Radiomics using non-contrast CT to predict hemorrhagic transformation risk in stroke patients undergoing revascularization
© 2024. The Author(s), under exclusive licence to European Society of Radiology..
OBJECTIVES: This study explores whether textural features from initial non-contrast CT scans of infarcted brain tissue are linked to hemorrhagic transformation susceptibility.
MATERIALS AND METHODS: Stroke patients undergoing thrombolysis or thrombectomy from Jan 2012 to Jan 2022 were analyzed retrospectively. Hemorrhagic transformation was defined using follow-up magnetic resonance imaging. A total of 94 radiomic features were extracted from the infarcted tissue on initial NCCT scans. Patients were divided into training and test sets (7:3 ratio). Two models were developed with fivefold cross-validation: one incorporating first-order and textural radiomic features, and another using only textural radiomic features. A clinical model was also constructed using logistic regression with clinical variables, and test set validation was performed.
RESULTS: Among 362 patients, 218 had hemorrhagic transformations. The LightGBM model with all radiomics features had the best performance, with an area under the receiver operating characteristic curve (AUROC) of 0.986 (95% confidence interval [CI], 0.971-1.000) on the test dataset. The ExtraTrees model performed best when textural features were employed, with an AUROC of 0.845 (95% CI, 0.774-0.916). Minimum, maximum, and ten percentile values were significant predictors of hemorrhagic transformation. The clinical model showed an AUROC of 0.544 (95% CI, 0.431-0.658). The performance of the radiomics models was significantly better than that of the clinical model on the test dataset (p < 0.001).
CONCLUSIONS: The radiomics model can predict hemorrhagic transformation using NCCT in stroke patients. Low Hounsfield unit was a strong predictor of hemorrhagic transformation, while textural features alone can predict hemorrhagic transformation.
CLINICAL RELEVANCE STATEMENT: Using radiomic features extracted from initial non-contrast computed tomography, early prediction of hemorrhagic transformation has the potential to improve patient care and outcomes by aiding in personalized treatment decision-making and early identification of at-risk patients.
KEY POINTS: • Predicting hemorrhagic transformation following thrombolysis in stroke is challenging since multiple factors are associated. • Radiomics features of infarcted tissue on initial non-contrast CT are associated with hemorrhagic transformation. • Textural features on non-contrast CT are associated with the frailty of the infarcted tissue.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
European radiology - (2024) vom: 03. Feb. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Heo, JoonNyung [VerfasserIn] |
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| Links: |
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| Themen: |
Cerebral hemorrhage |
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| Anmerkungen: |
Date Revised 03.02.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1007/s00330-024-10618-6 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM367981599 |
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| 100 | 1 | |a Heo, JoonNyung |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Radiomics using non-contrast CT to predict hemorrhagic transformation risk in stroke patients undergoing revascularization |
| 264 | 1 | |c 2024 | |
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| 500 | |a Date Revised 03.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2024. The Author(s), under exclusive licence to European Society of Radiology. | ||
| 520 | |a OBJECTIVES: This study explores whether textural features from initial non-contrast CT scans of infarcted brain tissue are linked to hemorrhagic transformation susceptibility | ||
| 520 | |a MATERIALS AND METHODS: Stroke patients undergoing thrombolysis or thrombectomy from Jan 2012 to Jan 2022 were analyzed retrospectively. Hemorrhagic transformation was defined using follow-up magnetic resonance imaging. A total of 94 radiomic features were extracted from the infarcted tissue on initial NCCT scans. Patients were divided into training and test sets (7:3 ratio). Two models were developed with fivefold cross-validation: one incorporating first-order and textural radiomic features, and another using only textural radiomic features. A clinical model was also constructed using logistic regression with clinical variables, and test set validation was performed | ||
| 520 | |a RESULTS: Among 362 patients, 218 had hemorrhagic transformations. The LightGBM model with all radiomics features had the best performance, with an area under the receiver operating characteristic curve (AUROC) of 0.986 (95% confidence interval [CI], 0.971-1.000) on the test dataset. The ExtraTrees model performed best when textural features were employed, with an AUROC of 0.845 (95% CI, 0.774-0.916). Minimum, maximum, and ten percentile values were significant predictors of hemorrhagic transformation. The clinical model showed an AUROC of 0.544 (95% CI, 0.431-0.658). The performance of the radiomics models was significantly better than that of the clinical model on the test dataset (p < 0.001) | ||
| 520 | |a CONCLUSIONS: The radiomics model can predict hemorrhagic transformation using NCCT in stroke patients. Low Hounsfield unit was a strong predictor of hemorrhagic transformation, while textural features alone can predict hemorrhagic transformation | ||
| 520 | |a CLINICAL RELEVANCE STATEMENT: Using radiomic features extracted from initial non-contrast computed tomography, early prediction of hemorrhagic transformation has the potential to improve patient care and outcomes by aiding in personalized treatment decision-making and early identification of at-risk patients | ||
| 520 | |a KEY POINTS: • Predicting hemorrhagic transformation following thrombolysis in stroke is challenging since multiple factors are associated. • Radiomics features of infarcted tissue on initial non-contrast CT are associated with hemorrhagic transformation. • Textural features on non-contrast CT are associated with the frailty of the infarcted tissue | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Cerebral hemorrhage | |
| 650 | 4 | |a Ischemic stroke | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Thrombolytic therapy | |
| 650 | 4 | |a Tomography (X-ray computed) | |
| 700 | 1 | |a Sim, Yongsik |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Byung Moon |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Dong Joon |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Young Dae |e verfasserin |4 aut | |
| 700 | 1 | |a Nam, Hyo Suk |e verfasserin |4 aut | |
| 700 | 1 | |a Choi, Yoon Seong |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Seung-Koo |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Eung Yeop |e verfasserin |4 aut | |
| 700 | 1 | |a Sohn, Beomseok |e verfasserin |4 aut | |
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