Leveraging Ensemble Models and Follow-up Data for Accurate Prediction of mRS Scores from Radiomic Features of DSC-PWI Images.
Predicting long-term clinical outcomes based on the early DSC PWI MRI scan is valuable for prognostication, resource management, clinical trials, and patient expectations. Current methods require subjective decisions about which imaging features to assess and may require time-consuming postprocessin...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1467 - 1484 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185280522&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280522 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2025 vid: 38 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185280522 185280522 185280522 10.1007/s10278-024-01280-x 185280522 ppf: 1467 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Leveraging Ensemble Models and Follow-up Data for Accurate Prediction of mRS Scores from Radiomic Features of DSC-PWI Images. aug: au: Yassin, Mazen M. Zaman, Asim Lu, Jiaxi Yang, Huihui Cao, Anbo Hassan, Haseeb Han, Taiyu Miao, Xiaoqiang Shi, Yongkang Guo, Yingwei Luo, Yu Kang, Yan affil: https://ror.org/01vy4gh70 School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, 518055, Shenzhen, China sug: subj: Ischemic Stroke Radiography Radiomics Ischemic Stroke Diagnosis Perfusion Magnetic Resonance Imaging Methods Scales After Care Prediction Models Ensemble Learning Human Stroke Patients Psychosocial Factors Magnetic Resonance Imaging Algorithms Analysis of Variance Post Hoc Analysis Descriptive Statistics Time Factors Machine Learning Funding Source ab: Predicting long-term clinical outcomes based on the early DSC PWI MRI scan is valuable for prognostication, resource management, clinical trials, and patient expectations. Current methods require subjective decisions about which imaging features to assess and may require time-consuming postprocessing. This study's goal was to predict multilabel 90-day modified Rankin Scale (mRS) score in acute ischemic stroke patients by combining ensemble models and different configurations of radiomic features generated from Dynamic susceptibility contrast perfusion-weighted imaging. In Follow-up studies, a total of 70 acute ischemic stroke (AIS) patients underwent magnetic resonance imaging within 24 hours poststroke and had a follow-up scan. In the single study, 150 DSC PWI Image scans for AIS patients. The DRF are extracted from DSC-PWI Scans. Then Lasso algorithm is applied for feature selection, then new features are generated from initial and follow-up scans. Then we applied different ensemble models to classify between three classes normal outcome (0, 1 mRS score), moderate outcome (2,3,4 mRS score), and severe outcome (5,6 mRS score). ANOVA and post-hoc Tukey HSD tests confirmed significant differences in model style performance across various studies and classification techniques. Stacking models consistently on average outperformed others, achieving an Accuracy of 0.68 ± 0.15, Precision of 0.68 ± 0.17, Recall of 0.65 ± 0.14, and F1 score of 0.63 ± 0.15 in the follow-up time study. Techniques like Bo_Smote showed significantly higher recall and F1 scores, highlighting their robustness and effectiveness in handling imbalanced data. Ensemble models, particularly Bagging and Stacking, demonstrated superior performance, achieving nearly 0.93 in Accuracy, 0.95 in Precision, 0.94 in Recall, and 0.94 in F1 metrics in follow-up conditions, significantly outperforming single models. Ensemble models based on radiomics generated from combining Initial and follow-up scans can be used to predict multilabel 90-day stroke outcomes with reduced subjectivity and user burden. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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