Ischemic Stroke Outcome Prediction with Diversity Features from Whole Brain Tissue Using Deep Learning Network

Overview

Journal Front Neurol

Specialty Neurology

Date 2024 May 20

PMID 38765270

Authors

Yingjian Yang

Yingwei Guo

Affiliations

Soon will be listed here.

Abstract

Objectives: This study proposed an outcome prediction method to improve the accuracy and efficacy of ischemic stroke outcome prediction based on the diversity of whole brain features, without using basic information about patients and image features in lesions.

Design: In this study, we directly extracted dynamic radiomics features (DRFs) from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) and further extracted static radiomics features (SRFs) and static encoding features (SEFs) from the minimum intensity projection (MinIP) map, which was generated from the time dimension of DSC-PWI images. After selecting whole brain features F from the combinations of DRFs, SRFs, and SEFs by the Lasso algorithm, various machine and deep learning models were used to evaluate the role of F in predicting stroke outcomes.

Results: The experimental results show that the feature F generated from DRFs, SRFs, and SEFs (Resnet 18) outperformed other single and combination features and achieved the best mean score of 0.971 both on machine learning models and deep learning models and the 95% CI were (0.703, 0.877) and (0.92, 0.983), respectively. Besides, the deep learning models generally performed better than the machine learning models.

Conclusion: The method used in our study can achieve an accurate assessment of stroke outcomes without segmentation of ischemic lesions, which is of great significance for rapid, efficient, and accurate clinical stroke treatment.

Citing Articles

Stroke-SCORE: Personalizing Acute Ischemic Stroke Treatment to Improve Patient Outcomes.

Seetge J, Cseke B, Karadi Z, Bosnyak E, Szapary L J Pers Med. 2025; 15(1.

PMID: 39852210 PMC: 11766924. DOI: 10.3390/jpm15010018.

Blood-brain barrier breakdown in brain ischemia: Insights from MRI perfusion imaging.

Sasannia S, Leigh R, Bastani P, Shin H, van Zijl P, Knutsson L Neurotherapeutics. 2024; 22(1):e00516.

PMID: 39709246 PMC: 11840350. DOI: 10.1016/j.neurot.2024.e00516.

References

Brugnara G, Neuberger U, Mahmutoglu M, Foltyn M, Herweh C, Nagel S . Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning. Stroke. 2020; 51(12):3541-3551. DOI: 10.1161/STROKEAHA.120.030287. View

Quan G, Ban R, Ren J, Liu Y, Wang W, Dai S . FLAIR and ADC Image-Based Radiomics Features as Predictive Biomarkers of Unfavorable Outcome in Patients With Acute Ischemic Stroke. Front Neurosci. 2021; 15:730879. PMC: 8483716. DOI: 10.3389/fnins.2021.730879. View

Osama S, Zafar K, Sadiq M . Predicting Clinical Outcome in Acute Ischemic Stroke Using Parallel Multi-parametric Feature Embedded Siamese Network. Diagnostics (Basel). 2020; 10(11). PMC: 7690444. DOI: 10.3390/diagnostics10110858. View

Fang G, Huang Z, Wang Z . Predicting Ischemic Stroke Outcome Using Deep Learning Approaches. Front Genet. 2022; 12:827522. PMC: 8818957. DOI: 10.3389/fgene.2021.827522. View

Campbell B, De Silva D, Macleod M, Coutts S, Schwamm L, Davis S . Ischaemic stroke. Nat Rev Dis Primers. 2019; 5(1):70. DOI: 10.1038/s41572-019-0118-8. View

Oosterveer D, Wermer M, Volker G, Vliet Vlieland T . Are There Differences in Long-Term Functioning and Recovery Between Hemorrhagic and Ischemic Stroke Patients Receiving Rehabilitation?. J Stroke Cerebrovasc Dis. 2022; 31(3):106294. DOI: 10.1016/j.jstrokecerebrovasdis.2021.106294. View

Smith S, Jenkinson M, Woolrich M, Beckmann C, Behrens T, Johansen-Berg H . Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004; 23 Suppl 1:S208-19. DOI: 10.1016/j.neuroimage.2004.07.051. View

Guo Y, Yang Y, Cao F, Liu Y, Li W, Yang C . Radiomics features of DSC-PWI in time dimension may provide a new chance to identify ischemic stroke. Front Neurol. 2022; 13:889090. PMC: 9672479. DOI: 10.3389/fneur.2022.889090. View

Yu H, Wang Z, Sun Y, Bo W, Duan K, Song C . Prognosis of ischemic stroke predicted by machine learning based on multi-modal MRI radiomics. Front Psychiatry. 2023; 13:1105496. PMC: 9868394. DOI: 10.3389/fpsyt.2022.1105496. View

10.

Klein S, Staring M, Murphy K, Viergever M, Pluim J . elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging. 2009; 29(1):196-205. DOI: 10.1109/TMI.2009.2035616. View

11.

Chen Y, Chen Y, Chen Y, Huang Y, Wong H, Yan J . Deep Learning-Based Brain Computed Tomography Image Classification with Hyperparameter Optimization through Transfer Learning for Stroke. Diagnostics (Basel). 2022; 12(4). PMC: 9026481. DOI: 10.3390/diagnostics12040807. View

12.

Malik A, Drumm B, DAnna L, Brooks I, Low B, Raha O . Mechanical thrombectomy in acute basilar artery stroke: a systematic review and Meta-analysis of randomized controlled trials. BMC Neurol. 2022; 22(1):415. PMC: 9644544. DOI: 10.1186/s12883-022-02953-2. View

13.

Wong K, Cummock J, Li G, Ghosh R, Xu P, Volpi J . Automatic Segmentation in Acute Ischemic Stroke: Prognostic Significance of Topological Stroke Volumes on Stroke Outcome. Stroke. 2022; 53(9):2896-2905. DOI: 10.1161/STROKEAHA.121.037982. View

14.

Xie Y, Jiang B, Gong E, Li Y, Zhu G, Michel P . JOURNAL CLUB: Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information. AJR Am J Roentgenol. 2018; 212(1):44-51. DOI: 10.2214/AJR.18.20260. View

15.

Guo Y, Yang Y, Wang M, Luo Y, Guo J, Cao F . The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction. Life (Basel). 2022; 12(11). PMC: 9694195. DOI: 10.3390/life12111847. View

16.

Ernst M, Boers A, Forkert N, Berkhemer O, Roos Y, Dippel D . Impact of Ischemic Lesion Location on the mRS Score in Patients with Ischemic Stroke: A Voxel-Based Approach. AJNR Am J Neuroradiol. 2018; 39(11):1989-1994. PMC: 7655370. DOI: 10.3174/ajnr.A5821. View

17.

Moulton E, Valabregue R, Piotin M, Marnat G, Saleme S, Lapergue B . Interpretable deep learning for the prognosis of long-term functional outcome post-stroke using acute diffusion weighted imaging. J Cereb Blood Flow Metab. 2022; 43(2):198-209. PMC: 9903217. DOI: 10.1177/0271678X221129230. View

18.

Yoo A, Barak E, Copen W, Kamalian S, Gharai L, Pervez M . Combining acute diffusion-weighted imaging and mean transmit time lesion volumes with National Institutes of Health Stroke Scale Score improves the prediction of acute stroke outcome. Stroke. 2010; 41(8):1728-35. DOI: 10.1161/STROKEAHA.110.582874. View

19.

Meyer-Wiethe K, Cangur H, Schindler A, Koch C, Seidel G . Ultrasound perfusion imaging: determination of thresholds for the identification of critically disturbed perfusion in acute ischemic stroke--a pilot study. Ultrasound Med Biol. 2007; 33(6):851-6. DOI: 10.1016/j.ultrasmedbio.2006.12.006. View

20.

Guo Y, Yang Y, Cao F, Li W, Wang M, Luo Y . Novel Survival Features Generated by Clinical Text Information and Radiomics Features May Improve the Prediction of Ischemic Stroke Outcome. Diagnostics (Basel). 2022; 12(7). PMC: 9324145. DOI: 10.3390/diagnostics12071664. View