] [Accepted: 01/26/2024] [Indexed: 02/25/2024] Open
Abstract
Multiple sclerosis (MS) is a chronic autoimmune disease with demyelination, inflammation, neuronal loss, and gliosis (scarring). Our object to review MS pathophysiology causes and treatment. A Narrative Review article was conducted by searching on Google scholar, PubMed, Research Gate about relevant keywords we exclude any unique cases and case reports. The destruction of myelinated axons in the central nervous system reserves this brunt. This destruction is generated by immunogenic T cells that produce cytokines, copying a proinflammatory T helper cells1-mediated response. Autoreactive cluster of differentiation 4 + cells, particularly the T helper cells1 subtype, are activated outside the system after viral infections. T-helper cells (cluster of differentiation 4+) are the leading initiators of MS myelin destruction. The treatment plan for individuals with MS includes managing acute episodes, using disease-modifying agents to decrease MS biological function of MS, and providing symptom relief. Management of spasticity requires physiotherapy, prescription of initial drugs such as baclofen or gabapentin, secondary drug options such as tizanidine or dantrolene, and third-line treatment such as benzodiazepines. To treat urinary incontinence some options include anticholinergic medications such as oxybutynin hydrochloride, tricyclic antidepressants (such as amitriptyline), and intermittent self-catheterization. When it comes to bowel problems, one can try to implement stool softeners and consume a high roughage diet. The review takes about MS causes Pathophysiology and examines current treatment strategies, emphasizing the advancements in disease-modifying therapies and symptomatic treatments. This comprehensive analysis enhances the understanding of MS and underscores the ongoing need for research to develop more effective treatments.
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Affiliation(s)
- Maha Haki
- Department of Pharmacy, Bilad Alrafidain University College, Diyala, Iraq
| | - Haeder A. AL-Biati
- Department of Pharmacy, Bilad Alrafidain University College, Diyala, Iraq
| | - Zahraa Salam Al-Tameemi
- Department of Pharmacy, Bilad Alrafidain University College, Diyala, Iraq
- Dr. Hany Akeel Institute, Iraqi Medical Research Center, Baghdad, Iraq
| | - Inas Sami Ali
- Department of Pharmacy, Bilad Alrafidain University College, Diyala, Iraq
| | - Hany A. Al-hussaniy
- Department of Pharmacy, Bilad Alrafidain University College, Diyala, Iraq
- Dr. Hany Akeel Institute, Iraqi Medical Research Center, Baghdad, Iraq
- Department of Pharmacology, College of Medicine, University of Baghdad, Baghdad, Iraq
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Martin A, Emorine T, Megdiche I, Créange A, Kober T, Massire A, Bapst B. Accurate Diagnosis of Cortical and Infratentorial Lesions in Multiple Sclerosis Using Accelerated Fluid and White Matter Suppression Imaging. Invest Radiol 2023; 58:337-345. [PMID: 36730698 DOI: 10.1097/rli.0000000000000939] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVES The precise location of multiple sclerosis (MS) cortical lesions can be very challenging at 3 T, yet distinguishing them from subcortical lesions is essential for the diagnosis and prognosis of the disease. Compressed sensing-accelerated fluid and white matter suppression imaging (CS-FLAWS) is a new magnetic resonance imaging sequence derived from magnetization-prepared 2 rapid acquisition gradient echo with promising features for the detection and classification of MS lesions. The objective of this study was to compare the diagnostic performances of CS-FLAWS (evaluated imaging) and phase sensitive inversion recovery (PSIR; reference imaging) for classification of cortical lesions (primary objective) and infratentorial lesions (secondary objective) in MS, in combination with 3-dimensional (3D) double inversion recovery (DIR). MATERIALS AND METHODS Prospective 3 T scans (MS first diagnosis or follow-up) acquired between March and August 2021 were retrospectively analyzed. All underwent 3D CS-FLAWS, axial 2D PSIR, and 3D DIR. Double-blinded reading sessions exclusively in axial plane and final consensual reading were performed to assess the number of cortical and infratentorial lesions. Wilcoxon test was used to compare the 2 imaging datasets (FLAWS + DIR and PSIR + DIR), and intraobserver and interobserver agreement was assessed using the intraclass correlation coefficient. RESULTS Forty-two patients were analyzed (38 with relapsing-remitting MS, 29 women, 42.7 ± 12.6 years old). Compressed sensing-accelerated FLAWS allowed the identification of 263 cortical lesions versus 251 with PSIR ( P = 0.74) and 123 infratentorial lesions versus 109 with PSIR ( P = 0.63), corresponding to a nonsignificant difference between the 2 sequences. Compressed sensing-accelerated FLAWS exhibited fewer false-negative findings than PSIR either for cortical lesions (1 vs 13; P < 0.01) or infratentorial lesions (1 vs 15; P < 0.01). No false-positive findings were found with any of the 2 sequences. Diagnostic confidence was high for each contrast. CONCLUSION Three-dimensional CS-FLAWS is as accurate as 2D PSIR imaging for classification of cortical and infratentorial MS lesions, with fewer false-negative findings, opening the way to a reliable full brain MS exploration in a clinically acceptable duration (5 minutes 15 seconds).
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Martí-Juan G, Frías M, Garcia-Vidal A, Vidal-Jordana A, Alberich M, Calderon W, Piella G, Camara O, Montalban X, Sastre-Garriga J, Rovira À, Pareto D. Detection of lesions in the optic nerve with magnetic resonance imaging using a 3D convolutional neural network. Neuroimage Clin 2022; 36:103187. [PMID: 36126515 PMCID: PMC9486565 DOI: 10.1016/j.nicl.2022.103187] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 08/14/2022] [Accepted: 09/06/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Optic neuritis (ON) is one of the first manifestations of multiple sclerosis, a disabling disease with rising prevalence. Detecting optic nerve lesions could be a relevant diagnostic marker in patients with multiple sclerosis. OBJECTIVES We aim to create an automated, interpretable method for optic nerve lesion detection from MRI scans. MATERIALS AND METHODS We present a 3D convolutional neural network (CNN) model that learns to detect optic nerve lesions based on T2-weighted fat-saturated MRI scans. We validated our system on two different datasets (N = 107 and 62) and interpreted the behaviour of the model using saliency maps. RESULTS The model showed good performance (68.11% balanced accuracy) that generalizes to unseen data (64.11%). The developed network focuses its attention to the areas that correspond to lesions in the optic nerve. CONCLUSIONS The method shows robustness and, when using only a single imaging sequence, its performance is not far from diagnosis by trained radiologists with the same constraint. Given its speed and performance, the developed methodology could serve as a first step to develop methods that could be translated into a clinical setting.
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Affiliation(s)
- Gerard Martí-Juan
- Neuroradiology Group, Vall d’Hebron Research Institute (VHIR), Barcelona, Spain
| | - Marcos Frías
- BCN Medtech, Department of Information and Communication Technologies, Barcelona, Spain
| | - Aran Garcia-Vidal
- Neuroradiology Group, Vall d’Hebron Research Institute (VHIR), Barcelona, Spain
| | - Angela Vidal-Jordana
- Department of Neurology, Multiple Sclerosis center of Catalonia (Cemcat), Vall d’Hebron University Hospital, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Manel Alberich
- Radiology (IDI), Vall d’Hebron University Hospital, Barcelona, Spain
| | - Willem Calderon
- Neuroradiology Group, Vall d’Hebron Research Institute (VHIR), Barcelona, Spain
| | - Gemma Piella
- BCN Medtech, Department of Information and Communication Technologies, Barcelona, Spain
| | - Oscar Camara
- BCN Medtech, Department of Information and Communication Technologies, Barcelona, Spain
| | - Xavier Montalban
- Department of Neurology, Multiple Sclerosis center of Catalonia (Cemcat), Vall d’Hebron University Hospital, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Jaume Sastre-Garriga
- Department of Neurology, Multiple Sclerosis center of Catalonia (Cemcat), Vall d’Hebron University Hospital, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Àlex Rovira
- Neuroradiology Group, Vall d’Hebron Research Institute (VHIR), Barcelona, Spain,Radiology (IDI), Vall d’Hebron University Hospital, Barcelona, Spain
| | - Deborah Pareto
- Neuroradiology Group, Vall d’Hebron Research Institute (VHIR), Barcelona, Spain,Radiology (IDI), Vall d’Hebron University Hospital, Barcelona, Spain,Corresponding author at: Radiology Department, Vall d’Hebron University Hospital, Psg, Vall d’Hebron 119-129, Barcelona 08036, Spain.
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Hannoun S, Kocevar G, Codjia P, Barile B, Cotton F, Durand-Dubief F, Sappey-Marinier D. T1/T2 ratio: A quantitative sensitive marker of brain tissue integrity in multiple sclerosis. J Neuroimaging 2021; 32:328-336. [PMID: 34752685 DOI: 10.1111/jon.12943] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/30/2021] [Accepted: 10/14/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND AND PURPOSE The aim of this study is to determine whether cerebral white matter (WM) microstructural damage, defined by decreased fractional anisotropy (FA) and increased axial (AD) and radial (RD) diffusivities, could be detected as accurately by measuring the T1/T2 ratio, in relapsing-remitting multiple sclerosis (RRMS) patients compared to healthy control (HC) subjects. METHODS Twenty-eight RRMS patients and 24 HC subjects were included in this study. Region-based analysis based on the ICBM-81 diffusion tensor imaging (DTI) atlas WM labels was performed to compare T1/T2 ratio to DTI values in normal-appearing WM (NAWM) regions of interest. Lesions segmentation was also performed and compared to the HC global WM. RESULTS A significant 19.65% decrease of T1/T2 ratio values was observed in NAWM regions of RRMS patients compared to HC. A significant 6.30% decrease of FA, as well as significant 4.76% and 10.27% increases of AD and RD, respectively, were observed in RRMS compared to the HC group in various NAWM regions. Compared to the global WM HC mask, lesions have significantly decreased T1/T2 ratio and FA and increased AD and RD (p < . 001). CONCLUSIONS Results showed significant differences between RRMS and HC in both DTI and T1/T2 ratio measurements. T1/T2 ratio even demonstrated extensive WM abnormalities when compared to DTI, thereby highlighting the ratio's sensitivity to subtle differences in cerebral WM structural integrity using only conventional MRI sequences.
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Affiliation(s)
- Salem Hannoun
- Medical Imaging Sciences Program, Division of Health Professions, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon
| | - Gabriel Kocevar
- CREATIS, UMR 5220 CNRS & U1294 INSERM, Université Claude Bernard - Lyon1, Université de Lyon, Villeurbanne, France.,Seenovate, Datascience pole, Lyon, France
| | - Pekes Codjia
- CREATIS, UMR 5220 CNRS & U1294 INSERM, Université Claude Bernard - Lyon1, Université de Lyon, Villeurbanne, France.,Service de Radiologie, Centre Hospitalier Lyon-Sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Berardino Barile
- CREATIS, UMR 5220 CNRS & U1294 INSERM, Université Claude Bernard - Lyon1, Université de Lyon, Villeurbanne, France
| | - Francois Cotton
- CREATIS, UMR 5220 CNRS & U1294 INSERM, Université Claude Bernard - Lyon1, Université de Lyon, Villeurbanne, France.,Service de Radiologie, Centre Hospitalier Lyon-Sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Francoise Durand-Dubief
- CREATIS, UMR 5220 CNRS & U1294 INSERM, Université Claude Bernard - Lyon1, Université de Lyon, Villeurbanne, France.,Service de Neurologie A, Hôpital Neurologique Pierre Wertheimer, Groupement Hospitalier Est, Hospices Civils de Lyon, Bron, France
| | - Dominique Sappey-Marinier
- CREATIS, UMR 5220 CNRS & U1294 INSERM, Université Claude Bernard - Lyon1, Université de Lyon, Villeurbanne, France.,Département IRM, CERMEP-Imagerie du Vivant, Université de Lyon, Bron, France
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