A Focus on the Role of DSC-PWI Dynamic Radiomics Features in Diagnosis and Outcome Prediction of Ischemic Stroke

Yingwei Guo, Yingjian Yang, Fengqiu Cao, Mingming Wang, Yu Luo, Jia Guo, Yang Liu, Xueqiang Zeng et al.

Journal of Clinical Medicine 2022 article cited by 23

Source Open-access PDF More in Medicine & Health

importance 6/10 ai assisted Radiomics and Machine Learning in Medical Imaging stroke radiomics stage: research

Problem

Accurately detecting ischemic stroke, assessing neurological impairment, and predicting patient outcome from brain perfusion imaging.

Why it matters

Reliable stroke detection and outcome prediction are of great clinical value for treatment planning.

What the AI did

Lasso and five dimension-reduction algorithms selected dynamic radiomics features, which were evaluated with ten machine learning models using ten-fold cross-validation.

Results

Best AUCs in group_A: 0.873 stroke detection, 0.795 NIHSS assessment, 0.818 outcome prediction

Impact

Radiomics plus ML achieved moderate-to-good cross-validated AUCs for stroke detection and outcome prediction.

Record

Venue	Journal of Clinical Medicine
Published	2022
Work type	article
Authors	Yingwei Guo, Yingjian Yang, Fengqiu Cao, Mingming Wang, Yu Luo, Jia Guo, Yang Liu, Xueqiang Zeng et al.
Citations	23 (OpenAlex, at import time)
References	66
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.3390/jcm11185364
Open access	gold
Keywords	radiomics, ischemic stroke, machine learning, outcome prediction, DSC-PWI

Importance rating

6 / 10

significant — thousands to millions affected

Stroke detection and outcome prediction described as of great clinical value for treatment planning; no population scale stated in abstract.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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