P004 ASPECTS rating on CT versus MRI depends on baseline lesion size – Implications for treatment selection in large ischemic stroke
<h3>Introduction</h3> Thrombectomy is of benefit for patients with large ischemic core, but inclusion in recent trials was based on different imaging criteria utilizing MRI, perfusion imaging, and/or non-enhanced CT (NECT) <h3>Aim of Study</h3> The purpose of this study was to directly compare the agreement of different ASPECTS rating methods according to the baseline core volume in patients receiving both MRI and CT on admission. We hypothesized that the agreement between CT- and DWI-ASPECTS declines with increasing baseline core volumes. <h3>Methods</h3> Observational retrospective study of anterior circulation stroke patients triaged by both multimodal-CT and MRI at baseline. Core lesion volume was defined using DWI, and ASPECTS was rated independently on NECT and DWI maps. The agreement between these ratings was tested according to baseline core volume. <h3>Results</h3> 76 patients were included. The rate of patients with a low DWI-ASPECTS≤5 and large core of >50ml was 41%/32%, respectively. The median intermodal ASPECTS difference was 2 (IQR:1-3) for patients with core volume <50ml, and 3 (IQR:2-4) for patients with core volume >50ml, which was different (p=0.01). In patients with a DWI-ASPECTS≤5, only 29% also had a NECT-ASPECTS≤5, and 60% (p<0.001) when applying a modified DWI-ASPECTS (>1/3 ASPECTS region). <h3>Conclusion</h3> The agreement between NECT and DWI-ASPECTS was lower in patients with larger core volumes, which might directly affect comparability depending on the utilized modality. Using a modified DWI-ASPECTS (>1/3 ASPECTS region) increased the agreement to NECT-ASPECTS, but still, 40% of patients with a low DWI-ASPECTS had an NECT-ASPECTS of ≥6. <h3>Disclosure of Interest</h3> no.
Authors
- André Kemmling (ORCID: https://orcid.org/0000-0001-8930-1894)
- Tobias Djamsched Faizy (ORCID: https://orcid.org/0000-0002-1631-2020)
- Uta Hanning (ORCID: https://orcid.org/0000-0002-7543-8555)
- Michael H. Lev (ORCID: https://orcid.org/0000-0003-0236-7319)
- Karsten Alfke (ORCID: https://orcid.org/0009-0001-4223-6404)
- Gabriel Broocks (ORCID: https://orcid.org/0000-0002-7575-9850)
- Jens Fiehler (ORCID: https://orcid.org/0000-0001-8533-7478)
- Robert William Regenhardt (ORCID: https://orcid.org/0000-0003-2958-3484)
- Paul Steffen (ORCID: https://orcid.org/0000-0001-7057-9917)
- Lukas Meyer
Institutions
- Harvard University (US)
- Universität Hamburg (DE)
- University of Münster (DE)
- Massachusetts General Hospital (US)
- MSH Medical School Hamburg – University of Applied Sciences and Medical University (DE)
- Helios Hospital Schwerin (DE)
- University Medical Center Hamburg-Eppendorf (DE)
- FH Münster University of Applied Sciences (DE)
Publication Details
- Journal
- Abstracts
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1136/jnis-2024-esmint.41
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.40