Evidence map›Paper›PMID 38689832›Full record

ArticleFrontiers in neuroinformatics2024

Automatic segmentation of hemorrhagic transformation on follow-up non-contrast CT after acute ischemic stroke.

Jiacheng Sun, Freda Werdiger, Christopher Blair, Chushuang Chen, Qing Yang, Andrew Bivard, Longting Lin, Mark Parsons

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.8field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 5 institutions in 1 country.

Jiacheng SunSydney Brain Centre, The Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia.
Freda WerdigerMelbourne Brain Centre at Royal Melbourne Hospital, Melbourne, VIC, Australia.
Christopher BlairSydney Brain Centre, The Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia.
Chushuang ChenSouth Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia.
Qing YangApollo Medical Imaging Technology Pty. Ltd., Melbourne, VIC, Australia.
Andrew BivardMelbourne Brain Centre at Royal Melbourne Hospital, Melbourne, VIC, Australia.
Longting LinSydney Brain Centre, The Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia.
Mark ParsonsSydney Brain Centre, The Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia.
UNSW Sydney · AUApollo Medical Imaging Technology (Australia) · AUSouth Western Sydney Local Health District · AUThe Royal Melbourne Hospital · AUUniversity of Melbourne · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Hemorrhagic transformation (HT) following reperfusion therapies is a serious complication for patients with acute ischemic stroke. Segmentation and quantification of hemorrhage provides critical insights into patients' condition and aids in prognosis. This study aims to automatically segment hemorrhagic regions on follow-up non-contrast head CT (NCCT) for stroke patients treated with endovascular thrombectomy (EVT). Methods: Patient data were collected from 10 stroke centers across two countries. We propose a semi-automated approach with adaptive thresholding methods, eliminating the need for extensive training data and reducing computational demands. We used Dice Similarity Coefficient (DSC) and Lin's Concordance Correlation Coefficient (Lin's CCC) to evaluate the performance of the algorithm. Results: A total of 51 patients were included, with 28 Type 2 hemorrhagic infarction (HI2) cases and 23 parenchymal hematoma (PH) cases. The algorithm achieved a mean DSC of 0.66 ± 0.17. Notably, performance was superior for PH cases (mean DSC of 0.73 ± 0.14) compared to HI2 cases (mean DSC of 0.61 ± 0.18). Lin's CCC was 0.88 (95% CI 0.79-0.93), indicating a strong agreement between the algorithm's results and the ground truth. In addition, the algorithm demonstrated excellent processing time, with an average of 2.7 s for each patient case. Conclusion: To our knowledge, this is the first study to perform automated segmentation of post-treatment hemorrhage for acute stroke patients and evaluate the performance based on the radiological severity of HT. This rapid and effective tool has the potential to assist with predicting prognosis in stroke patients with HT after EVT.

Indexed as

acute ischemic strokeendovascular thrombectomyhemorrhagic transformationimage segmentationnon-contrast CTvolume quantification

Identifiers

PMID38689832
PMCPMC11058994
OpenAlexW4394850200

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.