In Marfan Syndrome and Related Diseases, STABILISE Technique Should Be Used with Care: Results from a Volumetric Comparative Study of Endovascular Treatment for Aortic Dissection.

Objectives: Aortic dissection in patients with Marfan and related syndromes (HTAD) is a serious pathology whose treatment by thoracic endovascular repair (TEVAR) is still under debate. The aim of this study was to assess the results of the TEVAR for aortic dissection in patients with HTAD as compared to a young population without HTAD. Methods: The study received the proper ethical oversight. We performed an observational exposed (confirmed HTAD) vs. non-exposed (<65 years old) study of TEVAR-treated patients. The preoperative, 1 year, and last available CT scans were analyzed. The thoracic and abdominal aortic diameters, aortic length, and volumes were measured. The entry tears and false lumen (FL) status were assessed. The demographic, clinical, and anatomic data were collected during the follow-up. Results: Between 2011 and 2021, 17 patients were included in the HTAD group and 22 in the non-HTAD group. At 1 year, the whole aortic volume increased by +21.2% in the HTAD group and by +0.2% the non-HTAD groups, p = 0.005. An increase in the whole aortic volume > 10% was observed in ten cases (58.8%) in the HTAD group and in five cases (22.7%) in the non-HTAD group (p = 0.022). FL thrombosis was achieved in nine cases (52.9%) in the HTAD group vs. twenty (90.9%) cases in the non-HTAD group (p < 0.01). The risk factors for unfavorable anatomical evolution were male gender and the STABILISE technique. With a linear model, we observed a significantly different aortic volume evolution between the two groups (p < 0.01) with the STABILISE technique; this statistical difference was not found in the TEVAR subgroup. In the HTAD patients, there was a significant difference in the total aortic volume evolution progression between the patients treated with the STABILISE technique and the patients treated with TEVAR (+160.1 ± 52.3% vs. +47 ± 22.5%, p < 0.01 and +189.5 ± 92.5% vs. +58.6 ± 34.8%, p < 0.01 at 1 year and at the end of follow-up, respectively). Conclusions: TEVAR in the HTAD patients seemed to be associated with poorer anatomical outcomes at 1 year. This result was strongly related to the STABILISE technique which should be considered with care in these specific patients.

Value and prognostic impact of a deep learning segmentation model of COVID-19 lung lesions on low-dose chest CT.

Objectives: 1) To develop a deep learning (DL) pipeline allowing quantification of COVID-19 pulmonary lesions on low-dose computed tomography (LDCT). 2) To assess the prognostic value of DL-driven lesion quantification. Methods: This monocentric retrospective study included training and test datasets taken from 144 and 30 patients, respectively. The reference was the manual segmentation of 3 labels: normal lung, ground-glass opacity(GGO) and consolidation(Cons). Model performance was evaluated with technical metrics, disease volume and extent. Intra- and interobserver agreement were recorded. The prognostic value of DL-driven disease extent was assessed in 1621 distinct patients using C-statistics. The end point was a combined outcome defined as death, hospitalization>10 days, intensive care unit hospitalization or oxygen therapy. Results: The Dice coefficients for lesion (GGO+Cons) segmentations were 0.75±0.08, exceeding the values for human interobserver (0.70±0.08; 0.70±0.10) and intraobserver measures (0.72±0.09). DL-driven lesion quantification had a stronger correlation with the reference than inter- or intraobserver measures. After stepwise selection and adjustment for clinical characteristics, quantification significantly increased the prognostic accuracy of the model (0.82 vs. 0.90; p<0.0001). Conclusions: A DL-driven model can provide reproducible and accurate segmentation of COVID-19 lesions on LDCT. Automatic lesion quantification has independent prognostic value for the identification of high-risk patients.

Prevalence and risk factors for lung involvement on low-dose chest CT (LDCT) in a paucisymptomatic population of 247 patients affected by COVID-19.

BACKGROUND: Low-dose chest CT (LDCT) showed high sensitivity and ability to quantify lung involvement of COVID-19 pneumopathy. The aim of this study was to describe the prevalence and risk factors for lung involvement in 247 patients with a visual score and assess the prevalence of incidental findings. METHODS: For 12 days in March 2020, 250 patients with RT-PCR positive tests and who underwent LDCT were prospectively included. Clinical and imaging findings were recorded. The extent of lung involvement was quantified using a score ranging from 0 to 40. A logistic regression model was used to explore factors associated with a score ≥ 10. RESULTS: A total of 247 patients were analyzed; 138 (54%) showed lung involvement. The mean score was 4.5 ± 6.5, and the mean score for patients with lung involvement was 8.1 ± 6.8 [1-31]. The mean age was 43 ± 15 years, with 121 males (48%) and 17 asymptomatic patients (7%). Multivariate analysis showed that age > 54 years (odds ratio 4.4[2.0-9.6] p < 0.001) and diabetes (4.7[1.0-22.1] p = 0.049) were risk factors for a score ≥ 10. Multivariate analysis including symptoms showed that only age > 54 years (4.1[1.7-10.0] p = 0.002) was a risk factor for a score ≥ 10. Rhinitis (0.3[0.1-0.7] p = 0.005) and anosmia (0.3[0.1-0.9] p = 0.043) were protective against lung involvement. Incidental imaging findings were found in 19% of patients, with a need for follow-up in 0.6%. CONCLUSION: The prevalence of lung involvement was 54% in a predominantly paucisymptomatic population. Age ≥ 55 years and diabetes were risk factors for significant parenchymal lung involvement. Rhinitis and anosmia were protective against LDCT abnormalities.

Low-dose chest CT for diagnosing and assessing the extent of lung involvement of SARS-CoV-2 pneumonia using a semi quantitative score.

OBJECTIVES: The purpose is to assess the ability of low-dose CT (LDCT) to determine lung involvement in SARS-CoV-2 pneumonia and to describe a COVID19-LDCT severity score. MATERIALS AND METHODS: Patients with SARS-CoV-2 infection confirmed by RT-PCR were retrospectively analysed. Clinical data, the National Early Warning Score (NEWS) and imaging features were recorded. Lung features included ground-glass opacities (GGO), areas of consolidation and crazy paving patterns. The COVID19-LDCT score was calculated by summing the score of each segment from 0 (no involvement) to 10 (severe impairment). Univariate analysis was performed to explore predictive factor of high COVID19-LDCT score. The nonparametric Mann-Whitney test was used to compare groups and a Spearman correlation used with p<0.05 for significance. RESULTS: Eighty patients with positive RT-PCR were analysed. The mean age was 55 years ± 16, with 42 males (53%). The most frequent symptoms were fever (60/80, 75%) and cough (59/80, 74%), the mean NEWS was 1.7±2.3. All LDCT could be analysed and 23/80 (28%) were normal. The major imaging finding was GGOs in 56 cases (67%). The COVID19-LDCT score (mean value = 19±29) was correlated with NEWS (r = 0.48, p<0.0001). No symptoms were risk factor to have pulmonary involvement. Univariate analysis shown that dyspnea, high respiratory rate, hypertension and diabetes are associated to a COVID19-LDCT score superior to 50. CONCLUSIONS: COVID19-LDCT score did correlate with NEWS. It was significantly different in the clinical low-risk and high-risk groups. Further work is needed to validate the COVID19-LDCT score against patient prognosis.