Should the host reaction to anisakiasis influence the treatment? Different clinical presentations in two cases

Gastrointestinal anisakiasis is a parasitic infection occurring in people that consume raw or inadequately cooked fish or squid. It is frequently characterized by severe epigastric pain, nausea and vom iting caused by the penetration of the larvae into the gastric wall. Acute gastric anisakiasis with severe chest discomfort is rarely report ed in Italy. On the other hand, gastro-allergic anisakiasis with rash, urticaria and isolated angioedema or anaphylaxis is a clinical entity that has been described only recently. Also, if patients usually develop symptoms within 12 hours after raw seafood ingestion, not always endoscopic exploration can promptly identify the Anisakis larvae. Moreover, some authors consider the prevailing allergic reaction as a natural and effective defense against the parasitic attack. We report two cases of peculiar manifestations of anisakiasis in both acute and chronic forms (severe chest discomfort and anaphylactoid reaction)

Automated Scar Segmentation from CMR-LGE Images Using a Deep Learning Approach

Aim. The presence of myocardial scar is a strong predictor of ventricular remodeling, cardiac dysfunction and mortality. Our aim was to assess quantitatively the presence of scar tissue from cardiac-magnetic-resonance (CMR) with late-Gadolinium-enhancement (LGE) images using a deep-learning (DL) approach. Methods. Scar segmentation was performed automatically with a DL approach based on ENet, a deep fully-convolutional neural network (FCNN). We investigated three different ENet configurations. The first configuration (C1) exploited ENet to retrieve directly scar segmentation from the CMR-LGE images. The second (C2) and third (C3) configurations performed scar segmentation in the myocardial region, which was previously obtained in a manual or automatic way with a state-of-the-art DL method, respectively. Results. When tested on 250 CMR-LGE images from 30 patients, the best-performing configuration (C2) achieved 97% median accuracy (inter-quartile (IQR) range = 4%) and 71% median Dice similarity coefficient (IQR = 32%). Conclusions. DL approaches using ENet are promising in automatically segmenting scars in CMR-LGE images, achieving higher performance when limiting the search area to the manually-defined myocardial region

L’utilizzo delle protesi endoscopiche nella patologia dell’apparato digerente

L’utilizzo di protesi ad introduzione per via endoscopica per patologie dell’apparato digerente sia benigne che maligne ha avuto negli ultimi anni un considerevole sviluppo. Il posizionamento delle endoprotesi è ben tollerato dai pazienti, non necessita di anestesia e comporta rischi relativamente minimi. Le nuove protesi metalliche autoespansibili permettono di risolvere stenosi anche molto serrate senza quasi mai necessità di dilatazione, con riduzione dei rischi che da questa derivano. Viene riportata una revisione dell’esperienza di protesizzazione per patologie dell’apparato digerente e vengono discussi le indicazioni, i limiti e le complicanze, sulla scorta dei dati dalla letteratura internazionale

A Novel Approach Based on Spatio-temporal Features and Random Forest for Scar Detection Using Cine Cardiac Magnetic Resonance Images

Aim. To identify the presence of scar tissue in the left ventricle from Gadolinium (Gd)-free magnetic resonance cine sequences using a learning-based approach relying on spatio-temporal features. Methods. The spatial and temporal features were extracted using local binary patterns from (i) cine end-diastolic frame and (ii) two parametric images of amplitude and phase wall motion, respectively, and classified with Random Forest. Results. When tested on 328 cine sequences from 40 patients, a recall of 70% was achieved, improving significantly the classification resulting from spatial and temporal features processed separately. Conclusions. The proposed approach showed promising results, paving the way for scar identification from Gd-free images

A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images

Background and objective: In patients with suspected Coronary Artery Disease (CAD), the severity of stenosis needs to be assessed for precise clinical management. An automatic deep learning-based algorithm to classify coronary stenosis lesions according to the Coronary Artery Disease Reporting and Data System (CAD-RADS) in multiplanar reconstruction images acquired with Coronary Computed Tomography Angiography (CCTA) is proposed. Methods: In this retrospective study, 288 patients with suspected CAD who underwent CCTA scans were included. To model long-range semantic information, which is needed to identify and classify stenosis with challenging appearance, we adopted a token-mixer architecture (ConvMixer), which can learn structural relationship over the whole coronary artery. ConvMixer consists of a patch embedding layer followed by repeated convolutional blocks to enable the algorithm to learn long-range dependences between pixels. To visually assess ConvMixer performance, Gradient-Weighted Class Activation Mapping (Grad-CAM) analysis was used. Results: Experimental results using 5-fold cross-validation showed that our ConvMixer can classify significant coronary artery stenosis (i.e., stenosis with luminal narrowing ≥50%) with accuracy and sensitivity of 87% and 90%, respectively. For CAD-RADS 0 vs. 1–2 vs. 3–4 vs. 5 classification, ConvMixer achieved accuracy and sensitivity of 72% and 75%, respectively. Additional experiments showed that ConvMixer achieved a better trade-off between performance and complexity compared to pyramid-shaped convolutional neural networks. Conclusions: Our algorithm might provide clinicians with decision support, potentially reducing the interobserver variability for coronary artery stenosis evaluation

Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images

Objective: The aim of this paper is to investigate the use of fully convolutional neural networks (FCNNs) to segment scar tissue in the left ventricle from cardiac magnetic resonance with late gadolinium enhancement (CMR-LGE) images. Methods: A successful FCNN in the literature (the ENet) was modified and trained to provide scar-tissue segmentation. Two segmentation protocols (Protocol 1 and Protocol 2) were investigated, the latter limiting the scar-segmentation search area to the left ventricular myocardial tissue region. CMR-LGE from 30 patients with ischemic-heart disease were retrospectively analyzed, for a total of 250 images, presenting high variability in terms of scar dimension and location. Segmentation results were assessed against manual scar-tissue tracing using one-patient-out cross validation. Results: Protocol 2 outperformed Protocol 1 significantly (p value < 0.05), with median sensitivity and Dice similarity coefficient equal to 88.07% [inter-quartile range (IQR) 18.84%] and 71.25% (IQR 31.82%), respectively. Discussion: Both segmentation protocols were able to detect scar tissues in the CMR-LGE images but higher performance was achieved when limiting the search area to the myocardial region. The findings of this paper represent an encouraging starting point for the use of FCNNs for the segmentation of nonviable scar tissue from CMR-LGE images

Rationale and design of the dual-energy computed tomography for ischemia determination compared to “gold standard” non-invasive and invasive techniques (DECIDE-Gold) : a multicenter international efficacy diagnostic study of rest-stress dual-energy computed tomography angiography with perfusion

Background: Dual-energy CT (DECT) has potential to improve myocardial perfusion for physiologic assessment of coronary artery disease (CAD). Diagnostic performance of rest-stress DECT perfusion (DECTP) is unknown. Objective: DECIDE-Gold is a prospective multicenter study to evaluate the accuracy of DECT to detect hemodynamic (HD) significant CAD, as compared to fractional flow reserve (FFR) as a reference standard. Methods: Eligible participants are subjects with symptoms of CAD referred for invasive coronary angiography (ICA). Participants will undergo DECTP, which will be performed by pharmacological stress, and participants will subsequently proceed to ICA and FFR. HD-significant CAD will be defined as FFR\ua0 64\ua00.80. In those undergoing myocardial stress imaging (MPI) by positron emission tomography (PET), single photon emission computed tomography (SPECT) or cardiac magnetic resonance (CMR) imaging, ischemia will be graded by % ischemic myocardium. Blinded core laboratory interpretation will be performed for CCTA, DECTP, MPI, ICA, and FFR. Results: Primary endpoint is accuracy of DECTP to detect 651 HD-significant stenosis at the subject level when compared to FFR. Secondary and tertiary endpoints are accuracies of combinations of DECTP at the subject and vessel levels compared to FFR and MPI. Conclusion: DECIDE-Gold will determine the performance of DECTP for diagnosing ischemia