The research, led by Mingjing Lu, Zhongjian Xu, and Tang Hanfei, with senior authors Dehai Lang, Guo Daqiao, and Guofu Wang, took a deliberately dual approach. Rather than betting on a single machine learning paradigm, the investigators built and compared two complementary pipelines. The first was a classical radiomics pipeline, in which hand-engineered quantitative features—describing the shape, texture, and intensity distribution of the plaque—are extracted from manually defined regions of interest on CT angiography images. The second was a deep learning pipeline, in which convolutional neural networks learn discriminative image patterns directly from pixel data without explicit feature engineering. Both pipelines were trained to answer the same clinically critical question: is this plaque histologically stable or vulnerable, as confirmed by pathology after carotid endarterectomy?

The evidence base for the radiomics arm came from a retrospective multi-center cohort of 260 consecutive patients who underwent carotid endarterectomy, meaning that every plaque in the dataset had a gold-standard pathological diagnosis of stability or vulnerability. Of these, 200 patients were used for model development and internal validation, while 60 patients were held out entirely for external validation, a design choice that guards against the optimistic performance estimates that plague many machine learning studies in medicine. For the deep learning arm, the team assembled an independently annotated CT angiography dataset comprising 236 cases and a remarkable 7,394 individual regions of interest, providing the volume of labeled image data needed to train and test convolutional architectures meaningfully.

On the technical side, the radiomics workflow followed rigorous feature-selection discipline. Candidate radiomic features were screened using analysis of variance combined with Kruskal–Wallis testing to identify features that discriminated between stable and vulnerable plaques, followed by correlation filtering to remove redundant measures, and finally least absolute shrinkage and selection operator, or LASSO, regularization to compress the feature set to its most predictive and non-redundant core. Five classifier families were then trained on the selected features: Random Forest, Support Vector Machine, k-Nearest Neighbor, Naïve Bayes, and Logistic Regression. Performance was quantified using receiver operating characteristic analysis and the area under the curve, complemented by calibration analysis, which asks not merely whether a model ranks patients correctly but whether its predicted probabilities match observed reality—a property that matters enormously when model outputs are meant to inform surgical decisions.

The radiomics results were strikingly consistent. On the internal test set, the Random Forest model achieved an AUC of 0.858, followed closely by the Support Vector Machine at 0.857, Naïve Bayes at 0.855, k-Nearest Neighbor at 0.843, and Logistic Regression at 0.821, with small gaps between training and test performance that suggest the models were capturing genuine biological signal rather than memorizing noise. Crucially, this discrimination survived the move to entirely external data. In the 60-patient external validation cohort, the Support Vector Machine led with an AUC of 0.839, Logistic Regression reached 0.835, k-Nearest Neighbor 0.830, and both Naïve Bayes and Random Forest achieved 0.817. Calibration curves showed close agreement between predicted and observed probabilities across the model families, indicating that the classifiers were not only separating stable from vulnerable plaques but doing so with probabilities a clinician could reasonably act upon.

The deep learning arm told a complementary story. The authors trained a multilayer perceptron, a from-scratch convolutional neural network, and three ImageNet-pretrained transfer-learning backbones—VGG16, VGG19, and ResNet50—on the annotated regions of interest. Transfer learning, in which networks pre-trained on millions of natural images are fine-tuned on medical data, proved advantageous: VGG16 achieved the best test AUC of 0.767, ResNet50 followed at 0.754, and VGG19 reached 0.718, while the multilayer perceptron and the from-scratch CNN trailed at 0.669 and 0.654 respectively. External validation reproduced this ordering almost exactly, with VGG16 at 0.776, ResNet50 at 0.751, VGG19 at 0.719, and the simpler architectures in the high-0.68 range. Although the deep models did not surpass the radiomics classifiers in raw discrimination, their performance held up across centers, and they brought a distinct advantage: interpretability through attention mapping.

Using Gradient-weighted Class Activation Mapping, or Grad-CAM, the team visualized which parts of each image the networks attended to when making their predictions. The resulting heatmaps localized hyperattenuating regions—denser, brighter areas within the plaque that correspond to features such as calcification or hemorrhage—and these localizations were consistent with the pathological findings, most clearly for VGG16 and ResNet50. This alignment between machine attention and histological ground truth is more than a technical curiosity. It provides a sanity check that the networks are not exploiting scanner-specific artifacts or incidental image features, but are genuinely looking at the plaque biology that pathologists confirm under the microscope. For a field often criticized for black-box opacity, such visual evidence of anatomically plausible reasoning is a meaningful step toward clinical trust.

The clinical implications are considerable. Current decision-making for carotid stenosis leans heavily on the degree of luminal narrowing, yet the literature has long shown that plaque composition and stability, not stenosis alone, determine rupture risk. A patient with moderate narrowing but a vulnerable plaque may face a higher stroke risk than a patient with severe narrowing and a stable lesion. A validated, automated tool that reads a routine CT angiography scan—the same scan already obtained during standard preoperative workup—and reports a calibrated probability of plaque vulnerability could therefore reshape preoperative risk stratification, help prioritize patients for carotid endarterectomy or stenting, and guide the intensity of medical therapy and surveillance for those managed conservatively. Because the radiomics models in this study used conventional machine learning classifiers on compact feature sets, they are also lightweight and deployable, requiring no exotic hardware and lending themselves to integration into existing picture archiving and communication systems.

The authors are appropriately measured about what their framework can and cannot yet do. The study is retrospective, and the deep learning models, while externally validated, did not reach the discrimination levels of the best radiomics classifiers, suggesting that hand-crafted quantitative descriptors of plaque texture and morphology still carry information that current convolutional architectures do not fully capture from cropped regions of interest. The natural next steps include prospective validation in consecutive clinical cohorts, testing across scanner vendors and imaging protocols, and exploration of hybrid models that fuse radiomic features with deep-learned representations. Funding for the work came from the Shaoxing City Science and Technology Plan Project 2023, and the study was conducted under the Declaration of Helsinki with institutional ethics approval and informed consent from all participants. Even with those caveats, the convergence of pathology-validated labels, multi-center external testing, strong calibration, and interpretable attention maps makes this one of the more convincing demonstrations that AI-assisted plaque stability assessment is moving from proof of concept toward a genuinely usable instrument in the fight against ischemic stroke.

Subject of Research: AI-based carotid plaque stability assessment on CT angiography for ischemic stroke risk prediction

Article Title: Integrated radiomics and image-based deep learning framework using carotid CT angiography ROI datasets for plaque stability assessment as a predictor of ischemic stroke risk

Article References: Lu, M., Xu, Z., Hanfei, T., Luo, P., Huang, F., Wang, G., Bi, L., Jiang, N., Lang, D., Daqiao, G., & Wang, G. (2026). Integrated radiomics and image-based deep learning framework using carotid CT angiography ROI datasets for plaque stability assessment as a predictor of ischemic stroke risk. BioMedical Engineering OnLine. https://doi.org/10.1186/s12938-026-01621-7

Image Credits: AI Generated

DOI: 10.1186/s12938-026-01621-7

Keywords: carotid CT angiography, radiomics, deep learning, plaque stability, ischemic stroke, stroke risk stratification, carotid endarterectomy, machine learning, VGG16, ResNet50, Grad-CAM, biomedical engineering