계명대학교 의학도서관 Repository

Machine Learning for Detecting Early Infarction in Acute Stroke with Non-Contrast-enhanced CT

Metadata Downloads
Author(s)
Wu QiuHulin KuangEricka TelegJohanna M OspelSung Il SohnMohammed AlmekhlafiMayank GoyalMichael D HillAndrew M DemchukBijoy K Menon
Keimyung Author(s)
Sohn, Sung Il
Department
Dept. of Neurology (신경과학)
Journal Title
Radiology
Issued Date
2020
Volume
294
Issue
3
Abstract
Background:
Identifying the presence and extent of infarcted brain tissue at baseline plays a crucial role in the treatment of patients with acute ischemic stroke (AIS). Patients with extensive infarction are unlikely to benefit from thrombolysis or thrombectomy procedures.

Purpose:
To develop an automated approach to detect and quantitate infarction by using non–contrast-enhanced CT scans in patients with AIS.

Materials and Methods:
Non–contrast-enhanced CT images in patients with AIS (<6 hours from symptom onset to CT) who also underwent diffusion-weighted (DW) MRI within 1 hour after AIS were obtained from May 2004 to July 2009 and were included in this retrospective study. Ischemic lesions manually contoured on DW MRI scans were used as the reference standard. An automatic segmentation approach involving machine learning (ML) was developed to detect infarction. Randomly selected nonenhanced CT images from 157 patients with the lesion labels manually contoured on DW MRI scans were used to train and validate the ML model; the remaining 100 patients independent of the derivation cohort were used for testing. The ML algorithm was quantitatively compared with the reference standard (DW MRI) by using Bland-Altman plots and Pearson correlation.

Results:
In 100 patients in the testing data set (median age, 69 years; interquartile range [IQR]: 59–76 years; 59 men), baseline non–contrast-enhanced CT was performed within a median time of 48 minutes from symptom onset (IQR, 27–93 minutes); baseline MRI was performed a median of 38 minutes (IQR, 24–48 minutes) later. The algorithm-detected lesion volume correlated with the reference standard of expert-contoured lesion volume in acute DW MRI scans (r = 0.76, P < .001). The mean difference between the algorithm-segmented volume (median, 15 mL; IQR, 9–38 mL) and the DW MRI volume (median, 19 mL; IQR, 5–43 mL) was 11 mL (P = .89).

Conclusion:
A machine learning approach for segmentation of infarction on non–contrast-enhanced CT images in patients with acute ischemic stroke showed good agreement with stroke volume on diffusion-weighted MRI scans.
Keimyung Author(s)(Kor)
손성일
Publisher
School of Medicine (의과대학)
Citation
Wu Qiu et al. (2020). Machine Learning for Detecting Early Infarction in Acute Stroke with Non-Contrast-enhanced CT. Radiology, 294(3), 638–644. doi: 10.1148/radiol.2020191193
Type
Article
ISSN
1527-1315
Source
https://pubs.rsna.org/doi/10.1148/radiol.2020191193
DOI
10.1148/radiol.2020191193
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/43285
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Neurology (신경과학)
공개 및 라이선스
  • 공개 구분공개
  • 엠바고Forever
파일 목록

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.