Multimodal CT radiomics-clinical ensemble machine learning model effectively predicts futile recanalization after endovascular treatment of acute ischemic stroke

 Did you even objectively identify futile recanalization? I don't think you identified cause and effect properly, measuring Rankin scores has nothing directly to do with reperfusion! My god, the blithering stupidity out there is astounding!

 What followup research did you do ensure reperfusion will work completely every time? Oh NO, YOU INCOMPETENTLY DID NOTHING, right? Predicting failure is totally fucking useless! 

But it probably is because you did NOTHING to stop the 5 causes of the neuronal cascade of death in the first week and thus letting die hundreds of millions to billions of neurons!

You really don't  know what the fuck you are doing in stroke, so get the hell out!

Multimodal CT radiomics-clinical ensemble machine learning model effectively predicts futile recanalization after endovascular treatment of acute ischemic stroke

• 

  Zhenxiong Wang 1†

• Y

  Yidong Gao 1†

• P

  Pan Xu 2†

• D

  Di Wu 3

• W

  Wuying Li 2

• H

  Huameng Huang 2

• W

  Weihua Deng 2

• H

  Honggang Xu 1

• X

  Xinhua Wei 1

• 

  Xing Li 2*

• 1. Department of Radiology, Guangzhou First People's Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China

• 2. Department of Neurology, Guangzhou First People's Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China

Abstract

Backgrounds: 

Futile recanalization (FR) poses a significant challenge in endovascular treatment and there is a lack of reliable predictive models for assessing treatment outcomes in stroke. The aim of this study is to develop a robust CT radiomics-clinical ensemble model that predicts FR in patients with acute ischemic stroke (AIS) following endovascular treatment (EVT) utilizing machine learning techniques.

Methods: 

This study enrolled 101 patients diagnosed with AIS who underwent successful EVT. A total of 946 radiomics features were, respectively, extracted from non-contrast CT (NCCT), contrast-enhanced CT (CECT), and various CT perfusion maps (CBF, CBV, MTT, and TTP) using PyRadiomics prior to the endovascular intervention. Demographic characteristics, along with baseline clinical, laboratory, and angiographic variables, were incorporated as clinical features in the model analysis. Feature engineering was performed using SelectKBest. Five traditional machine learning algorithms were employed for modeling. The dataset was randomly split into a training cohort (n = 71, 70%) and an internal validation cohort (n = 30, 30%). Receiver operating characteristic (ROC) curves were utilized to evaluate the performance of each model.

Results: 

Among the 101 patients, FR occurred in 66 individuals (65%), as determined by the modified Rankin Scale (mRS) at 90 days. The ensemble model integrating clinical data, NCCT, and CBV achieved the highest performance, with an area under the curve (AUC) of 0.918 using the CatBoost algorithm.

Conclusion: 

The multimodal CT radiomics-clinical ensemble machine learning model demonstrated excellent predictive capability for identifying FR in AIS patients with large vessel occlusion prior to EVT.