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Cancer Prediction With Machine Learning of Thrombi From Thrombectomy in Stroke: Multicenter Development and Validation

 JoonNyung Heo  ;  Hyungwoo Lee  ;  Young Seog  ;  Sungeun Kim  ;  Jang-Hyun Baek  ;  Hyungjong Park  ;  Kwon-Duk Seo  ;  Gyu Sik Kim  ;  Han-Jin Cho  ;  Minyoul Baik  ;  Joonsang Yoo  ;  Jinkwon Kim  ;  Jun Lee  ;  Yoonkyung Chang  ;  Tae-Jin Song  ;  Jung Hwa Seo  ;  Seong Hwan Ahn  ;  Heow Won Lee  ;  Il Kwon  ;  Eunjeong Park  ;  Byung Moon Kim  ;  Dong Joon Kim  ;  Young Dae Kim  ;  Hyo Suk Nam 
 STROKE, Vol.54(8) : 2105-2113, 2023-08 
Journal Title
Issue Date
Humans ; Ischemic Stroke* / complications ; Machine Learning ; Neoplasms* / complications ; Prospective Studies ; Retrospective Studies ; Stroke* / etiology ; Thrombectomy / methods ; Thrombosis* / pathology
machine learning ; stroke ; thrombectomy
Background: We aimed to develop and validate machine learning models to diagnose patients with ischemic stroke with cancer through the analysis of histopathologic images of thrombi obtained during endovascular thrombectomy.

Methods: This was a retrospective study using a prospective multicenter registry which enrolled consecutive patients with acute ischemic stroke from South Korea who underwent endovascular thrombectomy. This study included patients admitted between July 1, 2017 and December 31, 2021 from 6 academic university hospitals. Whole-slide scanning was performed for immunohistochemically stained thrombi. Machine learning models were developed using transfer learning with image slices as input to classify patients into 2 groups: cancer group or other determined cause group. The models were developed and internally validated using thrombi from patients of the primary center, and external validation was conducted in 5 centers. The model was also applied to patients with hidden cancer who were diagnosed with cancer within 1 month of their index stroke.

Results: The study included 70 561 images from 182 patients in both internal and external datasets (119 patients in internal and 63 in external). Machine learning models were developed for each immunohistochemical staining using antibodies against platelets, fibrin, and erythrocytes. The platelet model demonstrated consistently high accuracy in classifying patients with cancer, with area under the receiver operating characteristic curve of 0.986 (95% CI, 0.983-0.989) during training, 0.954 (95% CI, 0.937-0.972) during internal validation, and 0.949 (95% CI, 0.891-1.000) during external validation. When applied to patients with occult cancer, the model accurately predicted the presence of cancer with high probabilities ranging from 88.5% to 99.2%.

Conclusions: Machine learning models may be used for prediction of cancer as the underlying cause or detection of occult cancer, using platelet-stained immunohistochemical slide images of thrombi obtained during endovascular thrombectomy.
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Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
Yonsei Authors
Kwon, Il(권일) ORCID logo https://orcid.org/0000-0001-9449-5646
Kim, Dong Joon(김동준) ORCID logo https://orcid.org/0000-0002-7035-087X
Kim, Byung Moon(김병문) ORCID logo https://orcid.org/0000-0001-8593-6841
Kim, Young Dae(김영대) ORCID logo https://orcid.org/0000-0001-5750-2616
Kim, Jinkwon(김진권) ORCID logo https://orcid.org/0000-0003-0156-9736
Nam, Hyo Suk(남효석) ORCID logo https://orcid.org/0000-0002-4415-3995
Park, Eunjeong(박은정)
Baik, Minyoul(백민렬)
Yoo, Joon Sang(유준상) ORCID logo https://orcid.org/0000-0003-1169-6798
Lee, Heow Won(이효원)
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