{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T13:22:26Z","timestamp":1792588946973,"version":"4.5.0"},"reference-count":24,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"7","license":[{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Stroke"],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:sec>\n            <jats:title>Background and Purpose:<\/jats:title>\n            <jats:p>The ISLES challenge (Ischemic Stroke Lesion Segmentation) enables globally diverse teams to compete to develop advanced tools for stroke lesion analysis with machine learning. Detection of irreversibly damaged tissue on computed tomography perfusion (CTP) is often necessary to determine eligibility for late-time-window thrombectomy. Therefore, the aim of ISLES-2018 was to segment infarcted tissue on CTP based on diffusion-weighted imaging as a reference standard.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods:<\/jats:title>\n            <jats:p>The data, from 4 centers, consisted of 103 cases of acute anterior circulation large artery occlusion stroke who underwent diffusion-weighted imaging rapidly after CTP. Diffusion-weighted imaging lesion segmentation was performed manually and acted as a reference standard. The data were separated into 63 cases for training and 40 for testing, upon which quality metrics (dice score coefficient, Hausdorff distance, absolute lesion volume difference, etc) were computed to rank methods based on their overall performance.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results:<\/jats:title>\n            <jats:p>Twenty-four different teams participated in the challenge. Median time to CTP was 185 minutes (interquartile range, 180\u2013238), the time between CTP and magnetic resonance imaging was 36 minutes (interquartile range, 25\u201379), and the median infarct lesion size was 15.2 mL (interquartile range, 5.7\u201345). The best performance for Dice score coefficient and absolute volume difference were 0.51 and 10.1 mL, respectively, from different teams. Based on the ranking criteria, the top team\u2019s algorithm demonstrated for average Dice score coefficient and average absolute volume difference 0.51 and 10.2 mL, respectively, outperforming the conventional threshold-based method (dice score coefficient, 0.3; volume difference, 15.3). Diverse algorithms were used, almost all based on deep learning, with top-ranked approaches making use of the raw perfusion data as well as methods to synthetically generate complementary information to boost prediction performance.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions:<\/jats:title>\n            <jats:p>Machine learning methods may predict infarcted tissue from CTP with improved accuracy compared with threshold-based methods used in clinical routine. This dataset will remain public and can be used to test improvement in algorithms over time.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/strokeaha.120.030696","type":"journal-article","created":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T09:01:00Z","timestamp":1620378060000},"page":"2328-2337","update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":89,"title":["Predicting Infarct Core From Computed Tomography Perfusion in Acute Ischemia With Machine Learning: Lessons From the ISLES Challenge"],"prefix":"10.1161","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9431-1069","authenticated-orcid":true,"given":"Arsany","family":"Hakim","sequence":"first","affiliation":[{"name":"University Institute of Diagnostic and Interventional Neuroradiology, Bern University Hospital, Inselspital (A.H., R.W.), University of Bern, Switzerland."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u00f8ren","family":"Christensen","sequence":"additional","affiliation":[{"name":"Stanford Stroke Center, Palo Alto, CA (S.C., M.G.L.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Winzeck","sequence":"additional","affiliation":[{"name":"University Division of Anaesthesia, Department of Medicine, University of Cambridge, United Kingdom (S.W.)."},{"name":"BioMedIA, Department of Computing, Imperial College London, United Kingdom (S.W.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3545-6927","authenticated-orcid":true,"given":"Maarten G.","family":"Lansberg","sequence":"additional","affiliation":[{"name":"Stanford Stroke Center, Palo Alto, CA (S.C., M.G.L.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8874-2487","authenticated-orcid":true,"given":"Mark W.","family":"Parsons","sequence":"additional","affiliation":[{"name":"Department of Neurology, Melbourne Brain Center, Royal Melbourne Hospital and University of Melbourne, Australia (M.W.P.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Lucas","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University of L\u00fcbeck, Germany (C.L.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2266-8867","authenticated-orcid":true,"given":"David","family":"Robben","sequence":"additional","affiliation":[{"name":"ESAT-PSI, KU Leuven, Belgium (D.R.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roland","family":"Wiest","sequence":"additional","affiliation":[{"name":"University Institute of Diagnostic and Interventional Neuroradiology, Bern University Hospital, Inselspital (A.H., R.W.), University of Bern, Switzerland."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mauricio","family":"Reyes","sequence":"additional","affiliation":[{"name":"ARTORG Center for Biomedical Engineering Research (M.R.), University of Bern, Switzerland."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5781-8848","authenticated-orcid":true,"given":"Greg","family":"Zaharchuk","sequence":"additional","affiliation":[{"name":"Department of Radiology, Stanford University, CA (G.Z.)."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1706442"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1713973"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1161\/STR.0000000000000158"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.1177\/0271678X15610586"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.116.015472"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.116.013147"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.1161\/strokeaha.117.016669"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.07.009"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2018.00679"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2018.01060"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101851"},{"key":"e_1_3_5_13_2","volume-title":"Multiscale Multimodal Medical Imaging","author":"Chen Y","year":"2019","unstructured":"Chen, Y, Chen, J, Wei, D, Li, Y, Zheng, Y. 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