Developing a model for estimating infarction onset time based on computed tomography radiomics in patients with acute middle cerebral artery occlusion

Abstract

Background Radiomics analysis is a newly emerging quantitative image analysis technique. The aim of this study was to extract a radiomics signature from the computed tomography (CT) imaging to determine the infarction onset time in patients with acute middle cerebral artery occlusion (MCAO). Methods A total of 123 patients with acute MCAO in the M1 segment (85 patients in the development cohort and 38 patients in the validation cohort) were enrolled in the present study. Clinicoradiological profiles, including head CT without contrast enhancement and computed tomographic angiography (CTA), were collected. The time from stroke onset (TFS) was classified into two subcategories: ≤ 4.5 h, and > 4.5 h. The middle cerebral artery (MCA) territory on CT images was segmented to extract and score the radiomics features associated with the TFS. In addition, the clinicoradiological factors related to the TFS were identified. Subsequently, a combined model of the radiomics signature and clinicoradiological factors was constructed to distinguish the TFS ≤ 4.5 h. Finally, we evaluated the overall performance of our constructed model in an external validation sample of ischemic stroke patients with acute MCAO in the M1 segment. Results The area under the curve (AUC) of the radiomics signature for discriminating the TFS in the development and validation cohorts was 0.770 (95% confidence interval (CI): 0.665–0.875) and 0.792 (95% CI: 0.633–0.950), respectively. The AUC of the combined model comprised of the radiomics signature, age and ASPECTS on CT in the development and validation cohorts was 0.808 (95% CI: 0.701–0.916) and 0.833 (95% CI: 0.702–0.965), respectively. In the external validation cohort, the AUC of the radiomics signature was 0.755 (95% CI: 0.614–0.897), and the AUC of the combined model was 0.820 (95% CI: 0.712–0.928). Conclusions The CT-based radiomics signature is a valuable tool for discriminating the TFS in patients with acute MCAO in the M1 segment, which may guide the use of thrombolysis therapy in patients with indeterminate stroke onset time.

How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?

“…Wen et al also developed a CT-based radiomics model to predict infarct onset time in patients with MCAO. They obtained similar results with the combined model (AUC of 0.808-0.833) [60]. Finally, Zhang et al also developed an MRI-based radiomics model to predict TSS with similar results [61].…”

Application of radiomics in ischemic stroke

“…27 When considering the diagnosis of stroke at an early stage, evidence supports the benefits of intravenous tissue plasminogen activator administration within 4.5 hours after symptom onset in ischemic stroke patients. 28 Wen and colleagues 29 leveraged this knowledge to extract radiomic features from CT images to determine the infarction onset time in patients with acute middle cerebral artery occlusion. They concluded that radiomic features were able to guide thrombolytic therapy in patients with uncertain stroke onset time.…”

Predictive value of Cmmi-MHR combined with thromboelastography parameters in acute cerebral infarction

“…Metal artifacts are image abnormalities caused by metal substances in the patient’s body that may obscure important anatomical structures or areas of the lesion. In MRI imaging, metal artifacts may reduce the clarity of brain structures and interfere with the doctor’s diagnosis [ 17 ]. Additionally, the selection of imaging parameters will directly affect the image quality.…”