Details

Autor(en) / Beteiligte

• Chen, Zejia Frank
• Zhang, Liying
• Carrington, André M
• Thornhill, Rebecca
• Miguel, Olivier
• Auriat, Angela M
• Omid-Fard, Nima
• Hiremath, Shivaprakash
• Tshemeister Abitbul, Vered
• Dowlatshahi, Dar
• Demchuk, Andrew
• Gladstone, David
• Morotti, Andrea
• Casetta, Ilaria
• Fainardi, Enrico
• Huynh, Thien
• Elkabouli, Marah
• Talbot, Zoé
• Melkus, Gerd
• Aviv, Richard I

Titel

Clinical Features, Non-Contrast CT Radiomic and Radiological Signs in Models for the Prediction of Hematoma Expansion in Intracerebral Hemorrhage

Ist Teil von

• Canadian Association of Radiologists journal, 2023-11, Vol.74 (4), p.713-722

Ort / Verlag

Los Angeles, CA: SAGE Publications

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Purpose Rapid identification of hematoma expansion (HE) risk at baseline is a priority in intracerebral hemorrhage (ICH) patients and may impact clinical decision making. Predictive scores using clinical features and Non-Contract Computed Tomography (NCCT)-based features exist, however, the extent to which each feature set contributes to identification is limited. This paper aims to investigate the relative value of clinical, radiological, and radiomics features in HE prediction. Methods Original data was retrospectively obtained from three major prospective clinical trials [“Spot Sign” Selection of Intracerebral Hemorrhage to Guide Hemostatic Therapy (SPOTLIGHT)NCT01359202; The Spot Sign for Predicting and Treating ICH Growth Study (STOP-IT)NCT00810888] Patients baseline and follow-up scans following ICH were included. Clinical, NCCT radiological, and radiomics features were extracted, and multivariate modeling was conducted on each feature set. Results 317 patients from 38 sites met inclusion criteria. Warfarin use (p=0.001) and GCS score (p=0.046) were significant clinical predictors of HE. The best performing model for HE prediction included clinical, radiological, and radiomic features with an area under the curve (AUC) of 87.7%. NCCT radiological features improved upon clinical benchmark model AUC by 6.5% and a clinical & radiomic combination model by 6.4%. Addition of radiomics features improved goodness of fit of both clinical (p=0.012) and clinical & NCCT radiological (p=0.007) models, with marginal improvements on AUC. Inclusion of NCCT radiological signs was best for ruling out HE whereas the radiomic features were best for ruling in HE. Conclusion NCCT-based radiological and radiomics features can improve HE prediction when added to clinical features. Visual Abstract