BACKGROUND: The clinical significance and outcome predictors of anomalous aortic origin of a coronary artery (AAOCA) in adults remains unclear. Therefore, the aim of this study was to analyze the clinical and prognostic implications of AAOCA in a large cohort of patients undergoing coronary computed tomography angiography (CCTA) in an Italian tertiary referral center. METHODS: Consecutive adults with AAOCA identified through CCTA from September 2004 to September 2024 were included. Data on clinical indications of CCTA, AAOCA subtypes, evidence of inducible myocardial ischemia, and concomitant coronary atherosclerotic disease were collected. Patients were followed up for the end points of all-cause mortality and major adverse cardiac events (nonfatal acute coronary syndromes, revascularization procedures, and heart failure hospitalization). Outcomes were compared with matched controls with normal coronary artery anatomy. RESULTS: Among 17 454 CCTAs performed over a span of 20 years, AAOCA was detected in 173 patients (62±15 years of age, n=58 women [34%]). Chest pain (34%) was the most common indication of CCTA. Obstructive coronary atherosclerotic disease was present in 36 patients (21%), and myocardial ischemia was detected in 60% of those (n=62) who underwent functional imaging testing. AAOCA repair was performed in 9 patients (5%), whereas the majority of patients were treated conservatively. After a median 37-month follow-up (17 to 69 months), mortality ( P =0.321) and major adverse cardiac events ( P =0.392) were similar between patients with AAOCA and controls. Only obstructive coronary atherosclerotic disease was associated with a higher event rate during follow-up, whereas AAOCA subtype and ischemia were not. CONCLUSIONS: In adults with AAOCA, concomitant obstructive coronary atherosclerotic disease is the primary determinant of adverse events. Given the low prevalence of AAOCA and event rates, multicenter registries are needed to refine risk stratification and management of these patients.

Purpose: To differentiate interstitial lung diseases (ILDs) with fibrotic and inflammatory patterns using high-resolution computed tomography (HRCT) and a radiomics-based artificial intelligence (AI) pipeline. Materials and methods: This single-center study included 84 patients: 50 with idiopathic pulmonary fibrosis (IPF)—representative of fibrotic pattern—and 34 with cellular non-specific interstitial pneumonia (NSIP) secondary to connective tissue disease (CTD)—as an example of mostly inflammatory pattern. For a secondary objective, we analyzed 50 additional patients with COVID-19 pneumonia. We performed semi-automatic segmentation of ILD regions using a deep learning model followed by manual review. From each segmented region, 103 radiomic features were extracted. Classification was performed using an XGBoost model with 1000 bootstrap repetitions and SHapley Additive exPlanations (SHAP) were applied to identify the most predictive features. Results: The model accurately distinguished a fibrotic ILD pattern from an inflammatory ILD one, achieving an average test set accuracy of 0.91 and AUROC of 0.98. The classification was driven by radiomic features capturing differences in lung morphology, intensity distribution, and textural heterogeneity between the two disease patterns. In differentiating cellular NSIP from COVID-19, the model achieved an average accuracy of 0.89. Inflammatory ILDs exhibited more uniform imaging patterns compared to the greater variability typically observed in viral pneumonia. Conclusion: Radiomics combined with explainable AI offers promising diagnostic support in distinguishing fibrotic from inflammatory ILD patterns and differentiating inflammatory ILDs from viral pneumonias. This approach could enhance diagnostic precision and provide quantitative support for personalized ILD management.

Photon-counting computed tomography (PCCT) represents a major technological innovation compared to conventional CT, offering improved spatial resolution, reduced electronic noise, and intrinsic spectral capabilities. These advances open new perspectives for synergy with radiomics, a field that extracts quantitative features from medical images. The ability of PCCT to generate multiple types of datasets, including high-resolution conventional images, iodine maps, and virtual monoenergetic reconstructions, increases the richness of extractable features and potentially enhances radiomics performance. This narrative review investigates the current evidence on the interplay between PCCT and radiomics in cardiothoracic imaging. Phantom studies demonstrate reduced reproducibility between PCCT and conventional CT systems, while intra-scanner repeatability remains high. Nonetheless, PCCT introduces additional complexity, as reconstruction parameters and acquisition settings significantly may affect feature stability. In chest imaging, early studies suggest that PCCT-derived features may improve nodule characterization, but existing machine learning models, such as those applied to interstitial lung disease, may require recalibration to accommodate the new imaging paradigm. In cardiac imaging, PCCT has shown particular promise: radiomic features extracted from myocardial and epicardial tissues can provide additional diagnostic insights, while spectral reconstructions improve plaque characterization. Proof-of-concept studies already suggest that PCCT radiomics can capture myocardial aging patterns and discriminate high-risk coronary plaques. In conclusion, evidence supports a growing synergy between PCCT and radiomics, with applications already emerging in both lung and cardiac imaging. By enhancing the reproducibility and richness of quantitative features, PCCT may significantly broaden the clinical potential of radiomics in computed tomography.