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Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH-2 trial intracerebral hemorrhage population.
Haider, Stefan P; Qureshi, Adnan I; Jain, Abhi; Tharmaseelan, Hishan; Berson, Elisa R; Zeevi, Tal; Majidi, Shahram; Filippi, Christopher G; Iseke, Simon; Gross, Moritz; Acosta, Julian N; Malhotra, Ajay; Kim, Jennifer A; Sansing, Lauren H; Falcone, Guido J; Sheth, Kevin N; Payabvash, Seyedmehdi.
Afiliação
  • Haider SP; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
  • Qureshi AI; Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Munich, Germany.
  • Jain A; Zeenat Qureshi Stroke Institute and Department of Neurology, University of Missouri, Columbia, MO, USA.
  • Tharmaseelan H; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
  • Berson ER; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
  • Zeevi T; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
  • Majidi S; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
  • Filippi CG; Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Iseke S; Department of Radiology, Tufts University School of Medicine, Boston, MA, USA.
  • Gross M; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
  • Acosta JN; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
  • Malhotra A; Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
  • Kim JA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
  • Sansing LH; Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
  • Falcone GJ; Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
  • Sheth KN; Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
  • Payabvash S; Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
Eur J Neurol ; 28(9): 2989-3000, 2021 09.
Article em En | MEDLINE | ID: mdl-34189814
ABSTRACT
BACKGROUND AND

PURPOSE:

Radiomics provides a framework for automated extraction of high-dimensional feature sets from medical images. We aimed to determine radiomics signature correlates of admission clinical severity and medium-term outcome from intracerebral hemorrhage (ICH) lesions on baseline head computed tomography (CT).

METHODS:

We used the ATACH-2 (Antihypertensive Treatment of Acute Cerebral Hemorrhage II) trial dataset. Patients included in this analysis (n = 895) were randomly allocated to discovery (n = 448) and independent validation (n = 447) cohorts. We extracted 1130 radiomics features from hematoma lesions on baseline noncontrast head CT scans and generated radiomics signatures associated with admission Glasgow Coma Scale (GCS), admission National Institutes of Health Stroke Scale (NIHSS), and 3-month modified Rankin Scale (mRS) scores. Spearman's correlation between radiomics signatures and corresponding target variables was compared with hematoma volume.

RESULTS:

In the discovery cohort, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.47 vs. 0.44, p = 0.008), admission NIHSS (0.69 vs. 0.57, p < 0.001), and 3-month mRS scores (0.44 vs. 0.32, p < 0.001). Similarly, in independent validation, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.43 vs. 0.41, p = 0.02), NIHSS (0.64 vs. 0.56, p < 0.001), and 3-month mRS scores (0.43 vs. 0.33, p < 0.001). In multiple regression analysis adjusted for known predictors of ICH outcome, the radiomics signature was an independent predictor of 3-month mRS in both cohorts.

CONCLUSIONS:

Limited by the enrollment criteria of the ATACH-2 trial, we showed that radiomics features quantifying hematoma texture, density, and shape on baseline CT can provide imaging correlates for clinical presentation and 3-month outcome. These findings couldtrigger a paradigm shift where imaging biomarkers may improve current modelsfor prognostication, risk-stratification, and treatment triage of ICH patients.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Hemorragia Cerebral / Hematoma Tipo de estudo: Clinical_trials / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Hemorragia Cerebral / Hematoma Tipo de estudo: Clinical_trials / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article