Tomography

Purpose: To investigate whether radiomic characteristics of visually unremarkable basal ganglia on preprocedural non-contrast computed tomography (CT) are associated with subsequent post-thrombectomy hyperdense transformation (HT) in patients with acute ischemic stroke. Materials and Methods: This retrospective single-center study included a derivation cohort of 84 patients with preserved Alberta Stroke Programme Early CT Scores (ASPECTS ≥ 8), no visually appreciable basal ganglia abnormality, anterior circulation large-vessel occlusion, and complete reperfusion after mechanical thrombectomy between January 2022 and July 2023. An independent temporal validation cohort comprised 31 consecutive eligible patients treated between August and November 2023. The ipsilateral basal ganglia were manually segmented on preprocedural non-contrast CT, and radiomic features were extracted using LIFEx. Candidate features underwent interobserver reproducibility assessment and correlation filtering before least absolute shrinkage and selection operator logistic regression with 10-fold cross-validation. The final model was applied without modification to the temporal validation cohort. Nested cross-validation and learning-curve analysis were additionally performed to assess model-development optimism and stability. Results: The final model retained two texture features, Gray-Level Run Length Matrix Short Runs Emphasis and Gray-Level Size Zone Matrix Grey Level Variance. In the derivation cohort, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.760 (95% CI, 0.652–0.855), with 48.8% sensitivity, 95.3% specificity, and 72.6% accuracy. When applied without modification to the independent temporal validation cohort, the AUC was 0.677 (95% CI, 0.475–0.858), with 33.3% sensitivity, 93.8% specificity, and 64.5% accuracy. Nested cross-validation yielded a mean AUC of 0.673 ± 0.037, with variability in feature selection across resampling iterations. Learning-curve analysis showed an early plateau, with little improvement in cross-validated discrimination as the training sample increased within the evaluated range. Conclusions: Radiomic analysis of visually unremarkable basal ganglia on preprocedural non-contrast CT identified an exploratory two-feature imaging signature associated with subsequent post-thrombectomy HT. The model retained modest discriminatory ability in temporal validation, although internal resampling demonstrated model-development optimism and limited feature-selection stability. These findings should be considered hypothesis-generating and warrant further evaluation using larger datasets, alternative modeling approaches, and independent multicenter validation.

Background/Objectives: Coronary artery calcification is a leading cause of stent failure and a strong predictor of adverse outcomes after percutaneous coronary intervention (PCI). Accurate characterization of calcium burden, depth, and morphology is central to procedural planning, targeted treatment, and better outcomes. This review summarizes the contemporary armamentarium for imaging calcified coronary lesions, from coronary angiography and computed tomography to advanced intracoronary imaging, and how it facilitates treatment. Methods: We synthesized evidence from landmark studies, validated imaging scoring systems, registries, and current guideline and consensus documents addressing the assessment and imaging-guided treatment of coronary calcification. Results: Invasive angiography shows poor diagnostic accuracy for coronary calcium detection, characterization, and quantification. Coronary CT angiography noninvasively quantifies calcium (Agatston score and three-dimensional volume) and supports preprocedural planning, although it may overestimate calcium volume. Intravascular imaging by means of intravascular ultrasound (IVUS) and optical coherence tomography (OCT) effectively detects coronary calcium. Their use is associated with improved clinical outcomes compared with sole angiographic guidance. The addition of near-infrared spectroscopy (NIRS-IVUS) characterizes lipids within calcified plaque and high-risk calcified nodules. OCT provides the highest spatial resolution and uniquely measures calcium thickness and longitudinal calcium length, while the utilization of artificial intelligence further enhances calcium assessment. Integration of these modalities into a morphology-based, imaging-guided approach facilitates calcium detection and modification and supports PCI optimization. Conclusions: Intravascular imaging, along with coronary CT angiography, has transformed the assessment of calcified coronary lesions. Combining their strengths enables precise, lesion-specific preparation; randomized trials show that intravascular imaging guidance improves outcomes in complex PCI, while CT-guided PCI planning is emerging as a complementary strategy whose clinical value is now being tested in randomized trials.

Objectives: The aim of this study is to provide early diagnosis of osteoporosis, osteopenia, and normal bone mineral density (BMD) on CT scans utilizing machine learning and radiomics techniques. Methods: In this retrospective study, 874 patients who met the predefined eligibility criteria were included. BMD was categorized as normal, osteopenia, or osteoporosis according to DEXA-derived T-scores. Lumbar vertebral bodies were automatically segmented on abdominal and lumbar CT images, and radiomic features were extracted from the segmented regions. Logistic regression (LR), decision table (DT), random forest (RF), multilayer perceptron (MLP), and bagging models were developed to distinguish among the three BMD categories. Model performance was evaluated using accuracy, F-score, area under the receiver operating characteristic curve (ROC area), and area under the precision–recall curve (PRC area). Results: Notably, the best results were observed in the L3 vertebra among the 874 patients included in the study. Accordingly, the MLP and LR methods showed the greatest accuracy value of 83.5%, F-score value of 81.2%, ROC area value of 0.912, and PRC area value of 0.892 in cases with normal BMD. At the L2 vertebral level, the LR approach produced the greatest results for patients with osteoporosis (Acc: 72.5%, F-score: 72.5%, ROC area: 0.897, and PRC area: 0.797). Conclusions: In the three-class BMD assessment, the models showed the highest performance for normal BMD and the lowest performance for osteopenia. Among the individual lumbar levels, L3 demonstrated the highest radiomics-based classification performance, whereas L1 showed comparatively lower performance. These level-specific findings should be regarded as exploratory and should not be interpreted as supporting clinical BMD classification from a single vertebra. CT radiomics may have potential as an opportunistic screening tool to identify patients who may benefit from formal DEXA or further osteoporosis evaluation; however, patient-level multivertebral validation is required before clinical implementation.