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Bonada M, Rossi LF, Carone G, Panico F, Cofano F, Fiaschi P, Garbossa D, Di Meco F, Bianconi A. Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field. Biomedicines 2024; 12:1878. [PMID: 39200342 PMCID: PMC11352020 DOI: 10.3390/biomedicines12081878] [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: 07/08/2024] [Revised: 07/29/2024] [Accepted: 07/31/2024] [Indexed: 09/02/2024] Open
Abstract
Deep learning (DL) has been applied to glioblastoma (GBM) magnetic resonance imaging (MRI) assessment for tumor segmentation and inference of molecular, diagnostic, and prognostic information. We comprehensively overviewed the currently available DL applications, critically examining the limitations that hinder their broader adoption in clinical practice and molecular research. Technical limitations to the routine application of DL include the qualitative heterogeneity of MRI, related to different machinery and protocols, and the absence of informative sequences, possibly compensated by artificial image synthesis. Moreover, taking advantage from the available benchmarks of MRI, algorithms should be trained on large amounts of data. Additionally, the segmentation of postoperative imaging should be further addressed to limit the inaccuracies previously observed for this task. Indeed, molecular information has been promisingly integrated in the most recent DL tools, providing useful prognostic and therapeutic information. Finally, ethical concerns should be carefully addressed and standardized to allow for data protection. DL has provided reliable results for GBM assessment concerning MRI analysis and segmentation, but the routine clinical application is still limited. The current limitations could be prospectively addressed, giving particular attention to data collection, introducing new technical advancements, and carefully regulating ethical issues.
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Affiliation(s)
- Marta Bonada
- Neurosurgery Unit, Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy; (M.B.); (F.C.); (D.G.)
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milan, Italy; (G.C.)
| | - Luca Francesco Rossi
- Department of Informatics, Polytechnic University of Turin, Corso Castelfidardo 39, 10129 Turin, Italy;
| | - Giovanni Carone
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milan, Italy; (G.C.)
| | - Flavio Panico
- Neurosurgery Unit, Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy; (M.B.); (F.C.); (D.G.)
| | - Fabio Cofano
- Neurosurgery Unit, Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy; (M.B.); (F.C.); (D.G.)
| | - Pietro Fiaschi
- Division of Neurosurgery, Ospedale Policlinico San Martino, IRCCS for Oncology and Neurosciences, Largo Rosanna Benzi 10, 16132 Genoa, Italy;
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, University of Genoa, Largo Rosanna Benzi 10, 16132 Genoa, Italy
| | - Diego Garbossa
- Neurosurgery Unit, Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy; (M.B.); (F.C.); (D.G.)
| | - Francesco Di Meco
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milan, Italy; (G.C.)
| | - Andrea Bianconi
- Neurosurgery Unit, Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy; (M.B.); (F.C.); (D.G.)
- Division of Neurosurgery, Ospedale Policlinico San Martino, IRCCS for Oncology and Neurosciences, Largo Rosanna Benzi 10, 16132 Genoa, Italy;
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Demuth S, Paris J, Faddeenkov I, De Sèze J, Gourraud PA. Clinical applications of deep learning in neuroinflammatory diseases: A scoping review. Rev Neurol (Paris) 2024:S0035-3787(24)00522-8. [PMID: 38772806 DOI: 10.1016/j.neurol.2024.04.004] [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: 02/18/2024] [Revised: 03/26/2024] [Accepted: 04/09/2024] [Indexed: 05/23/2024]
Abstract
BACKGROUND Deep learning (DL) is an artificial intelligence technology that has aroused much excitement for predictive medicine due to its ability to process raw data modalities such as images, text, and time series of signals. OBJECTIVES Here, we intend to give the clinical reader elements to understand this technology, taking neuroinflammatory diseases as an illustrative use case of clinical translation efforts. We reviewed the scope of this rapidly evolving field to get quantitative insights about which clinical applications concentrate the efforts and which data modalities are most commonly used. METHODS We queried the PubMed database for articles reporting DL algorithms for clinical applications in neuroinflammatory diseases and the radiology.healthairegister.com website for commercial algorithms. RESULTS The review included 148 articles published between 2018 and 2024 and five commercial algorithms. The clinical applications could be grouped as computer-aided diagnosis, individual prognosis, functional assessment, the segmentation of radiological structures, and the optimization of data acquisition. Our review highlighted important discrepancies in efforts. The segmentation of radiological structures and computer-aided diagnosis currently concentrate most efforts with an overrepresentation of imaging. Various model architectures have addressed different applications, relatively low volume of data, and diverse data modalities. We report the high-level technical characteristics of the algorithms and synthesize narratively the clinical applications. Predictive performances and some common a priori on this topic are finally discussed. CONCLUSION The currently reported efforts position DL as an information processing technology, enhancing existing modalities of paraclinical investigations and bringing perspectives to make innovative ones actionable for healthcare.
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Affiliation(s)
- S Demuth
- Inserm U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 44000 Nantes, France; Inserm U1119 : biopathologie de la myéline, neuroprotection et stratégies thérapeutiques, University of Strasbourg, 1, rue Eugène-Boeckel - CS 60026, 67084 Strasbourg, France.
| | - J Paris
- Inserm U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 44000 Nantes, France
| | - I Faddeenkov
- Inserm U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 44000 Nantes, France
| | - J De Sèze
- Inserm U1119 : biopathologie de la myéline, neuroprotection et stratégies thérapeutiques, University of Strasbourg, 1, rue Eugène-Boeckel - CS 60026, 67084 Strasbourg, France; Department of Neurology, University Hospital of Strasbourg, 1, avenue Molière, 67200 Strasbourg, France; Inserm CIC 1434 Clinical Investigation Center, University Hospital of Strasbourg, 1, avenue Molière, 67200 Strasbourg, France
| | - P-A Gourraud
- Inserm U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 44000 Nantes, France; "Data clinic", Department of Public Health, University Hospital of Nantes, Nantes, France
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Okar SV, Dieckhaus H, Beck ES, Gaitán MI, Norato G, Pham DL, Absinta M, Cortese IC, Fletcher A, Jacobson S, Nair G, Reich DS. Highly Sensitive 3-Tesla Real Inversion Recovery MRI Detects Leptomeningeal Contrast Enhancement in Chronic Active Multiple Sclerosis. Invest Radiol 2024; 59:243-251. [PMID: 37493285 PMCID: PMC10818009 DOI: 10.1097/rli.0000000000001011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
BACKGROUND Leptomeningeal contrast enhancement (LME) on T2-weighted Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRI is a reported marker of leptomeningeal inflammation, which is known to be associated with progression of multiple sclerosis (MS). However, this MRI approach, as typically implemented on clinical 3-tesla (T) systems, detects only a few enhancing foci in ~25% of patients and has thus been criticized as poorly sensitive. PURPOSE To compare an optimized 3D real-reconstruction inversion recovery (Real-IR) MRI sequence on a clinical 3 T scanner to T2-FLAIR for prevalence, characteristics, and clinical/radiological correlations of LME. MATERIALS AND METHODS We obtained 3D T2-FLAIR and Real-IR scans before and after administration of standard-dose gadobutrol in 177 scans of 154 participants (98 women, 64%; mean ± SD age: 49 ± 12 years), including 124 with an MS-spectrum diagnosis, 21 with other neurological and/or inflammatory disorders, and 9 without neurological history. We calculated contrast-to-noise ratios (CNR) in 20 representative LME foci and determined association of LME with cortical lesions identified at 7 T (n = 19), paramagnetic rim lesions (PRL) at 3 T (n = 105), and clinical/demographic data. RESULTS We observed focal LME in 73% of participants on Real-IR (70% in established MS, 33% in healthy volunteers, P < 0.0001), compared to 33% on T2-FLAIR (34% vs. 11%, P = 0.0002). Real-IR showed 3.7-fold more LME foci than T2-FLAIR ( P = 0.001), including all T2-FLAIR foci. LME CNR was 2.5-fold higher by Real-IR ( P < 0.0001). The major determinant of LME status was age. Although LME was not associated with cortical lesions, the number of PRL was associated with the number of LME foci on both T2-FLAIR ( P = 0.003) and Real-IR ( P = 0.0003) after adjusting for age, sex, and white matter lesion volume. CONCLUSIONS Real-IR a promising tool to detect, characterize, and understand the significance of LME in MS. The association between PRL and LME highlights a possible role of the leptomeninges in sustaining chronic inflammation.
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Affiliation(s)
- Serhat Vahip Okar
- From the Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institute of Health, Bethesda, MD, USA (S.V.O., E.S.B., M.I.G., M.A., D.S.R.); qMRI Core facility, National Institute of Neurological Disorders and Stroke, National Institute of Health, Bethesda, MD, USA (H.D., G.N.); Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA (E.S.B.); Office of Biostatistics, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA (G.N.); Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, MD, USA (D.L.P.); Division of Neuroscience, Vita-Salute San Raffaele University and Hospital, Milan, Italy (M.A.); Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA (M.A.); Experimental Immunotherapeutics Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA (I.C.M.C.); Neuroimmunology Clinic, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA (A.F.); and Viral Immunology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD20814, USA (S.J.)
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Faustino R, Lopes C, Jantarada A, Mendonça A, Raposo R, Ferrão C, Freitas J, Mateus C, Pinto A, Almeida E, Gomes N, Marques L, Palavra F. Neuroimaging characterization of multiple sclerosis lesions in pediatric patients: an exploratory radiomics approach. Front Neurosci 2024; 18:1294574. [PMID: 38370435 PMCID: PMC10869542 DOI: 10.3389/fnins.2024.1294574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
Introduction Multiple sclerosis (MS), a chronic inflammatory immune-mediated disease of the central nervous system (CNS), is a common condition in young adults, but it can also affect children. The aim of this study was to construct radiomic models of lesions based on magnetic resonance imaging (MRI, T2-weighted-Fluid-Attenuated Inversion Recovery), to understand the correlation between extracted radiomic features, brain and lesion volumetry, demographic, clinical and laboratorial data. Methods The neuroimaging data extracted from eleven scans of pediatric MS patients were analyzed. A total of 60 radiomic features based on MR T2-FLAIR images were extracted and used to calculate gray level co-occurrence matrix (GLCM). The principal component analysis and ROC analysis were performed to select the radiomic features, respectively. The realized classification task by the logistic regression models was performed according to these radiomic features. Results Ten most relevant features were selected from data extracted. The logistic regression applied to T2-FLAIR radiomic features revealed significant predictor for multiple sclerosis (MS) lesion detection. Only the variable "contrast" was statistically significant, indicating that only this variable played a significant role in the model. This approach enhances the classification of lesions from normal tissue. Discussion and conclusion Our exploratory results suggest that the radiomic models based on MR imaging (T2-FLAIR) may have a potential contribution to characterization of brain tissues and classification of lesions in pediatric MS.
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Affiliation(s)
- Ricardo Faustino
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
- Faculty of Science, Institute of Biophysics and Biomedical Engineering, University of Lisbon, Lisbon, Portugal
- Biomedical Research Group, Faculty of Engineering, Faculty of Veterinary Medicine NICiTeS, Lusófona University, Lisbon, Portugal
| | - Cristina Lopes
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Afonso Jantarada
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Ana Mendonça
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Rafael Raposo
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Cristina Ferrão
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Joana Freitas
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Constança Mateus
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Ana Pinto
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Ellen Almeida
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Nuno Gomes
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Liliana Marques
- Neuroimaging and Biomedicine Research Group, Medical Imaging and Radiotherapy Research Unit, CrossI&D: Lisbon Research Center, Portuguese Red Cross Higher Health School (ESSCVP), Lisbon, Portugal
| | - Filipe Palavra
- Centre for Child Development – Neuropediatrics Unit, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Laboratory of Pharmacology and Experimental Therapeutics, Faculty of Medicine, Coimbra Institute for Clinical and Biomedical Research, University of Coimbra, Coimbra, Portugal
- Clinical Academic Center of Coimbra, Coimbra, Portugal
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Donnay C, Dieckhaus H, Tsagkas C, Gaitán MI, Beck ES, Mullins A, Reich DS, Nair G. Pseudo-Label Assisted nnU-Net enables automatic segmentation of 7T MRI from a single acquisition. FRONTIERS IN NEUROIMAGING 2023; 2:1252261. [PMID: 38107773 PMCID: PMC10722186 DOI: 10.3389/fnimg.2023.1252261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 11/06/2023] [Indexed: 12/19/2023]
Abstract
Introduction Automatic whole brain and lesion segmentation at 7T presents challenges, primarily from bias fields, susceptibility artifacts including distortions, and registration errors. Here, we sought to use deep learning algorithms (D/L) to do both skull stripping and whole brain segmentation on multiple imaging contrasts generated in a single Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) acquisition on participants clinically diagnosed with multiple sclerosis (MS), bypassing registration errors. Methods Brain scans Segmentation from 3T and 7T scanners were analyzed with software packages such as FreeSurfer, Classification using Derivative-based Features (C-DEF), nnU-net, and a novel 3T-to-7T transfer learning method, Pseudo-Label Assisted nnU-Net (PLAn). 3T and 7T MRIs acquired within 9 months from 25 study participants with MS (Cohort 1) were used for training and optimizing. Eight MS patients (Cohort 2) scanned only at 7T, but with expert annotated lesion segmentation, was used to further validate the algorithm on a completely unseen dataset. Segmentation results were rated visually by experts in a blinded fashion and quantitatively using Dice Similarity Coefficient (DSC). Results Of the methods explored here, nnU-Net and PLAn produced the best tissue segmentation at 7T for all tissue classes. In both quantitative and qualitative analysis, PLAn significantly outperformed nnU-Net (and other methods) in lesion detection in both cohorts. PLAn's lesion DSC improved by 16% compared to nnU-Net. Discussion Limited availability of labeled data makes transfer learning an attractive option, and pre-training a nnUNet model using readily obtained 3T pseudo-labels was shown to boost lesion detection capabilities at 7T.
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Affiliation(s)
- Corinne Donnay
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, MD, United States
| | - Henry Dieckhaus
- qMRI Core, NINDS, National Institutes of Health, Bethesda, MD, United States
| | - Charidimos Tsagkas
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, MD, United States
| | - María Inés Gaitán
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, MD, United States
| | - Erin S. Beck
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, MD, United States
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Andrew Mullins
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, MD, United States
- qMRI Core, NINDS, National Institutes of Health, Bethesda, MD, United States
| | - Daniel S. Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, MD, United States
| | - Govind Nair
- qMRI Core, NINDS, National Institutes of Health, Bethesda, MD, United States
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