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To evaluate the quality of the reports of loco-regional staging computed tomography (CT) or magnetic resonance imaging (MRI) in head and neck (HN) cancer.Consecutive reports of staging CT and MRI of all HN cancer cases from 2018 to 2020 were collected. We created lists of quality indicators for tumor (T) for each district and for node (N). We marked these as 0 or 1 in the report calculating a report score (RS) and a maximum sum (MS) of each list. Two radiologists and two otolaryngologists in consensus classified reports as low quality (LQ) if the RS fell in the percentage range 0-59% of MS and as high quality (HQ) if it fell in the range 60-100%, annotating technique and district. We evaluated the distribution of reports in these categories.Two hundred thirty-seven reports (97 CT and 140 MRI) of 95 oral cavity, 52 laryngeal, 47 oropharyngeal, 19 hypo-pharyngeal, 14 parotid, and 10 nasopharyngeal cancers were included. Sixty-six percent of all the reports were LQ for T, 66% out of all the MRI reports, and 65% out of all CT reports were LQ. Eight-five percent of reports were HQ for N, 85% out of all the MRI reports, and 82% out of all CT reports were HQ. Reports of oral cavity, oro-nasopharynx, and parotid were LQ, respectively, in 76%, 73%, 100% and 92 out of cases.Reports of staging CT/MRI in HN cancer were LQ for T description and HQ for N description.

Aims Mitral annulus disjunction (MAD) has been associated with sudden cardiac death in selected patients with arrhythmic presentation, while its clinical significance in unselected cohorts remains unknown. Our purpose was to assess the prevalence and clinical significance of MAD in consecutive patients referred to cardiovascular-magnetic-resonance (CMR). Methods and results Our population included 103 consecutive patients undergoing CMR at our Institution, between August and September 2021. MAD was defined as a ≥ 1 mm atrial displacement of the mitral leaflet hinge point in standard long-axis cine images during end-systole. MAD analysis was performed in 97 patients (feasibility = 94%) and resulted positive in 49 (51%). MAD—patients were more often males (75% vs. 57%; P = 0.045) and affected by ischaemic (35% vs. 12%, P = 0.01) and non-ischaemic cardiomyopathy (38% vs. 16%, P = 0.026) compared to MAD+ patients. No significant differences were found in terms of age, history of ventricular arrhythmias, bi-ventricular and bi-atrial volumes, bi-ventricular ejection fraction, native T1 and T2 mapping values, extracellular volume, and prevalence of late gadolinium enhancement (P > 0.05 for all) between MAD + vs. MAD—patients. MAD extent was higher in patients with mitral valve prolapse (MVP; n = 7), (3.5 ± 1.5 mm in MVP+ vs. 2.0 ± 1.0 mm in MVP– patients; P = 0.004). No significant differences were conversely found in MAD extent between patients with and without ventricular arrhythmias (2.5 ± 1.1 mm vs. 2.3 ± 1.1 mm; P = 0.815). Conclusions Our findings suggest a high prevalence of MAD in unselected cohorts of patients, with no clinical significance. Prospective studies are needed to further elucidate the interplay between MAD and malignant ventricular arrhythmias in unselected cohorts of patients.

The objective of this systematic review was to critically assess the available literature on deep learning (DL) and radiomics applied to the Liver Imaging Reporting and Data System (LI-RADS) in terms of 1) automatic LI-RADS classification of liver nodules; 2) the contribution of DL and radiomics to human evaluation in the classification of liver nodules following LI-RADS protocol.A literature search was conducted to identify original research studies published up to April 2021. The inclusion criteria were: English language, focus on computed tomography (CT) and/or magnetic resonance (MR) with specified number of patients and lesions, adoption of LI-RADS classification for the detected hepatic lesions, and application of AI in the classification of liver nodules. Review articles, conference papers, editorials and commentaries, animal studies or studies with absence of AI and/or LI-RADS were excluded. After screening 221 articles, 11 studies were included in this review.All the included studies proved that DL and radiomics have high performances in liver nodules classification, sometimes similar or better than human evaluation. The best performances of DL was an AUC of 0.95 on MR and the best performance of radiomics was AUC of 0.98 either on CT and MR, while the lower ones were respectively AUC of 0.63 either on CT and MR for DL and AUC of 0.70 on CT for radiomics.DL and radiomics could be a useful tool in assisting radiologists in the diagnosis and classification of liver nodules according to LI-RADS.

Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes mild-to-moderate symptoms in most individuals. However, rapid deterioration to severe disease with or without acute respiratory distress syndrome (ARDS) can occur within 1–2 weeks from the onset of symptoms in a proportion of patients. Early identification by risk stratifying such patients who are at risk of severe complications of COVID-19 is of great clinical importance. Computed tomography (CT) is widely available and offers the potential for fast triage, robust, rapid, and minimally invasive diagnosis: Ground glass opacities (GGO), crazy-paving pattern (GGO with superimposed septal thickening), and consolidation are the most common chest CT findings in COVID pneumonia. There is growing interest in the prognostic value of baseline chest CT since an early risk stratification of patients with COVID-19 would allow for better resource allocation and could help improve outcomes. Recent studies have demonstrated the utility of baseline chest CT to predict intensive care unit (ICU) admission in patients with COVID-19. Furthermore, developments and progress integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging allow for objective, unbiased, and rapid assessment of CT images.

Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the “most infected volume” composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.

BackgroundAlthough there are reports of otolaryngological symptoms and manifestations of CoronaVirus Disease 19 (COVID-19), there have been no documented cases of sudden neck swelling with rash in patients with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection described in literature.Case presentationWe report a case of a sudden neck swelling and rash likely due to late SARS-CoV-2 in a 64-year-old woman. The patient reported COVID-19 symptoms over the previous three weeks. Computed Tomography (CT) revealed a diffuse soft-tissue swelling and edema of subcutaneous tissue, hypodermis, and muscular and deep fascial planes. All the differential diagnoses were ruled out. Both the anamnestic history of the patient’s husband who had died of COVID-19 with and the collateral findings of pneumonia and esophageal wall edema suggested the association with COVID-19. This was confirmed by nasopharyngeal swab polymerase chain reaction. The patient was treated with lopinavir/ritonavir, hydroxychloroquine and piperacillin/tazobactam for 7 days. The neck swelling resolved in less than 24 h, while the erythema was still present up to two days later. The patient was discharged after seven days in good clinical condition and with a negative swab.ConclusionSudden neck swelling with rash may be a coincidental presentation, but, in the pandemic context, it is most likely a direct or indirect complication of COVID-19.