Osteophytes' position in subjects with DISH and right-sided aorta: verification of the 'aortic pulsation protective effect' theory.

OBJECTIVES: To validate in a large cohort with right-sided aorta the theory that thoracic right-sided flowing osteophytes in DISH results from a 'protective' effect of the pulsating descending left-sided thoracic aorta. METHODS: Chest CTs of patients with DISH and right-sided aorta and controls with DISH and left-sided aorta were evaluated and compared on each intervertebral space (IS) for the location of the aorta (right, left, centre) and the location of the osteophyte relative to the aorta (contralateral, ipsilateral, bilateral). RESULTS: The study and control cohorts included 31 and 35 subjects, respectively (male 22/9 and female 27/8; median age 64.8/65.3 years; P = 0.86). Osteophytes contralateral to the aorta's location were recorded in the majority of ISs in both the study and control groups (47% and 60%, respectively; P > 0.05), while ipsilateral osteophytes were recorded in 6.9% and 7.7%, respectively (P = 0.002). Bilateral osteophytes located to the right and the left of the aorta were significantly more prevalent in the study group compared with the controls (17.2% and 5.4%, respectively; P = 0.04). CONCLUSIONS: Aortic pulsation plays an important role in inhibiting the development of osteophytes and results in the majority of contralateral osteophytes on both right-sided and left-sided aortas. However, since both ipsilateral and bilateral osteophytes were not at all rare in both groups, other parameters, which are yet to be established, probably contribute to the location of osteophytes.

Evaluation of the Pulmonary Arteries on CTPA With Dual Energy CT: Objective Analysis and Subjective Preferences in a Multireader Study.

PURPOSE: To perform qualitative and quantitative evaluation of low-monoenergetic images (50 KeV) compared with conventional images (120 kVp) in pulmonary embolism (PE) studies and to determine the extent and clinical relevance of these differences as well as radiologists' preferences. MATERIALS AND METHODS: One hundred fifty CT examinations for PE detection conducted on a single-source dual-energy CT were retrospectively evaluated. Attenuation, contrast-to-noise-ratio, and signal-to-noise-ratio were obtained in a total of 8 individual pulmonary arteries on each exam-including both central (450/1200=37.5%) and peripheral (750/1200=62.5%) locations. Results were compared between the conventional and low-monoenergetic images. For quality assessment, 41 images containing PE were presented side-by-side as pairs of slices in both conventional and monoenergetic modes and evaluated for ease in embolus detection by 9 radiologists: cardiothoracic specialists (3), noncardiothoracic specialists (3), and residents (3). Paired samples t tests, a-parametric Wilcoxon test, McNemar test, and kappa statistics were performed. RESULTS: Monoenergetic images had an overall statistically significant increased average ratio of 2.09 to 2.26 ( P <0.05) for each measured vessel attenuation, with an increase in signal-to-noise ratio (23.82±9.29 vs. 11.39±3.2) and contrast-to-noise ratio (17.17±6.7 vs 7.27±2.52) ( P <0.05). Moreover, 10/150 (6%) of central pulmonary artery measurements considered suboptimal on conventional mode were considered diagnostic on the monoenergetic images (181±14.6 vs. 387.7±72.4 HU respectively, P <0.05). In the subjective evaluation, noncardiothoracic radiologists showed a preference towards low-monoenergetic images, whereas cardiothoracic radiologists did not (74.4% vs. 57.7%, respectively, P <0.05). CONCLUSIONS: The SNR and CNR increase on monoenergetic images may have clinical significance particularly in the setting of sub-optimal PE studies. Noncardiothoracic radiologists and residents prefer low monoenergetic images.

Rapid Classification of Sarcomas Using Methylation Fingerprint: A Pilot Study.

Sarcoma classification is challenging and can lead to treatment delays. Previous studies used DNA aberrations and machine-learning classifiers based on methylation profiles for diagnosis. We aimed to classify sarcomas by analyzing methylation signatures obtained from low-coverage whole-genome sequencing, which also identifies copy-number alterations. DNA was extracted from 23 suspected sarcoma samples and sequenced on an Oxford Nanopore sequencer. The methylation-based classifier, applied in the nanoDx pipeline, was customized using a reference set based on processed Illumina-based methylation data. Classification analysis utilized the Random Forest algorithm and t-distributed stochastic neighbor embedding, while copy-number alterations were detected using a designated R package. Out of the 23 samples encompassing a restricted range of sarcoma types, 20 were successfully sequenced, but two did not contain tumor tissue, according to the pathologist. Among the 18 tumor samples, 14 were classified as reported in the pathology results. Four classifications were discordant with the pathological report, with one compatible and three showing discrepancies. Improving tissue handling, DNA extraction methods, and detecting point mutations and translocations could enhance accuracy. We envision that rapid, accurate, point-of-care sarcoma classification using nanopore sequencing could be achieved through additional validation in a diverse tumor cohort and the integration of methylation-based classification and other DNA aberrations.