ObjectiveTo describe CT and clinical findings of pulmonary artery intimal sarcoma (PAIS) compared with those of pulmonary thromboembolism (PTE), to investigate MRI and positron emission tomography (PET)-CT findings of PAIS, and to evaluate the effect of delayed diagnosis of PAIS on survival outcomes.Materials and MethodsTwenty-six patients with PAIS were retrospectively identified and matched for sex, with patients with PTE at a ratio of 1:2. CT and clinical findings of the two groups were compared using Student's t test or chi-square test. The effect of delayed diagnosis on survival was investigated using Kaplan-Meier analysis.ResultsThe most common tumor pattern in PAIS was tumoral impaction. Heterogeneous attenuation, wall eclipse signs, intratumoral vessels, acute interphase angles, single location, presence of lung ischemia, and central location were significantly more common in PAIS than in PTE (all p < 0.01). Levels of D-dimers and brain natriuretic peptide were lower in PAIS than in PTE (p < 0.05). In three patients of PAIS, long inversion time sequence MRI showed intermingled dark signal intensity foci suggestive of intermingled thrombi. All nine patients who had undergone PET-CT displayed hypermetabolism. Diagnosis was delayed in 42.3% of the PAIS patients and those patients had a significantly shorter overall survival than patients whose diagnosis was not delayed (p < 0.05).ConclusionThe characteristic CT and clinical findings of PAIS may help achieve early diagnosis of PAIS and make better survival outcomes of patients. MRI and PET-CT can be used as second-line imaging modalities and could help distinguish PAIS from PTE and to plan clinical management.

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Objective: This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods:We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1-10, 11-100, 101-400, > 400) was evaluated. Results: In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and falsepositive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion:The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.