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Prediction of cardiovascular events after carotid endarterectomy using pathological images and clinical data
Shuya Ishida1, Kento Morita2,3, Kinta Hatakeyama4
1Graduate School of Engineering, Mie University, 1577, Kurimamachiya-Cho, Tsu, Mie, 514-8507, Japan.
Insights
Predicting cardiovascular events after carotid endarterectomy (CEA) is possible using pathological images and clinical data. This combined approach improves prediction accuracy for better patient outcomes.
Area of Science:
- Cardiovascular Medicine
- Pathology
- Medical Imaging
- Machine Learning
Background:
- Carotid endarterectomy (CEA) treats carotid artery stenosis.
- Post-CEA cardiovascular events lack clear prognostic factors.
- Predicting these events is crucial for patient management.
Purpose of the Study:
- To identify predictive factors for cardiovascular events post-CEA.
- To predict one-year cardiovascular events using pathological images and clinical data.
- To enhance prognostic accuracy for CEA patients.
Main Methods:
- A two-step method combining image-based risk scores and clinical data.
- Anomaly detection model trained on pathological images to compute risk scores.
- Statistical machine learning classifier to predict patient prognosis.
Main Results:
- The combined approach achieved an 81.9% AUC and 84.1% accuracy.
- Performance surpassed predictions using only images or clinical data.
- Key histopathological features identified: hemorrhage, lymphocytic infiltration, hemosiderin.
Conclusions:
- Predicting CEA patient prognosis using pathological images and clinical data is feasible.
- Identified histopathological features offer insights into event development.
- Findings may guide preventive strategies for plaque progression.
Purpose:
Carotid endarterectomy (CEA) is a surgical treatment for carotid artery stenosis. After CEA, some patients experience cardiovascular events (myocardial infarction, stroke, etc.); however, the prognostic factor has yet to be revealed. Therefore, this study explores the predictive factors in pathological images and predicts cardiovascular events within one year after CEA using pathological images of carotid plaques and patients' clinical data.
Method:
This paper proposes a two-step method to predict the prognosis of CEA patients. The proposed method first computes the pathological risk score using an anomaly detection model trained using pathological images of patients without cardiovascular events. By concatenating the obtained image-based risk score with a patient's clinical data, a statistical machine learning-based classifier predicts the patient's prognosis.
Results:
We evaluate the proposed method on a dataset containing 120 patients without cardiovascular events and 21 patients with events. The combination of autoencoder as the anomaly detection model and XGBoost as the classification model obtained the best results: area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, and F1-score were 81.9%, 84.1%, 79.1%, 86.3%, and 76.6%, respectively. These values were superior to those obtained using pathological images or clinical data alone.
Conclusion:
We showed the feasibility of predicting CEA patient's long-term prognosis using pathological images and clinical data. Our results revealed some histopathological features related to cardiovascular events: plaque hemorrhage (thrombus), lymphocytic infiltration, and hemosiderin deposition, which will contribute to developing preventive treatment methods for plaque development and progression.
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