Objective This study aimed to develop and validate a deep learning prediction model using longitudinal multimodal ultrasound imaging for early identification of treatment-sensitive and treatment-resistant carotid plaques in patients receiving lipid-lowering therapy. Materials and methods This prospective study enrolled 802 patients with vulnerable carotid plaques or stenosis ≥ 50%. Patients underwent serial multimodal ultrasound examinations, including B-mode imaging, superb microvascular imaging, and shear wave elastography at baseline and 3, 6, 9, and 12 months after initiating statin therapy. The dataset was divided into training and testing sets using stratified sampling with data augmentation. A hybrid DL model combining convolutional neural networks and long short-term memory networks analyzed longitudinal imaging sequences integrated with baseline clinical data. Five progressive prediction models were constructed for baseline and each follow-up time point, sharing identical architecture but trained independently on temporal sequences of varying lengths using 5-fold cross-validation. Model performance was assessed for discrimination ability, calibration consistency, and clinical utility. Results Five progressive prediction models demonstrated characteristic temporal performance patterns, with significant improvement from 3 to 6 months (AUC 0.866), followed by marginal gains. The 6-month model emerged as the most clinically practical assessment time point, achieving high specificity (93.7%) for early therapeutic decisions. Ablation experiments confirmed imaging features as primary predictive determinants, while attention mapping revealed consistent focus on plaque-adjacent regions, validating that treatment response prediction relies on morphological changes within target plaques. Conclusion A hybrid DL model enables reliable carotid plaque treatment response prediction within six months, optimizing personalized therapy through earlier identification of treatment-resistant patients. Critical relevance This study validates deep learning algorithms to predict carotid plaque treatment response within six months, advancing clinical radiology practice by enabling earlier therapeutic optimization through objective ultrasound-based assessment. Key Points <UnorderedList Mark="Bullet"> <ItemContent> Conventional imaging requires 12 months to reliably assess plaque treatment response. </ItemContent> <ItemContent> Deep learning model predicts treatment response at six months with high accuracy. </ItemContent> <ItemContent> Earlier prediction enables timely therapeutic adjustments for resistant patients. </ItemContent> </UnorderedList> Graphical Abstract

Deep learning-based early prediction of carotid plaque response to lipid-lowering therapy using longitudinal multimodal ultrasound imaging

期刊论文
出版模式：

开放获取
发表日期：

2026年4月21日

查看全文

Lulu Jiang,
Yaning Sun,
Wendi Huang,
Saisai Wang,
Danqin Pan,
Yanming Zhang,
Mengjie Liang

摘要

Objective

This study aimed to develop and validate a deep learning prediction model using longitudinal multimodal ultrasound imaging for early identification of treatment-sensitive and treatment-resistant carotid plaques in patients receiving lipid-lowering therapy.

Materials and methods

This prospective study enrolled 802 patients with vulnerable carotid plaques or stenosis ≥ 50%. Patients underwent serial multimodal ultrasound examinations, including B-mode imaging, superb microvascular imaging, and shear wave elastography at baseline and 3, 6, 9, and 12 months after initiating statin therapy. The dataset was divided into training and testing sets using stratified sampling with data augmentation. A hybrid DL model combining convolutional neural networks and long short-term memory networks analyzed longitudinal imaging sequences integrated with baseline clinical data. Five progressive prediction models were constructed for baseline and each follow-up time point, sharing identical architecture but trained independently on temporal sequences of varying lengths using 5-fold cross-validation. Model performance was assessed for discrimination ability, calibration consistency, and clinical utility.

Results

Five progressive prediction models demonstrated characteristic temporal performance patterns, with significant improvement from 3 to 6 months (AUC 0.866), followed by marginal gains. The 6-month model emerged as the most clinically practical assessment time point, achieving high specificity (93.7%) for early therapeutic decisions. Ablation experiments confirmed imaging features as primary predictive determinants, while attention mapping revealed consistent focus on plaque-adjacent regions, validating that treatment response prediction relies on morphological changes within target plaques.

Conclusion

A hybrid DL model enables reliable carotid plaque treatment response prediction within six months, optimizing personalized therapy through earlier identification of treatment-resistant patients.

Critical relevance

This study validates deep learning algorithms to predict carotid plaque treatment response within six months, advancing clinical radiology practice by enabling earlier therapeutic optimization through objective ultrasound-based assessment.

Key Points

Conventional imaging requires 12 months to reliably assess plaque treatment response.

Deep learning model predicts treatment response at six months with high accuracy.

Earlier prediction enables timely therapeutic adjustments for resistant patients.

Graphical Abstract