Mammography and stereotactic biopsy still remain the only techniques for characterising microcalcifications. MRI cannot be considered a diagnostic tool for evaluating microcalcifications. It is, however, useful for identifying DCIS with more aggressive histological grades. An important application of MRI in patients with DCIS associated with suspicious microcalcifications could be to evaluate disease extension after a microhistological diagnosis of malignancy, as it allows a more accurate presurgical planning.

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Aims Gamma-glutamyltransferase (GGT) has been recognized as a cardiovascular risk factor, and its highest molecular weight fraction [big GGT (b-GGT)] is found in vulnerable atherosclerotic plaques. We explored the relationship between b-GGT, computed tomography findings, and long-term outcomes in the general population. Methods and results Between May 2010 and October 2011, subjects aged 45–75 years living in a Tuscan city and without known cardiac disease were screened. The primary endpoint was a composite of cardiovascular death or acute coronary syndrome requiring urgent coronary revascularization. Gamma-glutamyltransferase fractions were available in 898 subjects [median age 65 years (25th–75th percentile 55–70), 46% men]. Median plasma GGT was 20 IU (15–29), and b-GGT was 2.28 (1.28–4.17). Coronary artery calcium (CAC) score values were 0 (0–60), and the volume of pro-atherogenic epicardial fat was 155 mL (114–204). In a model including age, sex, low-density lipoprotein (LDL) cholesterol, current or previous smoking status, hypertension, diabetes, obesity, b-GGT independently predicted epicardial fat volume (EFV) (r = 0.162, P < 0.001), but not CAC (P = 0.198). Over a 10.3-year follow-up (9.6–10.8), 27 subjects (3%) experienced the primary endpoint. We evaluated couples of variables including b-GGT and a cardiovascular risk factor, CAC or EFV. Big GGT yielded independent prognostic significance from age, LDL cholesterol, current or previous smoking status, hypertension, diabetes, obesity, but not CAC or EFV. Conversely, GGT predicted the primary endpoint even independently from CAC and EFV. Conclusion Big GGT seemed at least as predictive as the commonly available GGT assay; therefore, the need for b-GGT rather than GGT measurement should be carefully examined.

Partial detachment of intracardiac prosthesis is a common reality in cardiac surgical practice. Its identification and surgical correction can be very crucial for a patient, as well as for the surgeon. In this paper, we report a case of a 30-year-old man with partial detachment of mechanical mitral valve prosthesis. He recently underwent his seventh heart surgery procedure; five of them were caused by recurrent dehiscence of mitral valve prosthesis.

Background Non-contrast-enhanced cardiac computed tomography (CT) may provide two measures that are emerging as independent predictors of cardiovascular events: coronary calcium score (CCS) and the volume of epicardial fat, a metabolically and immunologically active tissue surrounding the coronary arteries. The quantification of epicardial fat volume (EFV) is not routinely performed in clinical practice for the long time required for image reconstruction and the intra- and inter-observer variability. Purpose We evaluated if artificial intelligence (AI) might prove a valuable tool to interpret the CT data-set, and to better understand the relative prognostic value of CCS and EFV compared to “traditional” cardiovascular risk factors. Methods The Montignoso HEart and Lung Project is a community-based study carried out in a small town of Northern Tuscany (Italy). Starting from 2009, asymptomatic individuals from the general population underwent a baseline screening including a non-contrast cardiac CT, and were followed-up. For the present study, CCS and EFV were automatically measured from CT scans through a deep learning (DL) strategy based on convolutional neural networks. Because of the low incidence of the primary endpoint (myocardial infarction [MI]), the observed cardiac events were predicted with a random forest model built using a subsampling approach. Results Study participants (n=1528; 48% males, age 40 to 77 years) experienced 47 MI events (3%) over 5.5±1.5 years. CCS and EFV independently predicted this endpoint (p values <0.001 and 0.005, respectively) in a model including other predictors, namely weight, age, male gender, and hypertension. The model displayed a good prognostic performance, with an out-of-bag accuracy of 80.43% (accuracy on non-event prediction: 81.17%; performance on event prediction: 57,45%). The CCS emerged as the most important predictor, followed by EFV, weight and age. Interestingly, the incidence of cardiovascular events linked with CCS levels was associated with elevated EFV and the subjects with elevated CCS values but low EFV had no events (figure 1). Conclusions The tools of AI allow to perform an automated analysis of non-contrast-enhanced CT scans, with rapid and accurate measurement of CCS and EFV through a DL approach. In asymptomatic individuals from the general population, these features are more predictive of non-fatal MI than other variables related to the cardiovascular risk, as we can be demonstrated through an application of AI. Figure 1 Funding Acknowledgement Type of funding source: None