Purpose The capability of lung ultrasound (LUS) to distinguish the different pulmonary patterns of COVID-19 and quantify the disease burden compared to chest CT is still unclear. Methods PCR-confirmed COVID-19 patients who underwent both LUS and chest CT at the Emergency Department were retrospectively analysed. In both modalities, twelve peripheral lung zones were identified and given a Severity Score basing on main lesion pattern. On CT scans the well-aerated lung volume (%WALV) was visually estimated. Per-patient and per-zone assessments of LUS classification performance taking CT findings as reference were performed, further revisioning the images in case of discordant results. Correlations between number of disease-positive lung zones, Severity Score and %WALV on both LUS and CT were assessed. The area under receiver operating characteristic curve (AUC) was calculated to determine LUS performance in detecting %WALV ≤ 70%. Results The study included 219 COVID-19 patients with abnormal chest CT. LUS correctly identified as positive 217 (99%) patients, but per-zone analysis showed sensitivity = 75% and specificity = 66%. The revision of the 121 (55%) cases with positive LUS and negative CT revealed COVID-compatible lesions in 42 (38%) CT scans. Number of disease-positive zones, Severity Score and %WALV between LUS and CT showed moderate correlations. The AUCs for LUS Severity Score and number of LUS-positive zones did not differ in detecting %WALV ≤ 70%. Conclusion LUS in COVID-19 is valuable for case identification but shows only moderate correlation with CT findings as for lesion patterns and severity quantification. The number of disease-positive lung zones in LUS alone was sufficient to discriminate relevant disease burden.

Background The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model. Methods LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model. Results Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81. Conclusions Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts. Key points We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.

Objectives: In this study, we aimed at investigating the relationship between diverticula and in vivo colonic features such as total colon length (TCL), using CTC. We also evaluated polyps, neoplastic lesions and the correlation among them. Methods: This retrospective study considered a series of patients who underwent CTC in our Hospital from 2010 to 2018. We evaluated TCL, the length of each colon segments and sigmoid colon diameter using dedicated software. We verified the presence of diverticula, polyps and neoplasm and measured the number of diverticula using a five-point class scale, evaluating the colonic segments involved by the disease and the number of diverticula for each segment. A logistic regression model was used to analyse the relationship between diverticula and the patients’ age, sigmoid colonic diameter and the length of each colonic segments. Results: The population finally included 467 patients, 177 males and 290 females (average age of 67 ± 12; range 45–96). The mean TCL was 169 ± 25 cm (range 115–241 cm). Out of the 467, 323 patients (69%) had at least one analyse. The patients with diverticula had a mean TCL significantly shorter than patients without diverticula (164 ± 22 vs 181 ± 27 cm; p = 0.001). Among the different variables, sigmoid colon length, sigmoid colon diameter and patient’s age were correlated with diverticula (p < 0.01). Otherwise there is no association among diverticula, polyps and neoplasm. Conclusions: The presence of colonic diverticula was significantly inversely correlated with TCL.The TCL was not significantly correlated with polyps and cancers. Advances in knowledge: The presence of colonic diverticula was significantly inversely correlated with total colon length, and in particular they significantly decreased with increasing colon length; our observation could contribute to the comprehension of diverticula pathogenesis.

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