Key result
Machine learning analysis of platelet-stained thrombi classifies stroke patients with cancer with ~0.95 AUC.
Why the study?
The study aimed to develop and validate machine learning models to diagnose ischemic stroke patients with cancer by analyzing histopathologic images of thrombi obtained during endovascular thrombectomy.
Does a machine learning model analyzing thrombi from thrombectomy accurately predict underlying cancer in patients with acute ischemic stroke?
Observational (n=182)
Yes
Does a machine learning model analyzing thrombi from thrombectomy accurately predict underlying cancer in patients with acute ischemic stroke?
Machine learning analysis of platelet-stained thrombi obtained during stroke thrombectomy can accurately predict the presence of underlying or occult cancer.
Answers are grounded in this paper and cite their sources.
May aid occult cancer detection via thrombus ML post-thrombectomy; hypothesis-generating and requires prospective validation before any practice change.
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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Heo et al. (2023) conducted an observational in Acute ischemic stroke (n=182). Machine learning model of platelet-stained thrombi was evaluated on Classification of patients with cancer (AUC 0.949, 95% CI 0.891-1.000). A machine learning model analyzing platelet-stained thrombi from endovascular thrombectomy accurately classified stroke patients with cancer (AUC 0.949; 95% CI 0.891-1.000 in external validation).
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