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Xue H, Lu H, Wang Y, Li N, Wang G. MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging. PLoS One 2024; 19:e0304419. [PMID: 38820482 PMCID: PMC11142489 DOI: 10.1371/journal.pone.0304419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 05/12/2024] [Indexed: 06/02/2024] Open
Abstract
In recent years, various data-driven algorithms have been applied to the classification and staging of brain glioma MRI detection. However, the restricted availability of brain glioma MRI data in purely data-driven deep learning algorithms has presented challenges in extracting high-quality features and capturing their complex patterns. Moreover, the analysis methods designed for 2D data necessitate the selection of ideal tumor image slices, which does not align with practical clinical scenarios. Our research proposes an novel brain glioma staging model, Medical Cognition Embedded (MCE) model for 3D data. This model embeds knowledge characteristics into data-driven approaches to enhance the quality of feature extraction. Approach includes the following key components: (1) Deep feature extraction, drawing upon the imaging technical characteristics of different MRI sequences, has led to the design of two methods at both the algorithmic and strategic levels to mimic the learning process of real image interpretation by medical professionals during film reading; (2) We conduct an extensive Radiomics feature extraction, capturing relevant features such as texture, morphology, and grayscale distribution; (3) By referencing key points in radiological diagnosis, Radiomics feature experimental results, and the imaging characteristics of various MRI sequences, we manually create diagnostic features (Diag-Features). The efficacy of proposed methodology is rigorously evaluated on the publicly available BraTS2018 and BraTS2020 datasets. Comparing it to most well-known purely data-driven models, our method achieved higher accuracy, recall, and precision, reaching 96.14%, 93.4%, 97.06%, and 97.57%, 92.80%, 95.96%, respectively.
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Affiliation(s)
- Han Xue
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, Jilin, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China
- Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI, Changchun, Jilin, China
| | - Huimin Lu
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, Jilin, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China
- Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI, Changchun, Jilin, China
| | - Yilong Wang
- Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI, Changchun, Jilin, China
- The First Hospital of Jilin University, Changchun, Jilin, China
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, Jilin, China
| | - Niya Li
- Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI, Changchun, Jilin, China
| | - Guizeng Wang
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, Jilin, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China
- Jilin Provincial Smart Health Joint Innovation Laboratory for the New Generation of AI, Changchun, Jilin, China
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Pitarch C, Ungan G, Julià-Sapé M, Vellido A. Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology. Cancers (Basel) 2024; 16:300. [PMID: 38254790 PMCID: PMC10814384 DOI: 10.3390/cancers16020300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/28/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging.
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Affiliation(s)
- Carla Pitarch
- Department of Computer Science, Universitat Politècnica de Catalunya (UPC BarcelonaTech) and Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, 08034 Barcelona, Spain;
- Eurecat, Digital Health Unit, Technology Centre of Catalonia, 08005 Barcelona, Spain
| | - Gulnur Ungan
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain; (G.U.); (M.J.-S.)
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
| | - Margarida Julià-Sapé
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain; (G.U.); (M.J.-S.)
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
| | - Alfredo Vellido
- Department of Computer Science, Universitat Politècnica de Catalunya (UPC BarcelonaTech) and Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, 08034 Barcelona, Spain;
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
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Hu M, Yang K, Wang J, Qiu RLJ, Roper J, Kahn S, Shu HK, Yang X. MGMT promoter methylation prediction based on multiparametric MRI via vision graph neural network. J Med Imaging (Bellingham) 2024; 11:014503. [PMID: 38370421 PMCID: PMC10869845 DOI: 10.1117/1.jmi.11.1.014503] [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: 05/30/2023] [Revised: 12/24/2023] [Accepted: 01/29/2024] [Indexed: 02/20/2024] Open
Abstract
Purpose Glioblastoma (GBM) is aggressive and malignant. The methylation status of the O 6 -methylguanine-DNA methyltransferase (MGMT) promoter in GBM tissue is considered an important biomarker for developing the most effective treatment plan. Although the standard method for assessing the MGMT promoter methylation status is via bisulfite modification and deoxyribonucleic acid (DNA) sequencing of biopsy or surgical specimens, a secondary automated method based on medical imaging may improve the efficiency and accuracy of those tests. Approach We propose a deep vision graph neural network (ViG) using multiparametric magnetic resonance imaging (MRI) to predict the MGMT promoter methylation status noninvasively. Our model was compared to the RSNA radiogenomic classification winners. The dataset includes 583 usable patient cases. Combinations of MRI sequences were compared. Our multi-sequence fusion strategy was compared with those using single MR sequences. Results Our best model [Fluid Attenuated Inversion Recovery (FLAIR), T1-weighted pre-contrast (T1w), T2-weighted (T2)] outperformed the winning models with a test area under the curve (AUC) of 0.628, an accuracy of 0.632, a precision of 0.646, a recall of 0.677, a specificity of 0.581, and an F1 score of 0.661. Compared to the winning models with single MR sequences, our ViG utilizing fused-MRI showed a significant improvement statistically in AUC scores, which are FLAIR (p = 0.042 ), T1w (p = 0.017 ), T1wCE (p = 0.001 ), and T2 (p = 0.018 ). Conclusions Our model is superior to challenge champions. A graph representation of the medical images enabled good handling of complexity and irregularity. Our work provides an automatic secondary check pipeline to ensure the correctness of MGMT methylation status prediction.
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Affiliation(s)
- Mingzhe Hu
- Emory University, Department of Radiation Oncology and Winship Cancer Institute, Atlanta, Georgia, United States
- Emory University, Department of Computer Science and Informatics, Atlanta, Georgia, United States
| | - Kailin Yang
- Cleveland Clinic, Taussig Cancer Center, Department of Radiation Oncology, Cleveland, Ohio, United States
| | - Jing Wang
- Emory University, Department of Radiation Oncology and Winship Cancer Institute, Atlanta, Georgia, United States
| | - Richard L. J. Qiu
- Emory University, Department of Radiation Oncology and Winship Cancer Institute, Atlanta, Georgia, United States
| | - Justin Roper
- Emory University, Department of Radiation Oncology and Winship Cancer Institute, Atlanta, Georgia, United States
| | - Shannon Kahn
- Emory University, Department of Radiation Oncology and Winship Cancer Institute, Atlanta, Georgia, United States
| | - Hui-Kuo Shu
- Emory University, Department of Radiation Oncology and Winship Cancer Institute, Atlanta, Georgia, United States
| | - Xiaofeng Yang
- Emory University, Department of Radiation Oncology and Winship Cancer Institute, Atlanta, Georgia, United States
- Emory University, Department of Computer Science and Informatics, Atlanta, Georgia, United States
- Georgia Institute of Technology and Emory University, Department of Biomedical Engineering, Atlanta, Georgia, United States
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Guillevin R, Naudin M, Fayolle P, Giraud C, Le Guillou X, Thomas C, Herpe G, Miranville A, Fernandez-Maloigne C, Pellerin L, Guillevin C. Diagnostic and Therapeutic Issues in Glioma Using Imaging Data: The Challenge of Numerical Twinning. J Clin Med 2023; 12:7706. [PMID: 38137775 PMCID: PMC10744312 DOI: 10.3390/jcm12247706] [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: 11/06/2023] [Revised: 11/28/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
Glial tumors represent the leading etiology of primary brain tumors. Their particularities lie in (i) their location in a highly functional organ that is difficult to access surgically, including for biopsy, and (ii) their rapid, anisotropic mode of extension, notably via the fiber bundles of the white matter, which further limits the possibilities of resection. The use of mathematical tools enables the development of numerical models representative of the oncotype, genotype, evolution, and therapeutic response of lesions. The significant development of digital technologies linked to high-resolution NMR exploration, coupled with the possibilities offered by AI, means that we can envisage the creation of digital twins of tumors and their host organs, thus reducing the use of physical sampling.
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Affiliation(s)
- Rémy Guillevin
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Mathieu Naudin
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Pierre Fayolle
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Clément Giraud
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Xavier Le Guillou
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
- Department of Genetic, University Hospital Center of Poitiers, 86000 Poitiers, France
| | - Clément Thomas
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Guillaume Herpe
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Alain Miranville
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | | | - Luc Pellerin
- IRMETIST Laboratory, INSERM U1313, University of Poitiers and University Hospital Center of Poitiers, 86000 Poitiers, France
| | - Carole Guillevin
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
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Champendal M, Müller H, Prior JO, Dos Reis CS. A scoping review of interpretability and explainability concerning artificial intelligence methods in medical imaging. Eur J Radiol 2023; 169:111159. [PMID: 37976760 DOI: 10.1016/j.ejrad.2023.111159] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/26/2023] [Accepted: 10/19/2023] [Indexed: 11/19/2023]
Abstract
PURPOSE To review eXplainable Artificial Intelligence/(XAI) methods available for medical imaging/(MI). METHOD A scoping review was conducted following the Joanna Briggs Institute's methodology. The search was performed on Pubmed, Embase, Cinhal, Web of Science, BioRxiv, MedRxiv, and Google Scholar. Studies published in French and English after 2017 were included. Keyword combinations and descriptors related to explainability, and MI modalities were employed. Two independent reviewers screened abstracts, titles and full text, resolving differences through discussion. RESULTS 228 studies met the criteria. XAI publications are increasing, targeting MRI (n = 73), radiography (n = 47), CT (n = 46). Lung (n = 82) and brain (n = 74) pathologies, Covid-19 (n = 48), Alzheimer's disease (n = 25), brain tumors (n = 15) are the main pathologies explained. Explanations are presented visually (n = 186), numerically (n = 67), rule-based (n = 11), textually (n = 11), and example-based (n = 6). Commonly explained tasks include classification (n = 89), prediction (n = 47), diagnosis (n = 39), detection (n = 29), segmentation (n = 13), and image quality improvement (n = 6). The most frequently provided explanations were local (78.1 %), 5.7 % were global, and 16.2 % combined both local and global approaches. Post-hoc approaches were predominantly employed. The used terminology varied, sometimes indistinctively using explainable (n = 207), interpretable (n = 187), understandable (n = 112), transparent (n = 61), reliable (n = 31), and intelligible (n = 3). CONCLUSION The number of XAI publications in medical imaging is increasing, primarily focusing on applying XAI techniques to MRI, CT, and radiography for classifying and predicting lung and brain pathologies. Visual and numerical output formats are predominantly used. Terminology standardisation remains a challenge, as terms like "explainable" and "interpretable" are sometimes being used indistinctively. Future XAI development should consider user needs and perspectives.
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Affiliation(s)
- Mélanie Champendal
- School of Health Sciences HESAV, HES-SO, University of Applied Sciences Western Switzerland, Lausanne, CH, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, CH, Switzerland.
| | - Henning Müller
- Informatics Institute, University of Applied Sciences Western Switzerland (HES-SO Valais) Sierre, CH, Switzerland; Medical faculty, University of Geneva, CH, Switzerland.
| | - John O Prior
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, CH, Switzerland; Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital (CHUV), Lausanne, CH, Switzerland.
| | - Cláudia Sá Dos Reis
- School of Health Sciences HESAV, HES-SO, University of Applied Sciences Western Switzerland, Lausanne, CH, Switzerland.
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Capobianco E, Dominietto M. Assessment of brain cancer atlas maps with multimodal imaging features. J Transl Med 2023; 21:385. [PMID: 37308956 DOI: 10.1186/s12967-023-04222-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 05/22/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Glioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs. Without treatment, GBM can result in death in about 6 months. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity. MAIN TEXT Imaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers. CONCLUSIONS The focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy.
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Affiliation(s)
- Enrico Capobianco
- The Jackson Laboratory, 10 Discovery Drive, Farmington, CT, 06032, USA.
| | - Marco Dominietto
- Paul Scherrer Institute (PSI), Forschungsstrasse 111, 5232, Villigen, Switzerland
- Gate To Brain SA, Via Livio 7, 6830, Chiasso, Switzerland
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