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Posterior circulation ischemic stroke: radiomics-based machine learning approach to identify onset time from magnetic resonance imaging

  • Diagnostic Neuroradiology
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Abstract

Purpose

Posterior circulation ischemic stroke (PCIS) possesses unique features. However, previous studies have primarily or exclusively relied on anterior circulation stroke cases to build machine learning (ML) models for predicting onset time. To date, there is no research reporting the effectiveness and stability of ML in identifying PCIS onset time. We aimed to build diffusion-weighted imaging-based ML models to identify the onset time of PCIS patients.

Methods

Consecutive PCIS patients within 24 h of definite symptom onset were included (112 in the training set and 49 in the independent test set). Images were processed as follows: volume of interest segmentation, image feature extraction, and feature selection. Five ML models, naïve Bayes, logistic regression, tree ensemble, k-nearest neighbor, and random forest, were built based on the training set to estimate the stroke onset time (binary classification: ≤ 4.5 h or > 4.5 h). Relative standard deviations (RSD), receiver operating characteristic (ROC) curves, and the calibration plot was performed to evaluate the stability and performance of the five models.

Results

The random forest model had the best performance in the test set, with the highest area under the curve (AUC, 0.840; 95% CI: 0.706, 0.974). This model also achieved the highest accuracy, sensitivity, specificity, positive predictive value, and negative predictive value (83.7%, 64.3%, 91.4%, 75.0%, and 86.5%, respectively). Furthermore, the model had high stability (RSD = 0.0094).

Conclusion

The PCIS case-based ML model was effective for estimating the symptom onset time and achieved considerably high specificity and stability.

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Data availability

Data reported in this study are available upon reasonable request to the corresponding author.

Abbreviations

ADC:

Apparent diffusion coefficient

AIS:

Acute ischemic stroke

AUC:

Area under the curve

CTP:

CT perfusion imaging

DWI:

Diffusion-weighted imaging

FLAIR:

Fluid-attenuated inversion recovery

KNN:

K-nearest neighbor

LR:

Logistic regression

ML:

Machine learning

NB:

Naïve Bayes

NIHSS:

National Institutes of Health Stroke Scale

NPV:

Negative predictive value

PCIS:

Posterior circulation ischemic stroke

PDM:

PWI-DWI mismatch

PPV:

Positive predictive value

PWI:

Perfusion-weighted imaging

RF:

Random forest

ROC:

Receiver operating characteristic

RSD:

Relative standard deviation

TE:

Tree ensemble

TSS:

Time-since-stroke

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Acknowledgements

We thank Dawei Yang, MD, for his suggestions on the Discussion section. We express our gratitude for the support provided by the Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support (ZYLX202101).

Funding

This study was funded by the Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support (ZYLX202101).

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Authors and Affiliations

  1. Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 95 YongAn Road, Xicheng District, Beijing, 100050, People’s Republic of China

    Zhenhao Liu, Shiyu Zhang, Yuxin Wang, Hui Xu, Jun Lu, Peng-Gang Qiao & Zhenghan Yang

  2. Department of Radiology, Affiliated Hospital of Changzhi Institute of Traditional Chinese Medicine, No. 2, Zifang Lane, Hero South Road, Luzhou District, Changzhi, 046000, People’s Republic of China

    Zhenhao Liu, Yongqiang Gao, Hong Jin, Yufeng Zhang, Hongyang Wu & Peipei Chen

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  1. Zhenhao Liu
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Correspondence to Peng-Gang Qiao or Zhenghan Yang.

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Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This retrospective study was approved by our Institutional Review Board (number 2021–014).

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The informed consent requirement was waived by the Ethics Committee due to the retrospective nature of the study.

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Zhenghan Yang reports funds from the Beijing Municipal Administration of Hospitals. The other authors have no relevant financial or non-financial interests to disclose.

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Liu, Z., Zhang, S., Wang, Y. et al. Posterior circulation ischemic stroke: radiomics-based machine learning approach to identify onset time from magnetic resonance imaging. Neuroradiology (2024). https://doi.org/10.1007/s00234-024-03353-8

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