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Gastrointestinal stromal tumors (GISTs) arise from the interstitial cells of Cajal in the gastrointestinal tract and are the most common intestinal tumors. Usually GISTs are asymptomatic, especially small tumors that may not cause any symptoms and may be found accidentally on abdominal CT scans. Discovering of inhibitor of receptor tyrosine kinases has changed the outcome of patients with high-risk GISTs. This paper will focus on the role of imaging in diagnosis, characterization and follow-up. We shall also report our local experience in radiomics evaluation of GISTs.

Although preoperative biopsy of rectal cancer (RC) is an essential step for confirmation of diagnosis, it currently fails to provide prognostic information to the clinician beyond a rough estimation of tumour grade. In this study we used a risk classification to stratified patient in low-risk and high-risk patients in relation to the disease free survival and the overall survival using histopathological post-operative features. The purpose of this study was to evaluate if low-risk and high-risk RC can be distinguished using a CT-based radiomics model. We retrospectively reviewed the preoperative abdominal contrast-enhanced CT of 40 patients with RC. CT portal-venous phase was used for manual RC segmentation by two radiologists. Radiomics parameters were extracted by using PyRadiomics (3.0) software, which automatically obtained a total of 120 radiomics features. An operator-independent statistical hybrid method was adopted for the selection and reduction of features, while discriminant analysis was used to construct the predictive model. Postoperative histopathological report was used as reference standard. Receiver operating characteristics (ROC) and areas under the ROC curve (AUROC) were calculated to evaluate the diagnostic performance of the most dis-criminating selected parameters. Sensitivity, specificity, and accuracy were calculated. In our study cohort, the original_shape_Maximum3DDiameter and origi-nal_shape_MajorAxisLength demonstrated a good performance in the differentiation between preoperative degree of RC, with an AUROC of 0.680%, sensitivity of 74.02%, specificity of 73.45%, positive predictive value of 81.47%, and accuracy of 73.71%. In conclusion, this preliminary analysis showed statistically significant differences in radiomics features between low-risk and high-risk RC.

The objective of our work was to assess the accuracy in terms of spatial and contrast resolution, respiratory movement artifacts and synchronization of the “triple arterial phase” (TAP) sequence and compare it with the conventionally acquired arterial phase (AP). Between June 2021 and September 2021 16 patients underwent liver MRI for characterization and/or follow-up of focal hepatic lesions, and the TAP sequence was used. With the aim to obtain a TAP sequence we applied the KEYHOLE technique (modified to 35%) and the CENTRA-Keyhole method (Contrast Enhanced Timing Robust Angiography) for the K-space sampling, to the basic e-THRIVE sequence. 6/16 patients had previous MRI examinations performed with the classic single AP. Five radiologists experienced in the interpretation of liver imaging independently examined MRI studies. They were asked to provide their opinion, by using a qualitative evaluation scale, on spatial resolution, contrast/noise ratio (CNR), presence of breath-hold artifacts, synchronization of the TAP and its quality and usefulness for the characterization of liver lesions. The same questions were used for the evaluation of the 6/16 previous MRI examinations acquired with the conventional AP. For all readers, with TAP, the synchronization of the arterial study, the spatial resolution, the evaluation of artifacts from incorrect breath-hold were deemed good or excellent. The contrast resolution in the TAP was excellent for almost all cases. TAP was overall judged superior to AP. These preliminary results obtained are promising and require confirmation on a larger sample study to assess the diagnostic benefit of the TAP.

Dilated Cardiomyopathy is a high-incident disease, which diagnosis of and treatments are clinical priority. The aim of our study was to evaluate the diagnostic potential of cardiac magnetic resonance (CMR) imaging; echocardiography and the biochemical parameters that can help us differentiate between the post-ischemic and non-ischemic dilated cardiomyopathy. Materials and methods. The study enrolled 134 patients with dilated cardiomyopathy: 74 with the post-ischemic form and 60 with the non-ischemic one. All patients underwent a coronary imaging test, with echocardiogram, cardiac magnetic resonance and a blood test. Pro-inflammatory cytokines were evaluated using Luminex kit. Data was compared between the two groups. Results. Echocardiography allowed recognition of Left Ventricular Non Compaction in 2 patients. Longitudinal and circumferential strains were significantly different in the two groups (p

The aim of our study was to evaluate the atherosclerotic pattern of patients with coronary myocardial bridging (MB) by means of CT Coronary Angiography (CT-CA). 254 consecutive patients (166 male, mean age 58.6 +/- A 10.3) who underwent 64-slice CT-CA according to current clinical indications were reviewed for the presence of MB and concomitant segmental atherosclerotic pattern. Coronary plaques were assessed in all patients enrolled. 73 patients (29%) presented single (90%) or multiple (10%) MB, frequently (93%) localized in the mid-distal left anterior descending artery. The MB segment was always free of atherosclerosis. Segments proximal to the MB presented: no atherosclerotic disease (n = 37), positive remodeling (n = 23), < 50% (n = 14), or > 50% stenoses (n = 7). Distal segments presented a different atherosclerosis pattern (P < 0.0001): absence of disease (n = 73), no significant lesions (n = 8). No significant differences were found between segments proximal to MB and proximal coronary segments apart from left main trunk. Pattern of atherosclerotic lesions located in segments 6 and 7 significantly differs between patients with MB and patients without MB (P < 0.05). CT-CA is a reliable method to non-invasively demonstrate MB and related atherosclerotic pattern. CT-CA provides new insight regarding atherosclerosis distribution in segments close to MB.

Gastrointestinal lymphomas represent 5–20% of extranodal lymphomas and mainly occur in the stomach and small intestine. Clinical findings are not specific, thus often determining a delay in the diagnosis. Imaging features at conventional and cross-sectional imaging must be known by the radiologist since he/she plays a pivotal role in the diagnosis and disease assessment, thus assisting in the choice of the optimal treatment to patients. This review focuses on the wide variety of imaging presentation of esophageal, gastric, and small and large bowel lymphoma presenting their main imaging appearances at conventional and cross-sectional imaging, mainly focusing on computed tomography and magnetic resonance, helping in the choice of the best imaging technique for the disease characterization and assessment and the recognition of potential complications.

The current gold standard for the diagnosis of myocardial bridging is conventional coronary angiography; however, it shows only indirect signs of the disease, due to the systolic compression of the artery caused by the myocardial bridge with narrowing of the lumen and diastolic relaxation. On the other hand, computed tomography coronary angiography, even though exposing to radiation, clearly demonstrates the intramural course, the overlying muscular bands and the surrounding tissues also in asymptomatic patients and in absence of systolic compression. The prognosis of patients with myocardial bridge is usually good, but further studies are needed to evaluate the long-term prognosis of these patients, the adequate diagnostic and preventive approach and to better discern which patients should be candidate to medical, percutaneous or surgical treatment.

The widespread adoption of large-scale decentralized peer-to-peer (P2P) systems imposes huge challenges on distributed search and routing. Decentralized and unstructured P2P networks are very attractive because they require neither centralized directories, nor precise control over network topology or data placement. However their search mechanisms are extremely unscalable, generating large loads on the network participants. In this paper, to address this major limitation, we propose and evaluate the adoption of an innovative algorithm for routing user queries. The proposed approach aims at dynamically adapting the network topology to peer interests, on the basis of query interactions among users. Preliminaries evaluations show that the approach is able to dynamically group peer nodes in clusters containing peers with shared interests and organized into a small world topology.

This paper proposes a new heuristic for the dynamic version of the Steiner Tree Problem in Networks (SPN). The heuristic adopts a Random Neural Network (RNN) to improve solutions obtained by previously proposed dynamic algorithms. The Random Neural Network model is adapted to map the intrinsic features of the multicast transmission on a computer network. Exhaustive experiments are carried out to validate the proposed methodology.

Encryption algorithms based on block ciphers are among the most widely adopted solutions for providing information security. Over the years, a variety of methods have been proposed to evaluate the robustness of these algorithms to different types of security attacks. One of the most effective analysis techniques is differential cryptanalysis, whose aim is to study how variations in the input propagate on the output. In this work we address the modeling of differential attacks to block cipher algorithms by defining a Bayesian framework that allows a probabilistic estimation of the secret key. In order to prove the validity of the proposed approach, we present as case study a differential attack to the Data Encryption Standard (DES) which, despite being one of the methods that has been most thoroughly analyzed, is still of great interest to the scientific community since its vulnerabilities may have implications on other ciphers.

The present case-report empathizes the usefulness of post-mortem computed tomography (PMCT) in the detection of live birth signs in a baby found dead, comparing the results with standard autoptic, histologic and immunohistochemical examinations.The report describes the case of a new-born who was thrown out on the footpath from a window of the fifth floor according to the investigative police, whilst the mother has hidden herself immediately after childbirth.PMCT showed diffusely aerated lung parenchyma with signs of severe lung distress, air in the airways with no fluid inside, and air in the upper gastro-intestinal tract, in absence any resuscitation manoeuvres performed on the baby at delivery. PMCT underlined multiple fractures of the skull with degeneration of cerebral parenchyma.All these findings were confirmed by conventional autopsy and histological and immunohistochemical examinations, clinching the usefulness of PMCT in differentiating live births from stillbirths.(c) 2023 Elsevier B.V. All rights reserved.

Distributed environments consist of a huge number of entities that cooperate to achieve complex goals. When interactions occur between unknown parties, intelligent techniques for estimating agent reputations are required. Reputation management systems (RMS's) allow agents to perform such estimation in a cooperative way. In particular, distributed RMS's exploit feedbacks provided after each interaction and allow prediction of future behaviors of agents. Such systems, in contrast to centralized RMSs, are sensitive to fake information injected by malicious users; thus, predicting the performance of a distributed RMS is a very challenging task. Although many existing works have addressed some challenges concerning the design and assessment of specific RMS's, there are no simulation environments that adopt a general approach that can be applied to different application scenarios. To overcome this lack, we present DRESS, an agent-based simulation framework that aims to support researchers in the evaluation of distributed RMSs under different security attacks.

Objective: To identify the best method among the radiologic, laparoscopic and laparotomic scoring assessment to predict the outcomes of cytoreductive surgery in patients with advanced ovarian cancer (AOC). Methods: Patients with AOC who underwent pre-operative computed tomography (CT) scan, laparoscopic evaluation, and cytoreductive surgery between August 2016 and February 2021 were retrospectively reviewed. Predictive Index (PI) score and Peritoneal Cancer Index (PCI) scores were used to estimate the tumor load and predict the residual disease in the primary debulking surgery (PDS) and interval debulking surgery (IDS) after neoadjuvant chemotherapy (NACT) groups. Concordance percentages were calculated between the two scores. Results: Among 100 eligible patients, 69 underwent PDS, and 31 underwent NACT and IDS. Complete cytoreduction was achieved in 72.5% of patients in the PDS group and 77.4% in the IDS. In patients undergoing PDS, the laparoscopic PI and the laparotomic PCI had the best accuracies for complete cytoreduction (R0) [area under the curve (AUC) = 0.78 and AUC = 0.83, respectively]. In the IDS group, the laparotomic PI (AUC = 0.75) and the laparoscopic PCI (AUC= 0.87) were associated with the best accuracy in R0 prediction. Furthermore, radiological assessment, through PI and PCI, was associated with the worst accuracy in either PDS or IDS group (PI in PDS: AUC = 0.64; PCI in PDS: AUC = 0.64; PI in IDS: AUC = 0.46; PCI in IDS: AUC = 0.47). Conclusion: The laparoscopic score assessment had high accuracy for optimal cytoreduction in AOC patients undergoing PDS or IDS. Integrating diagnostic laparoscopy in the decision-making algorithm to accurately triage AOC patients to different treatment strategies seems necessary.

In recent years, the widespread diffusion of pervasive sensing devices and the increasing need for reducing energy consumption have encouraged research in the energy-aware management of smart environments. Following this direction, this paper proposes a hybrid intelligent system which exploits a fog-based architecture to achieve energy efficiency in smart buildings. Our proposal combines reactive intelligence, for quick adaptation to the ever-changing environment, and deliberative intelligence, for performing complex learning and optimization. Such hybrid nature allows our system to be adaptive, by reacting in real time to relevant events occurring in the environment and, at the same time, to constantly improve its performance by learning users' needs. The effectiveness of our approach is validated in the application scenario of a smart home by extensive experiments on real sensor traces. Experimental results show that our system achieves substantial energy savings in the management of a smart environment, whilst satisfying users' needs and preferences.

The applications of forensic radiology involve both Virtopsy both studies on living people - to demonstrate bone age, search for foreign bodies, such as voluntary injection of drug ovules or surgical sponges accidentally forgotten, to assess gunshot wounds, to evaluate injuries by road accidents, and cases of violence or abuse (both in adults and in children). Computed tomography is the most used imaging tool used in forensic pathology and its indications are mainly focused on cases of unnatural deaths or when a crime is suspected. It is preferred over the standard autopsy in selected cases, such as in putrefied, carbonized or badly damaged bodies; or as a preliminary evaluation in mass disasters.

Thanks to the collective action of participating smartphone users, mobile crowdsensing allows data collection at a scale and pace that was once impossible. The biggest challenge to overcome in mobile crowdsensing is that participants may exhibit malicious or unreliable behavior, thus compromising the accuracy of the data collection process. Therefore, it becomes imperative to design algorithms to accurately classify between reliable and unreliable sensing reports. To address this crucial issue, we propose a novel Framework for optimizing Information Reliability in Smartphone-based participaTory sensing (FIRST) that leverages mobile trusted participants (MTPs) to securely assess the reliability of sensing reports. FIRST models and solves the challenging problem of determining before deployment the minimum number of MTPs to be used to achieve desired classification accuracy. After a rigorous mathematical study of its performance, we extensively evaluate FIRST through an implementation in iOS and Android of a room occupancy monitoring system and through simulations with real-world mobility traces. Experimental results demonstrate that FIRST reduces significantly the impact of three security attacks (i.e., corruption, on/off, and collusion) by achieving a classification accuracy of almost 80% in the considered scenarios. Finally, we discuss our ongoing research efforts to test the performance of FIRST as part of the National Map Corps project.

In this article, we present Distributed Computing for Constrained Devices (DC4CD), a novel software architecture that supports symbolic distributed computing on wireless sensor networks. DC4CD integrates the functionalities of a high-level symbolic interpreter, a compiler, and an operating system, and includes networking abstractions to exchange high-level symbolic code among peer devices. Contrarily to other architectures proposed in the literature, DC4CD allows for changes at runtime, even on deployed nodes of both application and system code. Experimental results show that DC4CD is more efficient in terms of memory usage than existing architectures, with which it also compares well in terms of execution efficiency.

This book offers a comprehensive overview of the forensic and radiological aspects of pathological findings, focusing on the most relevant medico-legal issues, such as virtual autopsy (virtopsy), anthropometric identification, post-mortem decomposition features and the latest radiological applications used in forensic investigations. Forensic medicine and radiology are becoming increasingly relevant in the international medical and legal field as they offer essential techniques for determining cause of death and for anthropometric identification. This is highly topical in light of public safety and economic concerns arising as a result of mass migration and international tensions. The book discusses the latest technologies applied in the forensic field, in particular computed tomography and magnetic resonance, which are continuously being updated. Radiological techniques are fundamental in rapidly providing a full description of the damage inflicted to add to witness and medical testimonies, and forensic/radiological anthropology supplies valuable evidence in cases of violence and abuse. Written by international experts, it is of interest to students and residents in forensic medicine and radiology. It also presents a new approach to forensic investigation for lawyers and police special corps as well as law enforcement agencies.

Objective: The aim of this study was to acknowledge errors in patients positioning in CT colonography (CTC) and their effect in radiation exposure. Materials and methods: CTC studies of a total of 199 patients coming from two different referral hospitals were retrospectively reviewed. Two parameters have been considered for the analysis: patient position in relation to gantry isocentre and scan length related to the area of interest. CTDI vol and DLP were extracted for each patient. In order to evaluate the estimated effective total dose and the dose to various organs, we used the CT-EXPO ® software version 2.2. This software provides estimates of effective dose and doses to the other various organs. Results: Average value of the patients’ position is found to be below the isocentre for 48 ± 25mm and 29 ± 27mm in the prone and supine position. It was observed that the increase in CTDI and DLP values for patients in Group 1, due to the inaccurate positioning, was estimated at about 30% and 20% for prone and supine position, respectively, while in Group 2, a decrease in CTDI and DLP values was estimated at about 16% and 18% for prone and supine position, respectively, due to an average position above isocentre. A dose increase ranging from 4 up to 13% was calculated with increasing the over-scanned region below anal orifice. Conclusion: Radiographers and radiologists need to be aware of dose variation and noise effects on vertical positioning and over-scanning. More accurate training need to be achieved even so when examination protocol varies from general practice.

In distributed environments, where unknown entities cooperate to achieve complex goals, intelligent techniques for estimating agents' truthfulness are required. Distributed Reputation Management Systems (RMSs) allow to accomplish this task without the need for a central entity that may represent a bottleneck and a single point of failure. The design of a distributed RMS is a challenging task due to a multitude of factors that could impact on its performances. In order to support the researcher in evaluating the RMS robustness against security attacks since its beginning design phase, in this work we present a distributed simulation environment that allows to model both the agent's behaviors and the logic of the RMS itself. Moreover, in order to compare at simulation time the performance of the designed distributed RMS with a baseline obtained by an ideal RMS, we introduce an omniscient process called truth-holder which owns a global knowledge all involved entities. The effectiveness of our platform was proved by a set of experiments aimed at measuring the vulnerability of a RMS to a common set of security attacks.

In recent years, the increasing diffusion of malicious software has encouraged the adoption of advanced machine learning algorithms to timely detect new threats. A cloud-based approach allows to exploit the big data produced by client agents to train such algorithms, but on the other hand, poses severe challenges on their scalability and performance. We propose a hybrid cloud-based malware detection system in which static and dynamic analyses are combined in order to find a good trade-off between response time and detection accuracy. Our system performs a continuous learning process of its models, based on deep networks, by exploiting the growing amount of data provided by clients. The preliminary experimental evaluation confirms the suitability of the approach proposed here.

Emergency and trauma care produces a "perfect storm" for radiological errors: uncooperative patients, inadequate histories, time-critical decisions, concurrent tasks and often junior personnel working after hours in busy emergency departments. The main cause of diagnostic errors in the emergency department is the failure to correctly interpret radiographs, and the majority of diagnoses missed on radiographs are fractures. Missed diagnoses potentially have important consequences for patients, clinicians and radiologists. Radiologists play a pivotal role in the diagnostic assessment of polytrauma patients and of patients with non-traumatic craniothoracoabdominal emergencies, and key elements to reduce errors in the emergency setting are knowledge, experience and the correct application of imaging protocols. This article aims to highlight the definition and classification of errors in radiology, the causes of errors in emergency radiology and the spectrum of diagnostic errors in radiography, ultrasonography and CT in the emergency setting.