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Abstract Adhesive Capsulitis of the shoulder is a painful pathology limiting shoulder movements, commonly known as “Frozen Shoulder”. Since this pathology limits movement, it is important to make an early diagnosis. Diagnosing capsulitis relies on clinical assessment, although diagnostic imaging, such as Magnetic Resonance Imaging, can provide predictive or supportive information for specific characteristic signs. However, its diagnosis is not so simple nor so immediate, indeed it remains a difficult topic for many general radiologists and expert musculoskeletal radiologists. This study aims to investigate whether it is possible to use disease signs within a medical image to automatically diagnose Adhesive Capsulitis. To this purpose, we propose an automatic Model Checking-based approach to quickly diagnose the Adhesive Capsulitis taking as input the radiomic feature values from the medical images. Furthermore, we compare the performance achieved by our method with diagnostic results obtained by professional radiologists with different levels of experience. To the best of our knowledge, this is the first method for the automatic diagnosis of Adhesive Capsulitis of the Shoulder.

Lung screening is really crucial in the early detection and management of masses, with particular regard to cancer. Studies have shown that lung cancer screening, can reduce lung cancer mortality by 20–30% in high-risk populations. In recent times, the advent of deep learning, with particular regard to computer vision, demonstrated the ability to effectively detect and locate objects from video streams and also (medical) images. Considering these aspects, in this paper, we propose a method aimed to perform instance segmentation, i.e., by providing a mask for each lung mass instance detected, allowing for the identification of individual masses even if they overlap or are close to each other by classifying the detected masses into (generic) nodules, cancer or adenocarcinoma. In this paper, we considered the you-only-look-once model for lung nodule segmentation. An experimental analysis, performed on a set of real-world lung computed tomography images, demonstrated the effectiveness of the proposed method not only in the detection of lung masses but also in lung mass segmentation, thus providing a helpful way not only for radiologist to conduct automatic lung screening but also for discovering very small masses not easily recognizable to the naked eye and that may deserve attention. As a matter of fact, in the evaluation of a dataset composed of 3654 lung scans, the proposed method obtains an average precision of 0.757 and an average recall of 0.738 in the classification task. Additionally, it reaches an average mask precision of 0.75 and an average mask recall of 0.733. These results indicate that the proposed method is capable of not only classifying masses as nodules, cancer, and adenocarcinoma, but also effectively segmenting the areas, thereby performing instance segmentation.

Background: Endovascular intervention is now the primary line of therapy for arterial injury brought on by pelvic trauma since it can significantly reduce considerable morbidity associated with surgery and can swiftly access and control bleeding sites. Despite international guidelines and widespread awareness of the role of angioembolization in clinical practice, robust evidence comparing the outcomes of angioembolization in hemodynamically stable and unstable patients is still lacking. This study aims to directly compare the outcomes of angioembolization for the treatment of pelvic traumatic arterial injury in patients with hemodynamic stability vs. hemodynamic instability. Methods: In our multicenter retrospective investigation, we analyzed data from consecutive patients who underwent, from January 2020 to May 2023, angioembolization for traumatic pelvic arterial injury. Results: In total, 116 angioembolizations were performed. Gelatin sponges (56.9%) and coils (25.9%) were the most widely used embolic agents. The technical and clinical success rates were 100% and 91.4%, respectively. No statistically significant differences were observed between the two groups in terms of technical success, clinical success, procedure-related complication rate, or 30-day bleeding-related mortality. Conclusions: Angioembolization is an effective and safe option for the management of traumatic pelvic arterial lesions even in hemodynamically unstable patients, despite technical variations such as greater use of prophylactic angioembolization.

Inflammation-based scores are biomarkers of the crosstalk between the tumor microenvironment and the immune response. Investigating the intricate relationship between the tumor stromal microenvironment, biomarkers, and the response to transcatheter arterial chemoembolization (TACE) is essential for early identification of TACE refractoriness or failure, providing insights into tumor biology and facilitating personalized therapeutic interventions. This study addresses a dearth of recent literature exploring the prognostic significance of the preoperative LMR in individuals from western countries diagnosed with stage B hepatocellular carcinoma (HCC) undergoing drug eluting microspheres TACE (DEM-TACE) or conventional TACE (cTACE). This international multi-center retrospective analysis included consecutive patients with stage B HCC who underwent TACE from January 2017 to June 2023. The study evaluated the ability of the preoperative LMR to predict complete response (CR), objective response (OR), sustained response duration (SRD) exceeding 6 months, successful downstaging at 6 months, progression-free survival (PFS) at 6 months, and overall survival (OS) at 6 months. The study population included 109 HCC patients and it was divided into low LMR (LMR < 2.24) and high LMR (LMR ≥ 2.24) groups, according to ROC curve analysis to select the optimal LMR cut-off value. High LMR was associated with lower Hepatitis C prevalence, higher absolute lymphocyte count, and a trend toward lower alpha-fetoprotein. The group with high LMRs exhibited superior CR rates (14.9% vs. 0%), overall OR (43.2% vs. 14.3%), and better PFS at 6 months (75.7% vs. 45.7%). The LMR, specifically categorized as

Soft tissue sarcomas (STSs) are rare, heterogeneous, and very often asymptomatic diseases. Their diagnosis is fundamental, as is the identification of the degree of malignancy, which may be high, medium, or low. The Italian Medical Oncology Association and European Society of Medical Oncology (ESMO) guidelines recommend magnetic resonance imaging (MRI) because the clinical examination is typically ineffective. The diagnosis of these rare diseases with artificial intelligence (AI) techniques presents reduced datasets and therefore less robust methods. However, the combination of AI techniques with radiomics may be a new angle in diagnosing rare diseases such as STSs. Results obtained are promising within the literature, not only for the performance but also for the explicability of the data. In fact, one can make tumor classification, site localization, and prediction of the risk of developing metastasis. Thanks to the synergy between computer scientists and radiologists, linking numerical features to radiological evidence with excellent performance could be a new step forward for the diagnosis of rare diseases.

Abstract The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical management of patients with the coronavirus disease has undergone an evolution over the months, thanks to the increasing knowledge of the virus, symptoms and efficacy of the various therapies. Currently, however, there is no specific therapy for SARS-CoV-2 virus, know also as Coronavirus disease 19, and treatment is based on the symptoms of the patient taking into account the overall clinical picture. Furthermore, the test to identify whether a patient is affected by the virus is generally performed on sputum and the result is generally available within a few hours or days. Researches previously found that the biomedical imaging analysis is able to show signs of pneumonia. For this reason in this paper, with the aim of providing a fully automatic and faster diagnosis, we design and implement a method adopting deep learning for the novel coronavirus disease detection, starting from computed tomography medical images. The proposed approach is aimed to detect whether a computed tomography medical images is related to an healthy patient, to a patient with a pulmonary disease or to a patient affected with Coronavirus disease 19. In case the patient is marked by the proposed method as affected by the Coronavirus disease 19, the areas symptomatic of the Coronavirus disease 19 infection are automatically highlighted in the computed tomography medical images. We perform an experimental analysis to empirically demonstrate the effectiveness of the proposed approach, by considering medical images belonging from different institutions, with an average time for Coronavirus disease 19 detection of approximately 8.9 s and an accuracy equal to 0.95.

The recent introduction of generative adversarial networks has demonstrated remarkable capabilities in generating images that are nearly indistinguishable from real ones. Consequently, both the academic and industrial communities have raised concerns about the challenge of differentiating between fake and real images. This issue holds significant importance, as images play a vital role in various domains, including image recognition and bioimaging classification in the biomedical field. In this paper, we present a method to assess the distinguishability of bioimages generated by a generative adversarial network, specifically using a dataset of retina images. Once the images are generated, we train several supervised machine learning models to determine whether these classifiers can effectively discriminate between real and fake retina images. Our experiments utilize a deep convolutional generative adversarial network, a type of generative adversarial network, and demonstrate that the generated images, although visually imperceptible as fakes, are correctly identified by a classifier with an F-Measure greater than 0.95. While the majority of the generated images are accurately recognized as fake, a few of them are not classified as such and are consequently considered real retina images.

Background and Objectives: Treatment of visceral artery pseudoaneurysms (VAPs) is always indicated regardless of their diameters, as their risk of rupture is significantly higher than that of visceral artery aneurysms. The invasiveness of surgery and its associated complications have led to a shift in favor of radiological interventions as the initial treatment of choice. However, there are still some unanswered questions on endovascular treatment of VAPs regarding the optimal endovascular technique and the efficacy and safety outcomes. The purpose of this multicenter study was to retrospectively evaluate the effectiveness and safety of endovascular treatment of visceral pseudoaneurysms using Ethylene-Vinyl Alcohol (EVOH) Copolymer-Based Non-Adhesive Liquid Embolic Agents (NALEAs). Materials and Methods: Consecutive patients who underwent endovascular embolization with EVOH-based NALEAs for visceral artery pseudoaneurysms between January 2018 and June 2023 were retrospectively evaluated. Results: 38 embolizations were performed. Technical success was achieved in all patients. The clinical success rate was high (92.1% overall), with no significant differences between ruptured and unruptured VAPs (p = 0.679). Seven patients (18.4%) experienced procedure-related complications, related to one case of non-target embolization, four splenic abscesses due to end-organ infarction, and two femoral pseudoaneurysms. The rates of procedure-related complications, end-organ infarction, and vascular access-site complications did not significantly differ between ruptured and unruptured VAPs (p > 0.05). Conclusions: Both ruptured and unruptured visceral pseudoaneurysms can be effectively and safely treated with NALEA-based endovascular embolization. We suggest considering the use of NALEAs, particularly in specific clinical cases that highlight their advantages, including patients with coagulopathy, fragile vessels, and embolization targets that are located at a considerable distance from the microcatheter tip and are otherwise difficult to reach.

Brain cancer is widely recognised as one of the most aggressive types of tumors. In fact, approximately 70% of patients diagnosed with this malignant cancer do not survive. In this paper, we propose a method aimed to detect and localise brain cancer, starting from the analysis of magnetic resonance images. The proposed method exploits deep learning, in particular convolutional neural networks and class activation mapping, in order to provide explainability by highlighting the areas of the medical image related to brain cancer (from the model point of view). We evaluate the proposed method with 3000 magnetic resonances using a free available dataset. The results we obtained are encouraging. We reach an accuracy ranging from 97.83% to 99.67% in brain cancer detection by exploiting four different models: VGG16, ResNet50, Alex_Net, and MobileNet, thus showing the effectiveness of the proposed method.

Brain cancer is acknowledged as one of the most aggressive tumors, with a significant impact on patient survival rates. Unfortunately, approximately 70% of patients diagnosed with this malignant cancer do not survive. This paper introduces a method designed to detect and localize brain cancer by proposing an automated approach for the detection and localization of brain cancer. The method utilizes magnetic resonance imaging analysis. By leveraging the information provided by brain medical images, the proposed method aims to enhance the detection and precise localization of brain cancer to improve the prognosis and treatment outcomes for patients. We exploit the YOLO model to automatically detect and localize brain cancer: in the analysis of 300 brain images we obtain a precision of 0.943 and a recall of 0.923 in brain cancer detection while, relating to brain cancer localization, an mAP_0.5 equal to 0.941 is reached, thus showing the effectiveness of the proposed model for brain cancer detection and localization.

Background and Objectives: Roughly 10% of cancer patients experience an episode of bleeding. The bleeding severity can range from occasional trivial bleeds to major bleeding. The treatment for the bleeding may vary, depending on the clinical condition and anatomical site, and may include various strategies, among which TAE is a cornerstone of major bleeding management. However, the existing literature on tumor hemorrhages is inconsistent. The objective of this multicenter retrospective cohort study was to evaluate the effectiveness and safety of arterial embolization in the treatment of tumor hemorrhages in patients with solid cancers. Materials and Methods: The data for patients with solid cancers undergoing TAE for the management of tumor hemorrhages from January 2020 to May 2023 were gathered. Results: A total of 92 patients with cancer-related bleeding were treated between January 2020 and May 2023. No bleeding was detected by X-ray angiography (XA) in 12 (13%) cases; therefore, a blind embolization was performed. The most common bleeding site was the liver (21.7%). A total of 66 tumor hemorrhages were spontaneous. The most commonly used embolic agent was polyvinyl alcohol (PVA) particles (30.4%). Technical success was achieved in 82 (89.1%) cases, with an 84.8% clinical success rate related to 14 cases of rebleeding. Proximal embolization was performed for 19 (20.7%) patients. Complications were recorded for 10 (10.9%) patients. The 30-day bleeding-related mortality was 15.2%. The technical success, clinical success, proximal embolization rate, and 30-day rebleeding were worse in the subset of patients undergoing TAE with coils. Conclusions: Transcatheter arterial embolization (TAE) represents a viable and potentially life-saving therapeutic approach in the management of tumor hemorrhages, demonstrating a notable effectiveness and safety. The TAE of bleeding tumors using coils resulted in a higher rate of non-superselective proximal embolization, with a trend toward lower clinical success rates and higher rebleeding episodes.

Background and Objectives: Transcatheter arterial embolization (TAE) is the mainstay of treatment for acute major hemorrhage, even in patients with coagulopathy and spontaneous bleeding. Coagulopathy is associated with worsening bleeding severity and higher mortality and clinical failure rates. Furthermore, some unanswered questions remain, such as the definition of coagulopathy, the indication for TAE or conservative treatment, and the choice of embolic agent. This study aims to assess the efficacy and safety of TAE for spontaneous non-neurovascular acute bleeding in patients with coagulopathy. Materials and Methods: This study is a multicenter analysis of retrospectively collected data of consecutive patients with coagulopathy who had undergone, from January 2018 to May 2023, transcatheter arterial embolization for the management of spontaneous hemorrhages. Results: During the study interval (January 2018–May 2023), 120 patients with coagulopathy underwent TAE for spontaneous non-neurovascular acute bleeding. The abdominal wall was the most common bleeding site (72.5%). The most commonly used embolic agent was polyvinyl alcohol (PVA) particles or microspheres (25.0%), whereas coils and gelatin sponge together accounted for 32.5% of the embolic agents used. Technical success was achieved in all cases, with a 92.5% clinical success rate related to 9 cases of rebleeding. Complications were recorded in 12 (10%) patients. Clinical success was significantly better in the group of patients who underwent correction of the coagulopathy within 24 h of TAE. Conclusions: Transcatheter arterial embolization (TAE) is effective and safe for the management of acute non-neurovascular bleeding in patients with coagulopathy. Correction of coagulopathy should not delay TAE and vice versa, as better clinical outcomes were noted in the subgroup of patients undergoing correction of coagulopathy within 24 h of TAE.

Currently, deep learning networks, with particular regard to convolutional neural network models, are typically exploited for biomedical image classification. One of the disadvantages of deep learning is that is extremely expensive to train due to complex data models. Extreme learning machine has recently emerged which, as shown in experimental studies, can produce an acceptable predictive performance in several classification tasks, and at a much lower training cost compared to deep learning networks that are trained by backpropagation. We propose a method devoted to exploring the possibility of considering extreme learning machines for biomedical classification tasks. Binary and multiclass classification in four case studies are considered to demonstrate the effectiveness of extreme learning machine, considering the biomedical images acquired with the dermatoscope and with the blood cell microscope, showing that the extreme learning machine can be successfully applied for biomedical image classification.

Background and Objectives: Increasing attention is being paid to the coagulation disorders associated with SARS-CoV-2 infection. Bleeding accounts for 3–6% of COVID-19 patient deaths, and is often a forgotten part of the disease. The bleeding risk is enhanced by several factors, including spontaneous heparin-induced thrombocytopenia, thrombocytopenia, the hyperfibrinolytic state, the consumption of coagulation factors, and thromboprophylaxis with anticoagulants. This study aims to assess the efficacy and safety of TAE in the management of bleeding in COVID-19 patients. Materials and Methods: This multicenter retrospective study analyzes data from COVID-19 patients subjected to transcatheter arterial embolization for the management of bleeding from February 2020 to January 2023. Results: Transcatheter arterial embolization was performed in 73 COVID-19 patients for acute non-neurovascular bleeding during the study interval (February 2020–January 2023). Coagulopathy was observed in forty-four (60.3%) patients. The primary cause of bleeding was spontaneous soft tissue hematoma (63%). A 100% technical success rate was recorded; six cases of rebleeding resulted in a 91.8% clinical success rate. No cases of non-target embolization were observed. Complications were recorded in 13 (17.8%) patients. The efficacy and safety endpoints did not differ significantly between the coagulopathy and non-coagulopathy groups. Conclusions: Transcatheter Arterial Embolization (TAE) is an effective, safe and potentially life-saving option for the management of acute non-neurovascular bleeding in COVID-19 patients. This approach is effective and safe even in the subgroup of COVID-19 patients with coagulopathy.

Peritoneal carcinosis is a condition characterized by the spread of cancer cells to the peritoneum, which is the thin membrane that lines the abdominal cavity. It is a serious condition that can result from many different types of cancer, including ovarian, colon, stomach, pancreatic, and appendix cancer. The diagnosis and quantification of lesions in peritoneal carcinosis are critical in the management of patients with the condition, and imaging plays a central role in this process. Radiologists play a vital role in the multidisciplinary management of patients with peritoneal carcinosis. They need to have a thorough understanding of the pathophysiology of the condition, the underlying neoplasms, and the typical imaging findings. In addition, they need to be aware of the differential diagnoses and the advantages and disadvantages of the various imaging methods available. Imaging plays a central role in the diagnosis and quantification of lesions, and radiologists play a critical role in this process. Ultrasound, computed tomography, magnetic resonance, and PET/CT scans are used to diagnose peritoneal carcinosis. Each imaging procedure has advantages and disadvantages, and particular imaging techniques are recommended based on patient conditions. Our aim is to provide knowledge to radiologists regarding appropriate techniques, imaging findings, differential diagnoses, and treatment options. With the advent of AI in oncology, the future of precision medicine appears promising, and the interconnection between structured reporting and AI is likely to improve diagnostic accuracy and treatment outcomes for patients with peritoneal carcinosis.

Purpose: The purpose of this study was to evaluate the prevalence and the underreporting rate of sacroiliitis (SI) in a large cohort of patients with biopsy-proved Crohn’s disease (CD) who underwent magnetic resonance enterography (MRE) or computed tomography enterography (CTE). Materials and Methods: Patients with CD were recruited from eight Italian health centers in the period from January 2013 to December 2017. Disease activity was recorded according to the CD activity index (CDAI). The scans were read by two blinded readers who defined the presence of SI according to Assessment of SpondyloArthritis International Society (ASAS) classifications and European League Against Rheumatism (EULAR) recommendations. Moreover, SI was scored using a simplified Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system. Results: Interobserver agreement in diagnosing SI on imaging was good (K = 0.72–0.83). SI was diagnosed in 129 (14.4%, 54 men, 75 women) out of 894 patients; however, sacroiliac joint (SIJ) abnormalities were not mentioned in the radiological reports of 112 patients (86%). Fifty (38.7%) out of 129 patients also underwent a subsequent SIJ evaluation through a dedicated MRI protocol to confirm SI. SI was found in a higher percentage of patients with “active” than “inactive” CD (18% vs. 4%). Conclusion: This study confirms the feasibility of CTE and MRE for the screening of SI in CD patients; however, it also underlines the remarkable problem concerning the underreporting of this entity in radiological practice.

Solitary pulmonary nodules (SPNs) are a diagnostic and therapeutic challenge for thoracic surgeons. Although such lesions are usually benign, the risk of malignancy remains significant, particularly in elderly patients, who represent a large segment of the affected population. Surgical treatment in this subset, which usually presents several comorbidities, requires careful evaluation, especially when pre-operative biopsy is not feasible and comorbidities may jeopardize the outcome. Radiomics and artificial intelligence (AI) are progressively being applied in predicting malignancy in suspicious nodules and assisting the decision-making process. In this study, we analyzed features of the radiomic images of 71 patients with SPN aged more than 75 years (median 79, IQR 76–81) who had undergone upfront pulmonary resection based on CT and PET-CT findings. Three different machine learning algorithms were applied—functional tree, Rep Tree and J48. Histology was malignant in 64.8% of nodules and the best predictive value was achieved by the J48 model (AUC 0.9). The use of AI analysis of radiomic features may be applied to the decision-making process in elderly frail patients with suspicious SPNs to minimize the false positive rate and reduce the incidence of unnecessary surgery.

BackgroundStructured reporting (SR) in radiology is becoming increasingly necessary and has been recognized recently by major scientific societies. This study aims to build structured CT-based reports in Neuroendocrine Neoplasms during the staging phase in order to improve communication between the radiologist and members of multidisciplinary teams.Materials and MethodsA panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A Modified Delphi process was used to develop the SR and to assess a level of agreement for all report sections. Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation.ResultsThe final SR version was built by including n=16 items in the “Patient Clinical Data” section, n=13 items in the “Clinical Evaluation” section, n=8 items in the “Imaging Protocol” section, and n=17 items in the “Report” section. Overall, 54 items were included in the final version of the SR. Both in the first and second round, all sections received more than a good rating: a mean value of 4.7 and range of 4.2-5.0 in the first round and a mean value 4.9 and range of 4.9-5 in the second round. In the first round, the Cα correlation coefficient was a poor 0.57: the overall mean score of the experts and the sum of scores for the structured report were 4.7 (range 1-5) and 728 (mean value 52.00 and standard deviation 2.83), respectively. In the second round, the Cα correlation coefficient was a good 0.82: the overall mean score of the experts and the sum of scores for the structured report were 4.9 (range 4-5) and 760 (mean value 54.29 and standard deviation 1.64), respectively.ConclusionsThe present SR, based on a multi-round consensus-building Delphi exercise following in-depth discussion between expert radiologists in gastro-enteric and oncological imaging, derived from a multidisciplinary agreement between a radiologist, medical oncologist and surgeon in order to obtain the most appropriate communication tool for referring physicians.

Currently, there are 3.1 million American men affected by prostate cancer. Early detection represents the only way to safe lives. To evaluate a prostate cancer, the most widespread rank is the so-called Gleason score, based on the microscopic cancer appearance. Once assigned to the diagnosed prostate cancer its relative Gleason score, the correct therapy to be adopted must be promptly defined. To support pathologists and radiologists in timely diagnosis, in this paper we propose a method aimed to infer the Gleason score and the prostate cancer therapy exploiting formal methods. We consider a set of radiomic features directly obtained from magnetic resonance images. For this reason the proposed method is non invasive, since it does not require a biopsy. We model magnetic resonance images of patients as timed automata networks and we assign the Gleason score and the relative treatment, exploiting a set of temporal logic properties. In the experimental analysis, the properties are verified on 36 different patients, confirming the effectiveness of the proposed method with a sensitivity and a specificity equal to 1 for all the evaluated cases in Gleason score inference, and a sensitivity equal to 0.94 and a specificity equal to 1 in treatment prediction.

Purpose: To investigate the early CT findings in COVID-19 pneumonia as compared to influenza A virus H1N1 (AH1N1), with focus on vascular enlargement within consolidation or ground glass opacity (GGO) areas. Methods: 50 patients with COVID-19 pneumonia were retrospectively compared to 50 patients with AH1N1 pneumonia diagnosed during the 2009 pandemic. Two radiologists reviewed chest CT scans independently and blindly, with discordance resolved by consensus. Dilated or tortuous vessels within hyperdense lesions were recorded. Results: COVID-19 pneumonia presented with bilateral (96%), peripheral areas of GGO (22%), consolidation (4%) or combined GGO-consolidation (74%). The vascular enlargement sign in COVID-19 pneumonia was much more commonly present in COVID-19 (45/50, 90%) versus AH1N1 pneumonia (12/50, 24%) (p < 0.001). Vascular enlargement was more often present in lower lobes with a peripheral distribution. Conclusions: Vascular enlargement in consolidative/GGO areas may represent a reasonably common early CT marker in COVID-19 patients and is of uncertain etiology. Although speculative, theoretical mechanisms could potentially reflect acute inflammatory changes, pulmonary endothelial activation, or acute stasis. Further studies are necessary to verify specificity and to study if prognostic for clinical outcomes.

Background: Respiratory sound analysis represents a research topic of growing interest in recent times. In fact, in this area, there is the potential to automatically infer the abnormalities in the preliminary stages of a lung dysfunction. Methods: In this paper, we propose a method to analyse respiratory sounds in an automatic way. The aim is to show the effectiveness of machine learning techniques in respiratory sound analysis. A feature vector is gathered directly from breath audio and, thus, by exploiting supervised machine learning techniques, we detect if the feature vector is related to a patient affected by a lung disease. Moreover, the proposed method is able to characterise the lung disease in asthma, bronchiectasis, bronchiolitis, chronic obstructive pulmonary disease, pneumonia, and lower or upper respiratory tract infection. Results: A retrospective experimental analysis on 126 patients with 920 recording sessions showed the effectiveness of the proposed method. Conclusion: The experimental analysis demonstrated that it is possible to detect lung disease by exploiting machine learning techniques. We considered several supervised machine learning algorithms, obtaining the most interesting performance with the neural network model, with an F-Measure of 0.983 in lung disease detection and equal to 0.923 in lung disease characterisation, increasing the state-of-the-art performance.

This article provides an overview of imaging assessment of ablated pancreatic cancer. Only studies reporting radiological assessment on pancreatic ablated cancer were retained. We found 16 clinical studies that satisfied the inclusion criteria. Radiofrequency ablation and irreversible electroporation have become established treatment modalities because of their efficacy, low complication rates, and availability. Microwave Ablation (MWA) has several advantages over radiofrequency ablation (RFA), which may make it more attractive to treat pancreatic cancer. Electrochemotherapy (ECT) is a very interesting emerging technique, characterized by low complication rate and safety profile. According to the literature, the assessment of the effectiveness of ablative therapies is difficult by means of the Response Evaluation Criteria in Solid Tumors (RECIST) criteria that are not suitable to evaluate the treatment response considering that are related to technique used, the timing of reassessment, and the imaging procedure being used to evaluate the efficacy. RFA causes various appearances on imaging in the ablated zone, correlating to the different effects, such as interstitial edema, hemorrhage, carbonization, necrosis, and fibrosis. Irreversible electroporation (IRE) causes the creation of pores within the cell membrane causing cell death. Experimental studies showed that Diffusion Weigthed Imaging (DWI) extracted parameters could be used to detect therapy effects. No data about functional assessment post MWA is available in literature. Morphologic data extracted by Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) do not allow to differentiate partial, complete, or incomplete response after ECT conversely to functional parameters, obtained with Position Emission Tomography (PET), MRI, and CT.

Background: Structured reporting (SR) in radiology has been recognized recently by major scientific societies. This study aims to build structured computed tomography (CT) and magnetic resonance (MR)-based reports in pancreatic adenocarcinoma during the staging phase in order to improve communication between the radiologist and members of multidisciplinary teams. Materials and Methods: A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi process was used to develop the CT-SR and MRI-SR, assessing a level of agreement for all report sections. Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation. Results: The final CT-SR version was built by including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 7 items in the “Imaging Protocol” section, and n = 18 items in the “Report” section. Overall, 52 items were included in the final version of the CT-SR. The final MRI-SR version was built by including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 8 items in the “Imaging Protocol” section, and n = 14 items in the “Report” section. Overall, 49 items were included in the final version of the MRI-SR. In the first round for CT-SR, all sections received more than a good rating. The overall mean score of the experts was 4.85. The Cα correlation coefficient was 0.85. In the second round, the overall mean score of the experts was 4.87, and the Cα correlation coefficient was 0.94. In the first round, for MRI-SR, all sections received more than a good rating. The overall mean score of the experts was 4.73. The Cα correlation coefficient was 0.82. In the second round, the overall mean score of the experts was 4.91, and the Cα correlation coefficient was 0.93. Conclusions: The CT-SR and MRI-SR are based on a multi-round consensus-building Delphi exercise derived from the multidisciplinary agreement of expert radiologists in order to obtain more appropriate communication tools for referring physicians.

Background: Structured reporting (SR) in radiology is becoming necessary and has recently been recognized by major scientific societies. This study aimed to build CT-based structured reports for lung cancer during the staging phase, in order to improve communication between radiologists, members of the multidisciplinary team and patients. Materials and Methods: A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi exercise was used to build the structural report and to assess the level of agreement for all the report sections. The Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to perform a quality analysis according to the average inter-item correlation. Results: The final SR version was built by including 16 items in the “Patient Clinical Data” section, 4 items in the “Clinical Evaluation” section, 8 items in the “Exam Technique” section, 22 items in the “Report” section, and 5 items in the “Conclusion” section. Overall, 55 items were included in the final version of the SR. The overall mean of the scores of the experts and the sum of scores for the structured report were 4.5 (range 1–5) and 631 (mean value 67.54, STD 7.53), respectively, in the first round. The items of the structured report with higher accordance in the first round were primary lesion features, lymph nodes, metastasis and conclusions. The overall mean of the scores of the experts and the sum of scores for staging in the structured report were 4.7 (range 4–5) and 807 (mean value 70.11, STD 4.81), respectively, in the second round. The Cronbach’s alpha (Cα) correlation coefficient was 0.89 in the first round and 0.92 in the second round for staging in the structured report. Conclusions: The wide implementation of SR is critical for providing referring physicians and patients with the best quality of service, and for providing researchers with the best quality of data in the context of the big data exploitation of the available clinical data. Implementation is complex, requiring mature technology to successfully address pending user-friendliness, organizational and interoperability challenges.

Thermal ablation is a minimally or noninvasive cancer therapy technique that involves fewer complications, shorter hospital stays, and fewer costs. In this paper, a thermal-ablation bioheat model for cancer treatment is numerically investigated, using a porous media-based model. The main objective is to evaluate the effects of a variable blood volume fraction in the tumoral tissue (i.e., the porosity), in order to develop a more realistic model. A modified local thermal nonequilibrium model (LTNE) is implemented including the water content vaporization in the two phases separately and introducing the variable porosity in the domain, described by a quadratic function changing from the core to the rim of the tumoral sphere. The equations are numerically solved employing the finite-element commercial code COMSOL Multiphysics. Results are compared with the results obtained employing two uniform porosity values (ε = 0.07 and ε = 0.23) in terms of coagulation zones at the end of the heating period, maximum temperatures reached in the domain, and temperature fields and they are presented for different blood vessels. The outcomes highlight how important is to predict coagulation zones achieved in thermal ablation accurately. In this way, indeed, incomplete ablation, tumor recurrence, or healthy tissue necrosis can be avoided, and medical protocols and devices can be improved.

Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection.

Prostate cancer is classified into different stages, each stage is related to a different Gleason score. The labeling of a diagnosed prostate cancer is a task usually performed by radiologists. In this paper we propose a deep architecture, based on several convolutional layers, aimed to automatically assign the Gleason score to Magnetic Resonance Imaging (MRI) under analysis. We exploit a set of 71 radiomic features belonging to five categories: First Order, Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix and Gray Level Size Zone Matrix. The radiomic features are gathered directly from segmented MRIs using two free-available dataset for research purpose obtained from different institutions. The results, obtained in terms of accuracy, are promising: they are ranging between 0.96 and 0.98 for Gleason score prediction.

Inflammatory bowel disease (IBD) is a form of chronic inflammation of the gastrointestinal tract, including two major entities: ulcerative colitis and Crohn’s disease. Although intestinal imaging of IBD is well known, imaging of extraintestinal manifestations is not extensively covered. In particular, the spectrum of IBD-associated or related changes in the chest is broad and may mimic other conditions. The common embryonic origin of intestine and lungs from the foregut, autoimmunity, smoking, and bacterial translocation from the colon may all be involved in the pathogenesis of these manifestations in IBD patients. Chest involvement in IBD can present concomitant with or years after the onset of the bowel disease even postcolectomy and can affect more than one thoracic structure. The purpose of the present paper is to present the different radiological spectrum of IBD-related chest manifestations, including lung parenchyma, airways, serosal surfaces, and pulmonary vasculature. The most prevalent and distinctive pattern of respiratory involvement is large airway inflammation, followed by lung alterations. Pulmonary manifestations are mainly detected by pulmonary function tests and high-resolution computed tomography (HRCT). It is desirable that radiologists know the various radiological patterns of possible respiratory involvement in such patients, especially at HRCT. It is essential for radiologists to work in multidisciplinary teams in order to establish the correct diagnosis and treatment, which rests on corticosteroids at variance with any other form of bronchiectasis.

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths worldwide. It has been estimated that more than one-third of patients are diagnosed when CRC has already spread to the lymph nodes. One out of five patients is diagnosed with metastatic CRC. The stage of diagnosis influences treatment outcome and survival. Notwithstanding the recent advances in multidisciplinary management and treatment of CRC, patients are still reluctant to undergo screening tests because of the associated invasiveness and discomfort (e.g., colonoscopy with biopsies). Moreover, the serological markers currently used for diagnosis are not reliable and, even if they were useful to detect disease recurrence after treatment, they are not always detected in patients with CRC (e.g., CEA). Recently, translational research in CRC has produced a wide spectrum of potential biomarkers that could be useful for diagnosis, treatment, and follow-up of these patients. The aim of this review is to provide an overview of the newer noninvasive or minimally invasive biomarkers of CRC. Here, we discuss imaging and biomolecular diagnostics ranging from their potential usefulness to obtain early and less-invasive diagnosis to their potential implementation in the development of a bespoke treatment of CRC.

PURPOSEWe aimed to assess the correlation between pulmonary hemorrhage and pneumothorax in computed tomography (CT)-guided transthoracic fine needle aspiration (TTFNA), particularly its possible value as protection against the development of pneumotorax.MATERIALS AND METHODSWe reviewed the CT images of 538 patients (364 males and 174 females, mean age 70 years, range 36–90 years) who underwent CT-guided TTFNA of pulmonary nodules between January 2008 and September 2013. The following CT findings were assessed: pulmonary hemorrhage (type 1, along the needle track; type 2, perilesional; low-grade, ≤6 mm; high-grade, >6 mm), pneumothorax, distance between the target nodule and the pleural surface, and emphysema.RESULTSPneumothorax occurred in 154 cases (28.6%) and pulmonary hemorrhage occurred in 144 cases (26.8%). The incidence of pneumothorax was lower in patients showing type 1 and high-grade pulmonary hemorrhage pattern. The incidence of pneumothorax in biopsies ≥30 mm from pleural surface was 26% (12/46) in cases showing this pattern, while it was 71.4% (30/42) when this pattern was not seen. Similarly, the incidence of pneumothorax in biopsies

Introduction. Functional disorders of the pelvic floor represent have a significant impact on the quality of life. The advent of open-configuration systems allowed for the evaluation of defecation with MR imaging in sitting position. The purpose of the present study is to compare the results of static and dynamic pelvic MR performed in supine position versus sitting position, using a new MR prototype machine, in the diagnosis of pelvic floor descent. Materials and Methods. Thirty-one patients with pelvic floor disorders were enrolled, and underwent MR Defecography in supine position with 1.5 T closed magnet (MAGNETOM Symphony, Siemens, Germany) and in sitting position with a 0.25-Tesla open magnet system (G-Scan ESAOTE, Italy). Results. In rest and squeezing phases, positions of bladder, vagina, and ARJ were significantly different when the patient was imaged in supine versus sitting position. In the defecation phase, a significant difference for the bladder and vagina position was detected between the two exams whereas a significant difference for the ARJ was not found. A statistically significant difference exists when the pelvic floor descent is evaluated in sitting versus supine position. Conclusion. Our results show that MR Defecography in sitting position may represent a useful tool to correctly diagnose and grade the pelvic organ descent.

Thyroid nodules, with their high prevalence in the general population, represent a diagnostic challenge for clinicians. Ultrasound (US), although absolutely reliable in detecting thyroid nodules, is still not accurate enough to differentiate them into benign and malignant. A promising novel modality, US elastography, has been introduced in order to further increase US accuracy. The purpose of this review article is to assess the thyroid application of US strain elastography, also known as real-time elastography or quasistatic elastography. We provide a presentation of the technique, and of up-to-date literature, analyzing the most prominent results reported for thyroid nodules differentiation. The practical advantages and limitations of strain elastography are extensively discussed herein.

Papillary thyroid cancer (PTC) is the most common histologic type of differentiated thyroid cancer. The first site of metastasis is the cervical lymph nodes (LNs). The ultrasonography (US) is the best diagnostic method for the detection of cervical metastatic LNs. We use a new technique, B-flow imaging (BFI), recently used for evaluation of thyroid nodules, to estimate the presence of BFI twinkling signs (BFI-TS), within metastatic LNs in patients with PTC. Two hundred and fifty-two patients with known PTC were examined for preoperative evaluation with conventional US and BFI. Only 83 with at least one metastatic LN were included. All patients included underwent surgery; the final diagnosis was based on the results of histology. The following LN characteristics were evaluated: shape, abnormal echogenicity, absent hilum, calcifications, cystic appearance, peripheral vascularization, and BFI-TS. A total of 604 LNs were analyzed. Of these, 298 were metastatic, according to histopathology. The BFI-TS showed high values ​​of specificity (99.7%) and sensitivity (80.9%). The combination of each conventional US sign with the BF-TS increases the specificity. Our findings suggest that BFI can be helpful in the selection of suspicious neck LNs that should be examined at cytologic examination for accurate preoperative staging and individual therapy selection.