Deep learning-based methods for prostate segmentation in magnetic resonance imaging

Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardware availability while still achieving accurate segmentation. We apply these models to a limited set of 85 manual prostate segmentations using the k-fold validation strategy and the Tversky loss function and we compare their results. We find that ENet and UNet are more accurate than ERFNet, with ENet much faster than UNet. Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where graphics processing unit (GPU) is not always available. In conclusion, ENet could be efficiently applied for prostate delineation even in small image training datasets with potential benefit for patient management personalization

Hereditary breast and ovarian cancer in families from southern Italy (Sicily)—Prevalence and geographic distribution of pathogenic variants in BRCA1/2 genes

Recent advances in the detection of germline pathogenic variants (PVs) in BRCA1/2 genes have allowed a deeper understanding of the BRCA-related cancer risk. Several studies showed a significant heterogeneity in the prevalence of PVs across different populations. Because little is known about this in the Sicilian population, our study was aimed at investigating the prevalence and geographic distribution of inherited BRCA1/2 PVs in families from this specific geographical area of Southern Italy. We retrospectively collected and analyzed all clinical information of 1346 hereditary breast and/or ovarian cancer patients genetically tested for germline BRCA1/2 PVs at University Hospital Policlinico “P. Giaccone” of Palermo from January 1999 to October 2019. Thirty PVs were more frequently observed in the Sicilian population but only some of these showed a specific territorial prevalence, unlike other Italian and European regions. This difference could be attributed to the genetic heterogeneity of the Sicilian people and its historical background. Therefore hereditary breast and ovarian cancers could be predominantly due to BRCA1/2 PVs different from those usually detected in other geographical areas of Italy and Europe. Our investigation led us to hypothesize that a higher prevalence of some germline BRCA PVs in Sicily could be a population-specific genetic feature of BRCA-positive carriers

Radiomics and prostate MRI: Current role and future applications

Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer

No evidence of association between prothrombotic gene polymorphisms and the development of acute myocardial infarction at a young age

Background : we investigated the association between 9 polymorphisms of genes encoding hemostasis factors and myocardial infarction in a large sample of young patients chosen because they have less coronary atherosclerosis than older patients, and thus their disease is more likely to be related to a genetic predisposition to a prothrombotic state Methods and Results : this nationwide case-control study involved 1210 patients who had survived a first myocardial infarction at an age of 45 years who underwent coronary arteriography in 125 coronary care units and 1210 healthy subjects matched for age, sex, and geographical origin. None of the 9 polymorphisms of genes encoding proteins involved in coagulation (G-455A -fibrinogen: OR, 1.0; CI, 0.8 to 1.2; G1691A factor V: OR, 1.1; CI, 0.6 to 2.1; G20210A factor II: OR, 1.0; CI, 0.5 to 1.9; and G10976A factor VII: OR, 1.0; CI, 0.8 to 1.3), platelet function (C807T glycoprotein Ia: OR, 1.1; CI, 0.9 to 1.3; and C1565T glycoprotein IIIa: OR, 0.9; CI, 0.8 to 1.2), fibrinolysis (G185T factor XIII: OR, 1.2; CI, 0.9 to 1.6; and 4G/5G plasminogen activator inhibitor type 1: OR, 0.9; CI, 0.7 to 1.2), or homocysteine metabolism (C677T methylenetetrahydrofolate reductase: OR, 0.9; CI, 0.8 to 1.1) were associated with an increased or decreased risk of myocardial infarction Conclusions : this study provides no evidence supporting an association between 9 polymorphisms of genes encoding proteins involved in hemostasis and the occurrence of premature myocardial infarction or protection against it