Radiomics-based prediction of hemorrhage expansion among patients with thrombolysis/thrombectomy related-hemorrhagic transformation using machine learning

 WHOM specifically is going to take the obvious next step and come up with a protocol that prevents hemorrhagic transformation? As is, this is useless. With NO LEADERSHIP IN STROKE nothing will occur. Another useless research prediction.

Radiomics-based prediction of hemorrhage expansion among patients with thrombolysis/thrombectomy related-hemorrhagic transformation using machine learning

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Junfeng Liu*, Wendan Tao*, Zhetao Wang, ...

First Published November 24, 2021 Research Article 

https://doi.org/10.1177/17562864211060029

Article information

Abstract

Introduction:

Patients with hemorrhagic transformation (HT) were reported to have hemorrhage expansion. However, identification these patients with high risk of hemorrhage expansion has not been well studied.

Objectives:

We aimed to develop a radiomic score to predict hemorrhage expansion after HT among patients treated with thrombolysis/thrombectomy during acute phase of ischemic stroke.

Methods:

A total of 104 patients with HT after reperfusion treatment from the West China hospital, Sichuan University, were retrospectively included in this study between 1 January 2012 and 31 December 2020. The preprocessed initial non-contrast-enhanced computed tomography (NECT) imaging brain images were used for radiomic feature extraction. A synthetic minority oversampling technique (SMOTE) was applied to the original data set. The after-SMOTE data set was randomly split into training and testing cohorts with an 8:2 ratio by a stratified random sampling method. The least absolute shrinkage and selection operator (LASSO) regression were applied to identify candidate radiomic features and construct the radiomic score. The performance of the score was evaluated by receiver operating characteristic (ROC) analysis and a calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical value of the model.

Results:

Among the 104 patients, 17 patients were identified with hemorrhage expansion after HT detection. A total of 154 candidate predictors were extracted from NECT images and five optimal features were ultimately included in the development of the radiomic score by using logistic regression machine-learning approach. The radiomic score showed good performance with high area under the curves in both the training data set (0.91, sensitivity: 0.83; specificity: 0.89), test data set (0.87, sensitivity: 0.60; specificity: 0.85), and original data set (0.82, sensitivity: 0.77; specificity: 0.78). The calibration curve and DCA also indicated that there was a high accuracy and clinical usefulness of the radiomic score for hemorrhage expansion prediction after HT.

Conclusions:

The currently established NECT-based radiomic score is valuable in predicting hemorrhage expansion after HT among patients treated with reperfusion treatment after ischemic stroke, which may aid clinicians in determining patients with HT who are most likely to benefit from anti-expansion treatment.

Keywords

hemorrhage expansion, hemorrhagic transformation, machine learning, radiomic score, thrombolysis/thrombectomy

Introduction

Hemorrhagic transformation (HT) is the most feared complication of intravenous thrombolytic therapy and mechanical thrombectomy after ischemic stroke.¹ Previous studies^2–4 reported that HT is associated with poor outcomes, especially symptomatic HT that has a mortality rate approaching 50% and significant morbidity with survival. Treatment approaches in these patients showed substantial variability across different studies,^5–7 and no established treatment has been recommended by current guidelines.(So you're toast if this occurs and your doctors know nothing on how to treat it.)

Hemorrhage expansion has been reported in patients diagnosed with symptomatic HT, and it occurs in 30% to 40% of patients.^5–7 It suggests a therapeutic opportunity exists in those patients. Theoretically, the risk of hemorrhage expansion may be greater in patients with asymptomatic HT who received reperfusion treatment, especially in patients with successful recanalization or with endothelial injury related to a neuro-interventional procedure.⁸ Given the high risk of ongoing bleeding after HT, early identification of patients with a potential risk of hemorrhage expansion is a potential target for therapeutic strategies.

The predictors and outcomes of hemorrhage expansion after HT have not been well studied. Prior studies^5–7 of hemorrhage expansion after thrombolysis only included patients with symptomatic HT and thus are weighted toward parenchymal hematoma (PH)-2. Recently, radiomic analysis was developed as a promising quantitative method for the objective assessment of the heterogeneity within lesions, which can capture image information not assessable by human eyes.^9,10 It has been proven to have a superior ability in prediction of extra-organ metastasis for cancers,^11,12 and hemorrhage expansion after spontaneous intracerebral hemorrhage.^13,14

In this study, we hypothesized that extraction of quantitative radiomic image features on non-contrast-enhanced computed tomography (NECT) scans and evaluation of these data by an automated machine learning methods might offer additional information in the prediction of hemorrhage expansion after HT. To test and evaluate this hypothesis, we aimed to develop a quantitative radiomic score to predict hemorrhage expansion in patients diagnosed with HT on NECT brain scans after thrombolysis and/or thrombectomy. Furthermore, we investigated whether the radiomic model could predict the functional outcomes at 3 months after stroke onset.

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