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Danilov G, Afandiev R, Pogosbekyan E, Goraynov S, Pronin I, Zakharova N. Radiomics Enhances Diagnostic and Prognostic Value of Diffusion Kurtosis Imaging in Diffuse Axonal Injury. Stud Health Technol Inform 2023; 309:287-291. [PMID: 37869859 DOI: 10.3233/shti230798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Abstract
The aim of our study was to investigate the potential of advanced radiomics in analyzing diffusion kurtosis MRI (DKI) to increase the informativeness of DKI in diffuse axonal injury (DAI). We hypothesized that DKI radiomic features could be used to detect microstructural brain injury and predict outcomes in DAI. The study enrolled 31 patients with DAI (mean age 31.48 ± 11.10 years, 8 (25.8%) female) and 12 healthy volunteers (mean age 33.67 ± 11.06 years, 4 (33.3%) female). A total of 342,300 radiomic features were calculated (2282 features per each combination of 10 parametric DKI maps with 15 ROIs). Our results showed that several radiomic features were capable of distinguishing between healthy and injured brain tissue and accurately predicting outcomes with an accuracy of over 0.9. Advanced DKI radiomic features show high diagnostic and prognostic potential in DAI and may outperform average ROI values in DKI maps.
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Affiliation(s)
- Gleb Danilov
- Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Ramin Afandiev
- Department of Neuroimaging, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Eduard Pogosbekyan
- Department of Neuroimaging, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Sergey Goraynov
- Department of Neurotrauma, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Igor Pronin
- Department of Neuroimaging, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Natalia Zakharova
- Department of Neuroimaging, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
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Danilov G, Korolev V, Shifrin M, Ilyushin E, Maloyan N, Saada D, Ishankulov T, Afandiev R, Shevchenko A, Konakova T, Tsukanova T, Shugay S, Pronin I, Potapov A. Noninvasive Glioma Grading with Deep Learning: A Pilot Study. Stud Health Technol Inform 2022; 290:675-678. [PMID: 35673102 DOI: 10.3233/shti220163] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Gliomas are the most common neuroepithelial brain tumors, different by various biological tissue types and prognosis. They could be graded with four levels according to the 2007 WHO classification. The emergence of non-invasive histological and molecular diagnostics for nervous system neoplasms can revolutionize the efficacy and safety of medical care and radically reduce healthcare costs. Our pilot study aimed to evaluate the diagnostic accuracy of deep learning (DL) in subtyping gliomas by WHO grades (I-IV) based on preoperative magnetic resonance imaging (MRI) from Burdenko Neurosurgery Center's database. A total of 707 MRI studies was included. A "3D classification" approach predicting tumor type for the entire patient's MRI data showed the best result (accuracy = 83%, ROC AUC = 0.95), consistent with that of other authors who used different methodologies. Our preliminary results proved the separability of MR T1 axial images with contrast enhancement by WHO grade using DL.
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Affiliation(s)
- Gleb Danilov
- Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Vladislav Korolev
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russian Federation
| | - Michael Shifrin
- Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Eugene Ilyushin
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russian Federation
| | - Narek Maloyan
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russian Federation
| | - Daniel Saada
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russian Federation
| | - Timur Ishankulov
- Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Ramin Afandiev
- Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Alexander Shevchenko
- Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Tatyana Konakova
- Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Tatyana Tsukanova
- Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Svetlana Shugay
- Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Igor Pronin
- Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
| | - Alexander Potapov
- Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation
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