Imperial College London
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Dr Lucia M. Li

Faculty of Medicine, Department of Brain Sciences

Clinical Lecturer (Neurology)
 
 
 
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Contact

 

lucia.li

 
 
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Location

 

Burlington DanesHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Rosnati:2022:10.1007/978-3-031-17899-3_14,
author = {Rosnati, M and Soreq, E and Monteiro, M and Li, L and Graham, NSN and Zimmerman, K and Rossi, C and Carrara, G and Bertolini, G and Sharp, DJ and Glocker, B},
doi = {10.1007/978-3-031-17899-3_14},
pages = {135--146},
publisher = {Springer Nature Switzerland},
title = {Automatic lesion analysis for increased efficiency in outcome prediction of traumatic brain injury},
url = {http://dx.doi.org/10.1007/978-3-031-17899-3_14},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - The accurate prognosis for traumatic brain injury (TBI) patients is difficult yet essential to inform therapy, patient management, and long-term after-care. Patient characteristics such as age, motor and pupil responsiveness, hypoxia and hypotension, and radiological findings on computed tomography (CT), have been identified as important variables for TBI outcome prediction. CT is the acute imaging modality of choice in clinical practice because of its acquisition speed and widespread availability. However, this modality is mainly used for qualitative and semi-quantitative assessment, such as the Marshall scoring system, which is prone to subjectivity and human errors. This work explores the predictive power of imaging biomarkers extracted from routinely-acquired hospital admission CT scans using a state-of-the-art, deep learning TBI lesion segmentation method. We use lesion volumes and corresponding lesion statistics as inputs for an extended TBI outcome prediction model. We compare the predictive power of our proposed features to the Marshall score, independently and when paired with classic TBI biomarkers. We find that automatically extracted quantitative CT features perform similarly or better than the Marshall score in predicting unfavourable TBI outcomes. Leveraging automatic atlas alignment, we also identify frontal extra-axial lesions as important indicators of poor outcome. Our work may contribute to a better understanding of TBI, and provides new insights into how automated neuroimaging analysis can be used to improve prognostication after TBI.
AU  - Rosnati,M
AU  - Soreq,E
AU  - Monteiro,M
AU  - Li,L
AU  - Graham,NSN
AU  - Zimmerman,K
AU  - Rossi,C
AU  - Carrara,G
AU  - Bertolini,G
AU  - Sharp,DJ
AU  - Glocker,B
DO  - 10.1007/978-3-031-17899-3_14
EP  - 146
PB  - Springer Nature Switzerland
PY  - 2022///
SN  - 0302-9743
SP  - 135
TI  - Automatic lesion analysis for increased efficiency in outcome prediction of traumatic brain injury
UR  - http://dx.doi.org/10.1007/978-3-031-17899-3_14
UR  - https://link.springer.com/book/10.1007/978-3-031-17899-3
UR  - http://hdl.handle.net/10044/1/101067
ER  -
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