Predicting vulnerability status of carotid plaques using CTA-based quantitative analysis.

The study aimed to develop a radiomics model to assess carotid artery plaque vulnerability using CTA images. It retrospectively included 107 patients with carotid artery stenosis who underwent carotid artery stenting (CAS) from 2017 to 2022. Patients were categorized into stable and vulnerable plaque groups based on pathology.

Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography.

Objectives: Aimed to develop a nomogram model based on deep learning features and radiomics features for the prediction of early hematoma expansion.

Methods: A total of 561 cases of spontaneous intracerebral hemorrhage (sICH) with baseline Noncontrast Computed Tomography (NCCT) were included. The metrics of hematoma detection were evaluated by Intersection over Union (IoU), Dice coefficient (Dice), and accuracy (ACC).