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Hamon G, Legrand L, Hmeydia G, Turc G, Hassen WB, Charron S, Debacker C, Naggara O, Thirion B, Chen B, Lapergue B, Oppenheim C, Benzakoun J. Multicenter validation of synthetic FLAIR as a substitute for FLAIR sequence in acute ischemic stroke. Eur Stroke J 2024:23969873241263418. [PMID: 39096195 DOI: 10.1177/23969873241263418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2024] Open
Abstract
PURPOSE To evaluate performance of synthetic and real FLAIR for identifying early stroke in a multicenter cohort. METHODS This retrospective study was conducted using DWI and FLAIR extracted from the Endovascular Treatment in Ischemic Stroke image registry (2017-2021). The database was partitioned into subsets according to MRI field strength and manufacturer, and randomly divided into training set (70%) used for model fine-tuning, validation set (15%), and test set (15%). In test set, five readers, blinded to FLAIR sequence type, assessed DWI-FLAIR mismatch using real and synthetic FLAIR. Interobserver agreement for DWI-FLAIR rating and concordance between synthetic and real FLAIR were evaluated with kappa statistics. Sensitivity and specificity for identification of ⩽4.5 h AIS were compared in patients with known onset-to-MRI delay using McNemar's test. RESULTS 1454 complete MRI sets (1172 patients, median (IQR) age: 73 years (62-82); 762 women) acquired on 125 MRI units were analyzed. In test set (207 MRI), interobserver reproducibility for DWI-FLAIR mismatch labeling was substantial for real and synthetic FLAIR (Fleiss κ = 0.79 (95%CI: 0.73-0.84) and 0.77 (95%CI: 0.71-0.82), respectively). After consensus, concordance between real and synthetic FLAIR was excellent (κ = 0.85 (95%CI: 0.78-0.92)). In 141 MRI sets with known onset-to-MRI delay, diagnostic performances for ⩽4.5 h AIS identification did not differ between real and synthetic FLAIR (sensitivity: 60/71 (85%) vs 59/71 (83%), p = .56; specificity: 65/70 (93%) vs 65/70 (93%), p > 0.99). CONCLUSION A deep-learning-based FLAIR fine-tuned on multicenter data can provide comparable performances to real FLAIR for early AIS identification. This approach may help reducing MR protocol duration and motion artifacts.
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Affiliation(s)
- Guillaume Hamon
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, Paris, France
| | - Laurence Legrand
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, Paris, France
- Department of Neuroradiology, GHU Paris Psychiatrie et Neurosciences, Paris, France
- Université Paris-Cité, FHU Neurovasc, Paris, France
| | - Ghazi Hmeydia
- Department of Neuroradiology, GHU Paris Psychiatrie et Neurosciences, Paris, France
| | - Guillaume Turc
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, Paris, France
- Université Paris-Cité, FHU Neurovasc, Paris, France
- Department of Neurology, GHU Paris Psychiatrie et Neurosciences, Paris, France
| | - Wagih Ben Hassen
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, Paris, France
- Department of Neuroradiology, GHU Paris Psychiatrie et Neurosciences, Paris, France
- Université Paris-Cité, FHU Neurovasc, Paris, France
| | - Sylvain Charron
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, Paris, France
- Université Paris-Cité, FHU Neurovasc, Paris, France
| | - Clement Debacker
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, Paris, France
- Department of Neuroradiology, GHU Paris Psychiatrie et Neurosciences, Paris, France
- Université Paris-Cité, FHU Neurovasc, Paris, France
| | - Olivier Naggara
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, Paris, France
- Department of Neuroradiology, GHU Paris Psychiatrie et Neurosciences, Paris, France
- Université Paris-Cité, FHU Neurovasc, Paris, France
| | | | - Bailiang Chen
- CIC, Innovation Technologique, Université de Lorraine, INSERM 1433, Nancy, France
| | - Bertrand Lapergue
- Department of Neurology, Foch Hospital, Versailles Saint-Quentin-en-Yvelines University, Suresnes, France
| | - Catherine Oppenheim
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, Paris, France
- Department of Neuroradiology, GHU Paris Psychiatrie et Neurosciences, Paris, France
- Université Paris-Cité, FHU Neurovasc, Paris, France
| | - Joseph Benzakoun
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, Paris, France
- Department of Neuroradiology, GHU Paris Psychiatrie et Neurosciences, Paris, France
- Université Paris-Cité, FHU Neurovasc, Paris, France
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Johansen J, Offersen CM, Carlsen JF, Ingala S, Hansen AE, Nielsen MB, Darkner S, Pai A. An Automatic DWI/FLAIR Mismatch Assessment of Stroke Patients. Diagnostics (Basel) 2023; 14:69. [PMID: 38201378 PMCID: PMC10802848 DOI: 10.3390/diagnostics14010069] [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: 11/15/2023] [Revised: 12/20/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024] Open
Abstract
DWI/FLAIR mismatch assessment for ischemic stroke patients shows promising results in determining if patients are eligible for recombinant tissue-type plasminogen activator (r-tPA) treatment. However, the mismatch criteria suffer from two major issues: binary classification of a non-binary problem and the subjectiveness of the assessor. In this article, we present a simple automatic method for segmenting stroke-related parenchymal hyperintensities on FLAIR, allowing for an automatic and continuous DWI/FLAIR mismatch assessment. We further show that our method's segmentations have comparable inter-rater agreement (DICE 0.820, SD 0.12) compared to that of two neuro-radiologists (DICE 0.856, SD 0.07), that our method appears robust to hyper-parameter choices (suggesting good generalizability), and lastly, that our methods continuous DWI/FLAIR mismatch assessment correlates to mismatch assessments made for a cohort of wake-up stroke patients at hospital submission. The proposed method shows promising results in automating the segmentation of parenchymal hyperintensity within ischemic stroke lesions and could help reduce inter-observer variability of DWI/FLAIR mismatch assessment performed in clinical environments as well as offer a continuous assessment instead of the current binary one.
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Affiliation(s)
- Jacob Johansen
- Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark; (S.D.); (A.P.)
- Cerebriu A/S, 1434 Copenhagen, Denmark;
| | - Cecilie Mørck Offersen
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark; (J.F.C.); (A.E.H.); (M.B.N.)
- Department of Radiology, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Jonathan Frederik Carlsen
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark; (J.F.C.); (A.E.H.); (M.B.N.)
- Department of Radiology, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Silvia Ingala
- Cerebriu A/S, 1434 Copenhagen, Denmark;
- Department of Radiology, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Adam Espe Hansen
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark; (J.F.C.); (A.E.H.); (M.B.N.)
- Department of Radiology, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Michael Bachmann Nielsen
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark; (J.F.C.); (A.E.H.); (M.B.N.)
- Department of Radiology, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Sune Darkner
- Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark; (S.D.); (A.P.)
| | - Akshay Pai
- Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark; (S.D.); (A.P.)
- Cerebriu A/S, 1434 Copenhagen, Denmark;
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Ben Alaya I, Limam H, Kraiem T. Automatic triaging of acute ischemic stroke patients for reperfusion therapies using Artificial Intelligence methods and multiple MRI features: A review. Clin Imaging 2023; 104:109992. [PMID: 37857099 DOI: 10.1016/j.clinimag.2023.109992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 09/25/2023] [Accepted: 09/25/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND The selection of appropriate treatments for Acute Ischemic Stroke (AIS), including Intravenous (IV) tissue plasminogen activator (tPA) and Mechanical thrombectomy, is a critical aspect of clinical decision-making. Timely treatment is essential, with recommended administration of therapies within 4.5 h of symptom onset. However, patients with unknown Time Since Stroke (TSS), are often excluded from thrombolysis, even if the stroke onset exceeds 6 h. Current clinical guidelines propose using multimodal Magnetic Resonance Imaging (MRI) to assess various mismatches. METHODS The review explores the significance of automatic methods based on Artificial Intelligence (AI) algorithms that utilize multiple MRI features to identify patients who are most likely to benefit from acute reperfusion therapies. These AI methods include TSS classification and patient selection for therapies in the late time window (>6 h) using MRI images to provide detailed stroke information. RESULTS The review discusses the challenges and limitations in the existing mismatch methods, which may lead to missed opportunities for reperfusion therapy. To address these limitations, AI approaches have been developed to enhance accuracy and support clinical decision-making. These AI methods have shown promising results, outperforming traditional mismatch assessments and providing improved sensitivity and specificity in identifying patients eligible for reperfusion therapies. DISCUSSION In summary, the integration of AI algorithms utilizing multiple MRI features has the potential to enhance accuracy, improve patient outcomes, and positively influence the decision-making process in AIS. However, ongoing research and collaboration among clinicians, researchers, and technologists are vital to realize the full potential of AI in optimizing stroke management.
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Affiliation(s)
- Ines Ben Alaya
- Tunis El Manar University, Higher Institute of Medical Technology of Tunis, Laboratory of Biophysics and Medical Technology, 1006 Tunis, Tunisia.
| | - Hela Limam
- Tunis El Manar University, Higher Institute of Computer Science, Higher Institute of Management of Tunis, BestMod Laboratory, 1002 Tunis, Tunisia.
| | - Tarek Kraiem
- Tunis El Manar University, Higher Institute of Medical Technology of Tunis, Laboratory of Biophysics and Medical Technology, 1006 Tunis, Tunisia
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Gilotra K, Swarna S, Mani R, Basem J, Dashti R. Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease. Front Hum Neurosci 2023; 17:1254417. [PMID: 37746051 PMCID: PMC10516608 DOI: 10.3389/fnhum.2023.1254417] [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: 07/07/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction Cerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms. Methods We review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice. Results The incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers. Conclusion The implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.
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Affiliation(s)
| | | | | | | | - Reza Dashti
- Dashti Lab, Department of Neurological Surgery, Stony Brook University Hospital, Stony Brook, NY, United States
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Offersen CM, Sørensen J, Sheng K, Carlsen JF, Langkilde AR, Pai A, Truelsen TC, Nielsen MB. Artificial Intelligence for Automated DWI/FLAIR Mismatch Assessment on Magnetic Resonance Imaging in Stroke: A Systematic Review. Diagnostics (Basel) 2023; 13:2111. [PMID: 37371006 DOI: 10.3390/diagnostics13122111] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/15/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
We conducted this Systematic Review to create an overview of the currently existing Artificial Intelligence (AI) methods for Magnetic Resonance Diffusion-Weighted Imaging (DWI)/Fluid-Attenuated Inversion Recovery (FLAIR)-mismatch assessment and to determine how well DWI/FLAIR mismatch algorithms perform compared to domain experts. We searched PubMed Medline, Ovid Embase, Scopus, Web of Science, Cochrane, and IEEE Xplore literature databases for relevant studies published between 1 January 2017 and 20 November 2022, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We assessed the included studies using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Five studies fit the scope of this review. The area under the curve ranged from 0.74 to 0.90. The sensitivity and specificity ranged from 0.70 to 0.85 and 0.74 to 0.84, respectively. Negative predictive value, positive predictive value, and accuracy ranged from 0.55 to 0.82, 0.74 to 0.91, and 0.73 to 0.83, respectively. In a binary classification of ±4.5 h from stroke onset, the surveyed AI methods performed equivalent to or even better than domain experts. However, using the relation between time since stroke onset (TSS) and increasing visibility of FLAIR hyperintensity lesions is not recommended for the determination of TSS within the first 4.5 h. An AI algorithm on DWI/FLAIR mismatch assessment focused on treatment eligibility, outcome prediction, and consideration of patient-specific data could potentially increase the proportion of stroke patients with unknown onset who could be treated with thrombolysis.
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Affiliation(s)
- Cecilie Mørck Offersen
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Jens Sørensen
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
| | - Kaining Sheng
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Jonathan Frederik Carlsen
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Annika Reynberg Langkilde
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Akshay Pai
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Cerebriu A/S, 1127 Copenhagen, Denmark
| | - Thomas Clement Truelsen
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
- Department of Neurology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
| | - Michael Bachmann Nielsen
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
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Kihira S, Derakhshani A, Leung M, Mahmoudi K, Bauer A, Zhang H, Polson J, Arnold C, Tsankova NM, Hormigo A, Salehi B, Pham N, Ellingson BM, Cloughesy TF, Nael K. Multi-Parametric Radiomic Model to Predict 1p/19q Co-Deletion in Patients with IDH-1 Mutant Glioma: Added Value to the T2-FLAIR Mismatch Sign. Cancers (Basel) 2023; 15:cancers15041037. [PMID: 36831380 PMCID: PMC9954034 DOI: 10.3390/cancers15041037] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/28/2023] [Accepted: 01/28/2023] [Indexed: 02/10/2023] Open
Abstract
PURPOSE The T2-FLAIR mismatch sign has shown promise in determining IDH mutant 1p/19q non-co-deleted gliomas with a high specificity and modest sensitivity. To develop a multi-parametric radiomic model using MRI to predict 1p/19q co-deletion status in patients with newly diagnosed IDH1 mutant glioma and to perform a comparative analysis to T2-FLAIR mismatch sign+. METHODS In this retrospective study, patients with diagnosis of IDH1 mutant gliomas with known 1p/19q status who had preoperative MRI were included. T2-FLAIR mismatch was evaluated independently by two board-certified neuroradiologists. Texture features were extracted from glioma segmentation of FLAIR images. eXtremeGradient Boosting (XGboost) classifiers were used for model development. Leave-one-out-cross-validation (LOOCV) and external validation performances were reported for both the training and external validation sets. RESULTS A total of 103 patients were included for model development and 18 patients for external testing validation. The diagnostic performance (sensitivity/specificity/accuracy) in the determination of the 1p/19q co-deletion status was 59%/83%/67% (training) and 62.5%/70.0%/66.3% (testing) for the T2-FLAIR mismatch sign. This was significantly improved (p = 0.04) using the radiomics model to 77.9%/82.8%/80.3% (training) and 87.5%/89.9%/88.8% (testing), respectively. The addition of radiomics as a computer-assisted tool resulted in significant (p = 0.02) improvement in the performance of the neuroradiologist with 13 additional corrected cases in comparison to just using the T2-FLAIR mismatch sign. CONCLUSION The proposed radiomic model provides much needed sensitivity to the highly specific T2-FLAIR mismatch sign in the determination of the 1p/19q non-co-deletion status and improves the overall diagnostic performance of neuroradiologists when used as an assistive tool.
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Affiliation(s)
- Shingo Kihira
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Ahrya Derakhshani
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Michael Leung
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Keon Mahmoudi
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Adam Bauer
- Department of Radiology, Kaiser Permanente Fontana Medical Center, Fontana, CA 92335, USA
| | - Haoyue Zhang
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jennifer Polson
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Corey Arnold
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Nadejda M. Tsankova
- Department of Pathology and Laboratory Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Adilia Hormigo
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Banafsheh Salehi
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Nancy Pham
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
| | - Benjamin M. Ellingson
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
- UCLA Brain Tumor Imaging Laboratory, Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Timothy F. Cloughesy
- UCLA Brain Tumor Imaging Laboratory, Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Neurology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Kambiz Nael
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA 90095, USA
- Correspondence: ; Tel.: +1-310-267-5932
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