Deans' stroke musings: Radiomics-based machine learning model for predicting clinically ineffective reperfusion in acute ischaemic stroke patients after endovascular treatment

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Friday, September 26, 2025

Radiomics-based machine learning model for predicting clinically ineffective reperfusion in acute ischaemic stroke patients after endovascular treatment

Predicting rather than preventing the problem! YOU'RE FIRED FOR EXTREME INCOMPETENCE!

Radiomics-based machine learning model for predicting clinically ineffective reperfusion in acute ischaemic stroke patients after endovascular treatment

Xiaolong Hu^1,2^†

Suya Li²^†

Shifei Ye²

Zhiliang Ding³

Peng Li²^*

Yibin Fang^1,2^*

¹Tongji University Affiliated Shanghai 4th People’s Hospital, Tongji University School of Medicine, Shanghai, China
²Department of Neurovascular Disease, Tongji University Affiliated Shanghai 4th People’s Hospital, Shanghai, China
³Nanjing Medical University Affiliated Suzhou Municipal Hospital, Nanjing, China

Background: Patients with acute ischaemic stroke (AIS) undergoing endovascular treatment may have a poor prognosis, even with successful recanalization. This study aims to evaluate a machine learning model based on CT-thrombosis radiomics to assess clinically ineffective reperfusion (CIR) after endovascular treatment (EVT) in patients with AIS.

Methods: A total of 144 patients from two centres were included in this study, spanning from December 2021 to October 2024. The participants were randomly divided into a training set (70%) and a test set (30%). Patient outcomes were defined as clinically ineffective reperfusion (thrombolysis in cerebral infarction, TICI ≥2b, three-month post-surgery modified Rankin Scale, mRS ≥3) and effective reperfusion (TICI ≥2b, three-month post-surgery mRS <3). A total of 1,702 features were extracted from the intrathrombus and perithrombus regions. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection to construct the machine learning model, with the AUC of the receiver operating characteristic (ROC) curve used for model evaluation.

Results: In the test set, the random forest (RF) model demonstrated the highest diagnostic performance among all the models (RF_INTRA AUC = 0.78, RF_PERI AUC = 0.76, RF_F AUC = 0.83).

Conclusion: The machine learning model based on intrathrombus and perithrombus radiomics features can accurately predict clinically ineffective reperfusion in patients after EVT. However, further study is needed to validate these findings in larger, independent cohorts and explore the broader clinical applicability of the model.