Diffusion-/perfusion-weighted imaging fusion to automatically identify stroke within 4.5 h

• Published in: European radiology 2024-10, Vol.34 (10), p.6808-6819
• Main Authors: Jiang, Liang, Sun, Jiarui, Wang, Yajing, Yang, Haodi, Chen, Yu-Chen, Peng, Mingyang, Zhang, Hong, Chen, Yang, Yin, Xindao
• Format: Article
• Language: English
• Subjects: Aged; Calibration; Classification; Decision making; Diagnostic Radiology; Diffusion Magnetic Resonance Imaging - methods; Female; Head and Neck; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Image segmentation; Imaging; Internal Medicine; Interventional Radiology; Labeling; Machine Learning; Male; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Multimodal Imaging - methods; Neuroradiology; Performance evaluation; Perfusion; Radiology; Regression models; Segmentation; Sensitivity analysis; Sensitivity and Specificity; Stroke; Stroke - diagnostic imaging; Support vector machines; Time Factors; Ultrasound
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-024-10619-5

Objectives We aimed to develop machine learning (ML) models based on diffusion- and perfusion-weighted imaging fusion (DP fusion) for identifying stroke within 4.5 h, to compare them with DWI- and/or PWI-based ML models, and to construct an automatic segmentation-classification model and compare with manual labeling methods. Methods ML models were developed from multimodal MRI datasets of acute stroke patients within 24 h of clear symptom onset from two centers. The processes included manual segmentation, registration, DP fusion, feature extraction, and model establishment (logistic regression (LR) and support vector machine (SVM)). A segmentation-classification model (X-Net) was proposed for automatically identifying stroke within 4.5 h. The area under the receiver operating characteristic curve (AUC), sensitivity, Dice coefficients, decision curve analysis, and calibration curves were used to evaluate model performance. Results A total of 418 patients (≤ 4.5 h: 214; > 4.5 h: 204) were evaluated. The DP fusion model achieved the highest AUC in identifying the onset time in the training (LR: 0.95; SVM: 0.92) and test sets (LR: 0.91; SVM: 0.90). The DP fusion-LR model displayed consistent positive and greater net benefits than other models across a broad range of risk thresholds. The calibration curve demonstrated the good calibration of the DP fusion-LR model (average absolute error: 0.049). The X-Net model obtained the highest Dice coefficients (DWI: 0.81; Tmax: 0.83) and achieved similar performance to manual labeling (AUC: 0.84). Conclusions The automatic segmentation-classification models based on DWI and PWI fusion images had high performance in identifying stroke within 4.5 h. Clinical relevance statement Perfusion-weighted imaging (PWI) fusion images had high performance in identifying stroke within 4.5 h. The automatic segmentation-classification models based on DWI and PWI fusion images could provide clinicians with decision-making guidance for acute stroke patients with unknown onset time. Key Points • The diffusion/perfusion-weighted imaging fusion model had the best performance in identifying stroke within 4.5 h. • The X-Net model had the highest Dice and achieved performance close to manual labeling in segmenting lesions of acute stroke. • The automatic segmentation-classification model based on DP fusion images performed well in identifying stroke within 4.5 h.