Predicting Mechanical Thrombectomy Outcome and Time Limit through ADC Value Analysis: A Comprehensive Clinical and Simulation Study Using Machine Learning
Predicting outcomes after mechanical thrombectomy (MT) remains challenging for patients with acute ischemic stroke (AIS). This study aimed to explore the usefulness of machine learning (ML) methods using detailed apparent diffusion coefficient (ADC) analysis to predict patient outcomes and simulate...
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MDPI AG
2023-06-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/13/2138 |
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| author | Daisuke Oura Soichiro Takamiya Riku Ihara Yoshimasa Niiya Hiroyuki Sugimori |
| author_facet | Daisuke Oura Soichiro Takamiya Riku Ihara Yoshimasa Niiya Hiroyuki Sugimori |
| author_sort | Daisuke Oura |
| collection | DOAJ |
| description | Predicting outcomes after mechanical thrombectomy (MT) remains challenging for patients with acute ischemic stroke (AIS). This study aimed to explore the usefulness of machine learning (ML) methods using detailed apparent diffusion coefficient (ADC) analysis to predict patient outcomes and simulate the time limit for MT in AIS. A total of 75 consecutive patients with AIS with complete reperfusion in MT were included; 20% were separated to test data. The threshold ranged from 620 × 10<sup>−6</sup> mm<sup>2</sup>/s to 480 × 10<sup>−6</sup> mm<sup>2</sup>/s with a 20 × 10<sup>−6</sup> mm<sup>2</sup>/s step. The mean, standard deviation, and pixel number of the region of interest were obtained according to the threshold. Simulation data were created by mean measurement value of patients with a modified Rankin score of 3–4. The time limit was simulated from the cross point of the prediction score according to the time to perform reperfusion from imaging. The extra tree classifier accurately predicted the outcome (AUC: 0.833. Accuracy: 0.933). In simulation data, the prediction score to obtain a good outcome decreased according to increasing time to reperfusion, and the time limit was longer among younger patients. ML methods using detailed ADC analysis accurately predicted patient outcomes in AIS and simulated tolerance time for MT. |
| first_indexed | 2024-03-11T01:45:08Z |
| format | Article |
| id | doaj.art-59eece71f41c4e9c98edf9f4002914b0 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T01:45:08Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-59eece71f41c4e9c98edf9f4002914b02023-11-18T16:20:30ZengMDPI AGDiagnostics2075-44182023-06-011313213810.3390/diagnostics13132138Predicting Mechanical Thrombectomy Outcome and Time Limit through ADC Value Analysis: A Comprehensive Clinical and Simulation Study Using Machine LearningDaisuke Oura0Soichiro Takamiya1Riku Ihara2Yoshimasa Niiya3Hiroyuki Sugimori4Department of Radiology, Otaru General Hospital, Otaru 047-0152, JapanDepartment of Neurosurgery, Otaru General Hospital, Otaru 047-0152, JapanDepartment of Radiology, Otaru General Hospital, Otaru 047-0152, JapanDepartment of Neurosurgery, Otaru General Hospital, Otaru 047-0152, JapanFaculty of Health Sciences, Hokkaido University, Sapporo 060-0812, JapanPredicting outcomes after mechanical thrombectomy (MT) remains challenging for patients with acute ischemic stroke (AIS). This study aimed to explore the usefulness of machine learning (ML) methods using detailed apparent diffusion coefficient (ADC) analysis to predict patient outcomes and simulate the time limit for MT in AIS. A total of 75 consecutive patients with AIS with complete reperfusion in MT were included; 20% were separated to test data. The threshold ranged from 620 × 10<sup>−6</sup> mm<sup>2</sup>/s to 480 × 10<sup>−6</sup> mm<sup>2</sup>/s with a 20 × 10<sup>−6</sup> mm<sup>2</sup>/s step. The mean, standard deviation, and pixel number of the region of interest were obtained according to the threshold. Simulation data were created by mean measurement value of patients with a modified Rankin score of 3–4. The time limit was simulated from the cross point of the prediction score according to the time to perform reperfusion from imaging. The extra tree classifier accurately predicted the outcome (AUC: 0.833. Accuracy: 0.933). In simulation data, the prediction score to obtain a good outcome decreased according to increasing time to reperfusion, and the time limit was longer among younger patients. ML methods using detailed ADC analysis accurately predicted patient outcomes in AIS and simulated tolerance time for MT.https://www.mdpi.com/2075-4418/13/13/2138acute ischemic strokeMRIADCmachine learning |
| spellingShingle | Daisuke Oura Soichiro Takamiya Riku Ihara Yoshimasa Niiya Hiroyuki Sugimori Predicting Mechanical Thrombectomy Outcome and Time Limit through ADC Value Analysis: A Comprehensive Clinical and Simulation Study Using Machine Learning Diagnostics acute ischemic stroke MRI ADC machine learning |
| title | Predicting Mechanical Thrombectomy Outcome and Time Limit through ADC Value Analysis: A Comprehensive Clinical and Simulation Study Using Machine Learning |
| title_full | Predicting Mechanical Thrombectomy Outcome and Time Limit through ADC Value Analysis: A Comprehensive Clinical and Simulation Study Using Machine Learning |
| title_fullStr | Predicting Mechanical Thrombectomy Outcome and Time Limit through ADC Value Analysis: A Comprehensive Clinical and Simulation Study Using Machine Learning |
| title_full_unstemmed | Predicting Mechanical Thrombectomy Outcome and Time Limit through ADC Value Analysis: A Comprehensive Clinical and Simulation Study Using Machine Learning |
| title_short | Predicting Mechanical Thrombectomy Outcome and Time Limit through ADC Value Analysis: A Comprehensive Clinical and Simulation Study Using Machine Learning |
| title_sort | predicting mechanical thrombectomy outcome and time limit through adc value analysis a comprehensive clinical and simulation study using machine learning |
| topic | acute ischemic stroke MRI ADC machine learning |
| url | https://www.mdpi.com/2075-4418/13/13/2138 |
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