Pre-Menopausal Breast Fat Density Might Predict MACE During 10 Years of Follow-Up: The BRECARD Study.

OBJECTIVES: This study sought to determine whether the breast gland adipose tissue is associated with different rates of major adverse cardiac events (MACEs) in pre-menopausal women. BACKGROUND: To our knowledge, no study investigated the impact of breast adipose tissue infiltration on MACEs in pre-menopausal women. METHODS: Prospective multicenter cohort study conducted on pre-menopausal women >40 years of age without cardiovascular disease and breast cancer at enrollment. The study started in January 2000 and ended in January 2009, and the end of the follow-up for the evaluation of MACEs was in January 2019. Participants underwent mammography to evaluate breast density and were divided into 4 groups according to their breast density. The primary endpoint was the probability of a MACE at 10 years of follow-up in patients staged for different breast deposition/adipose tissue deposition. RESULTS: The propensity score matching divided the baseline population of 16,763 pre-menopausal women, leaving 3,272 women according to the category of breast density from A to D. These women were assigned to 4 groups of the study according to baseline breast density. At 10 years of follow-up, we had 160 MACEs in group 1, 62 MACEs in group 2, 27 MACEs in group 3, and 16 MACEs in group 4. MACEs were predicted by the initial diagnosis of lowest breast density (hazard ratio: 3.483; 95% confidence interval: 1.476 to 8.257). Further randomized clinical trials are needed to translate the results of the present study into clinical practice. The loss of ex vivo breast density models to study the cellular/molecular pathways implied in MACE is another study limitation. CONCLUSIONS: Among pre-menopausal women, a higher evidence of adipose tissue at the level of breast gland (lowest breast density, category A) versus higher breast density shows higher rates of MACEs. Therefore, the screening mammography could be proposed in overweight women to stage breast density and to predict MACEs. (Breast Density in Pre-menopausal Women Is Predictive of Cardiovascular Outcomes at 10 Years of Follow-Up [BRECARD]; NCT03779217).

Chest CT Computerized Aided Quantification of PNEUMONIA Lesions in COVID-19 Infection: A Comparison among Three Commercial Software.

PURPOSE: To compare different commercial software in the quantification of Pneumonia Lesions in COVID-19 infection and to stratify the patients based on the disease severity using on chest computed tomography (CT) images. MATERIALS AND METHODS: We retrospectively examined 162 patients with confirmed COVID-19 infection by reverse transcriptase-polymerase chain reaction (RT-PCR) test. All cases were evaluated separately by radiologists (visually) and by using three computer software programs: (1) Thoracic VCAR software, GE Healthcare, United States; (2) Myrian, Intrasense, France; (3) InferRead, InferVision Europe, Wiesbaden, Germany. The degree of lesions was visually scored by the radiologist using a score on 5 levels (none, mild, moderate, severe, and critic). The parameters obtained using the computer tools included healthy residual lung parenchyma, ground-glass opacity area, and consolidation volume. Intraclass coefficient (ICC), Spearman correlation analysis, and non-parametric tests were performed. RESULTS: Thoracic VCAR software was not able to perform volumes segmentation in 26/162 (16.0%) cases, Myrian software in 12/162 (7.4%) patients while InferRead software in 61/162 (37.7%) patients. A great variability (ICC ranged for 0.17 to 0.51) was detected among the quantitative measurements of the residual healthy lung parenchyma volume, GGO, and consolidations volumes calculated by different computer tools. The overall radiological severity score was moderately correlated with the residual healthy lung parenchyma volume obtained by ThoracicVCAR or Myrian software, with the GGO area obtained by the ThoracicVCAR tool and with consolidation volume obtained by Myrian software. Quantified volumes by InferRead software had a low correlation with the overall radiological severity score. CONCLUSIONS: Computer-aided pneumonia quantification could be an easy and feasible way to stratify COVID-19 cases according to severity; however, a great variability among quantitative measurements provided by computer tools should be considered.