Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative.

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR's usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics-Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item's explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/ . As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts.Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research.Key points• As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts.• Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3.• The resulting explanation and elaboration document with examples can be accessed at  https://radiomic.github.io/CLEAR-E3/ .

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII.

PURPOSE: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. METHODS: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. RESULT: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. CONCLUSION: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. CRITICAL RELEVANCE STATEMENT: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. KEY POINTS: • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score ( https://metricsscore.github.io/metrics/METRICS.html ) and a repository created to collect feedback from the radiomics community ( https://github.com/metricsscore/metrics ).

Reproducibility of radiomics quality score: an intra- and inter-rater reliability study.

OBJECTIVES: To investigate the intra- and inter-rater reliability of the total radiomics quality score (RQS) and the reproducibility of individual RQS items' score in a large multireader study. METHODS: Nine raters with different backgrounds were randomly assigned to three groups based on their proficiency with RQS utilization: Groups 1 and 2 represented the inter-rater reliability groups with or without prior training in RQS, respectively; group 3 represented the intra-rater reliability group. Thirty-three original research papers on radiomics were evaluated by raters of groups 1 and 2. Of the 33 papers, 17 were evaluated twice with an interval of 1 month by raters of group 3. Intraclass coefficient (ICC) for continuous variables, and Fleiss' and Cohen's kappa (k) statistics for categorical variables were used. RESULTS: The inter-rater reliability was poor to moderate for total RQS (ICC 0.30-055, p < 0.001) and very low to good for item's reproducibility (k - 0.12 to 0.75) within groups 1 and 2 for both inexperienced and experienced raters. The intra-rater reliability for total RQS was moderate for the less experienced rater (ICC 0.522, p = 0.009), whereas experienced raters showed excellent intra-rater reliability (ICC 0.91-0.99, p < 0.001) between the first and second read. Intra-rater reliability on RQS items' score reproducibility was higher and most of the items had moderate to good intra-rater reliability (k - 0.40 to 1). CONCLUSIONS: Reproducibility of the total RQS and the score of individual RQS items is low. There is a need for a robust and reproducible assessment method to assess the quality of radiomics research. CLINICAL RELEVANCE STATEMENT: There is a need for reproducible scoring systems to improve quality of radiomics research and consecutively close the translational gap between research and clinical implementation. KEY POINTS: • Radiomics quality score has been widely used for the evaluation of radiomics studies. • Although the intra-rater reliability was moderate to excellent, intra- and inter-rater reliability of total score and point-by-point scores were low with radiomics quality score. • A robust, easy-to-use scoring system is needed for the evaluation of radiomics research.

Cardiac Computed Tomography Evaluation of Association of Left Ventricle Disfunction and Epicardial Adipose Tissue Density in Patients with Low to Intermediate Cardiovascular Risk.

Background and objectives: Epicardial adipose tissue density (EAD) has been associated with coronary arteries calcium score, a higher load of coronary artery disease (CAD) and plaque vulnerability. This effect can be related to endocrine and paracrine effect of molecules produced by epicardial adipose tissue (EAT), that may influence myocardial contractility. Using coronary computed tomography angiography (CCT) the evaluation of EAD is possible in basal scans. The aim of the study is to investigate possible associations between EAD and cardiac function. Material and Methods: 93 consecutive patients undergoing CCT without and with contrast medium for known or suspected coronary CAD were evaluated. EAD was measured on basal scans, at the level of the coronary ostia, the lateral free wall of the left ventricle, at the level of the cardiac apex, and at the origin of the posterior interventricular artery. Cardiac function was evaluated in post-contrast CT scans in order to calculate ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV), and stroke volume (SV). Results: A statistically significant positive correlation between EAD and ejection fraction (r = 0.29, p-value < 0.01) was found. Additionally, a statistically significant negative correlation between EAD and ESV (r = -0.25, p-value < 0.01) was present. Conclusion: EAD could be considered a new risk factor associated with reduced cardiac function. The evaluation of this parameter with cardiac CT in patients with low to intermediate cardiovascular risk is possible.

CT Perfusion as a Predictor of the Final Infarct Volume in Patients with Tandem Occlusion.

BACKGROUND: CT perfusion (CTP) is used in patients with anterior circulation acute ischemic stroke (AIS) for predicting the final infarct volume (FIV). Tandem occlusion (TO), involving both intracranial large vessels and the ipsilateral cervical internal carotid artery could generate hemodynamic changes altering perfusion parameters. Our aim is to evaluate the accuracy of CTP in the prediction of the FIV in TOs. METHODS: consecutive patients with AIS due to middle cerebral artery occlusion, referred to a tertiary stroke center between March 2019 and January 2021, with an automated CTP and successful recanalization (mTICI = 2b - 3) after endovascular treatment were retrospectively included in the tandem group (TG) or in the control group (CG). Patients with parenchymal hematoma type 2, according to ECASS II classification of hemorrhagic transformations, were excluded in a secondary analysis. Demographic, clinical, radiological, time intervals, safety, and outcome measures were collected. RESULTS: among 319 patients analyzed, a comparison between the TG (N = 22) and CG (n = 37) revealed similar cerebral blood flow (CBF) > 30% (29.50 ± 32.33 vs. 15.76 ± 20.93 p = 0.18) and FIV (54.67 ± 65.73 vs. 55.14 ± 64.64 p = 0.875). Predicted ischemic core (PIC) and FIV correlated in both TG (tau = 0.761, p < 0.001) and CG (tau = 0.315, p = 0.029). The Bland-Altmann plot showed agreement between PIC and FIV for both groups, mainly in the secondary analysis. CONCLUSION: automated CTP could represent a good predictor of FIV in patients with AIS due to TO.

Can machine learning of post-procedural cone-beam CT images in acute ischemic stroke improve the detection of 24-h hemorrhagic transformation? A preliminary study.

PURPOSE: Hemorrhagic transformation (HT) is an independent predictor of unfavorable outcome in acute ischemic stroke (AIS) patients undergoing endovascular thrombectomy (EVT). Its early identification could help tailor AIS management. We hypothesize that machine learning (ML) applied to cone-beam computed tomography (CB-CT), immediately after EVT, improves performance in 24-h HT prediction. METHODS: We prospectively enrolled AIS patients undergoing EVT, post-procedural CB-CT, and 24-h non-contrast CT (NCCT). Three raters independently analyzed imaging at four anatomic levels qualitatively and quantitatively selecting a region of interest (ROI) < 5 mm2. Each ROI was labeled as "hemorrhagic" or "non-hemorrhagic" depending on 24-h NCCT. For each level of CB-CT, Mean Hounsfield Unit (HU), minimum HU, maximum HU, and signal- and contrast-to-noise ratios were calculated, and the differential HU-ROI value was compared between both hemispheres. The number of anatomic levels affected was computed for lesion volume estimation. ML with the best validation performance for 24-h HT prediction was selected. RESULTS: One hundred seventy-two ROIs from affected hemispheres of 43 patients were extracted. Ninety-two ROIs were classified as unremarkable, whereas 5 as parenchymal contrast staining, 29 as ischemia, 7 as subarachnoid hemorrhages, and 39 as HT. The Bernoulli Naïve Bayes was the best ML classifier with a good performance for 24-h HT prediction (sensitivity = 1.00; specificity = 0.75; accuracy = 0.82), though precision was 0.60. CONCLUSION: ML demonstrates high-sensitivity but low-accuracy 24-h HT prediction in AIS. The automated CB-CT imaging evaluation resizes sensitivity, specificity, and accuracy rates of visual interpretation reported in the literature so far. A standardized quantitative interpretation of CB-CT may be warranted to overcome the inter-operator variability.

Radiomics analysis of short tau inversion recovery images in cardiac magnetic resonance for the prediction of late gadolinium enhancement in patients with acute myocarditis.

RATIONALE AND OBJECTIVES: Cardiac Magnetic Resonance (CMR) imaging is recommended as the reference diagnostic non-invasive modality for myocarditis but is often limited by patients' compliance. The purpose of this study is to evaluate the validity of Radiomics applied to Short Tau Inversion Recovery (STIR) sequences, in predicting the presence of LGE in patients with suspected acute myocarditis. MATERIALS AND METHODS: 171 STIR images on short-axis view were segmented with "MaZda" software ver 4.6, by placing a region of interest (ROI) on the left ventricle by two radiologists in consensus. Images were classified according to the presence of LGE in the equivalent short-axis T1-IR slice. A total of 337 ROI features were extracted for each image. Dataset was then split into two parts (train and test set) with 70:30 ratio. RESULTS: Eleven classification models were trained. An Ensemble Machine Learning (EML) model was obtained by averaging the predictions of models with accuracy on test set >70%. The EML documented accuracy of 0.75, sensitivity of 0.8 and a specificity of 0.73 with a NPV of 0.81 and a PPV of 0.7, with AUC of 0.79 (95% CI: 0.66-0.92). CONCLUSION: Radiomics and machine learning analysis could be a promising approach in reducing scan times without reducing diagnostic accuracy in predicting LGE in patients with acute myocarditis.

Cardiac Computed Tomography Radiomics-Based Approach for the Detection of Left Ventricular Remodeling in Patients with Arterial Hypertension.

The aim of the study is to verify the feasibility of a radiomics based approach for the detection of LV remodeling in patients with arterial hypertension. Cardiac Computed Tomography (CCT) and clinical data of patients with and without history of arterial hypertension were collected. In one image per patient, on a 4-chamber view, left ventricle (LV) was segmented using a polygonal region of interest by two radiologists in consensus. A total of 377 radiomics features per region of interest were extracted. After dataset splitting (70:30 ratio), eleven classification models were tested for the discrimination of patients with and without arterial hypertension based on radiomics data. An Ensemble Machine Learning (EML) score was calculated from models with an accuracy >60%. Boruta algorithm was used to extract radiomic features discriminating between patients with and without history of hypertension. Pearson correlation coefficient was used to assess correlation between EML score and septum width in patients included in the test set. EML showed an accuracy, sensitivity and specificity of 0.7. Correlation between EML score and LV septum width was 0.53 (p-value < 0.0001). We considered LV septum width as a surrogate of myocardial remodeling in our population, and this is the reason why we can consider the EML score as a possible tool to evaluate myocardial remodeling. A CCT-based radiomic approach for the identification of LV remodeling is possible in patients with past medical history of arterial hypertension.

Relationship between septo-valvular angle and risk of pacemaker implantation after transcatheter aortic valve implantation: a preliminary study.

AIM: Pre-transcatheter aortic valve implantation (TAVI) computed tomography (CT) has proven to be crucial in identifying pre- and post-procedural predicting factors predisposing the onset of major arrhythmias that require permanent pacemaker (PPM) implantation caused by the compressive effects of the prostheses on the conduction system at the membranous septum (MS) and the muscular crest of the interventricular septum.Our analysis aims to verify if the pre-TAVI assessment of the angle between the MS and the aortic annulus (SVA) might be a predictive factor for the onset of arrhythmias that requires PPM. METHODS: Two cardiovascular specialist radiologists retrospectively and double-blind evaluated a randomized list of preprocedural CT of 57 patients who underwent TAVI with a self-expandable valve from April 2019 to February 2020. Two anatomical features were measured by readers: width of the SVA and MS length (MSL). RESULTS: A PPM was implanted in 18 patients (31%) after the procedure. There was no significant difference in the anatomical measurements performed between the two observers, regarding both anatomical measurements (intraclass correlation coefficient was 0.944 for the SVA and 0.774 for the MSL]. Receiver-operating characteristic curves (ROC) performed for both measurements have documented: for the SVA sensitivity 94% and Negative predictive value (NPV) 96% (area under the curve: 0.77; 95% confidence interval 0.66-0.90). The MSL ROC was not significant. The mean SVA value stratified for patients who did not undergo PPM implantation and patients who did resulted as significant (P < 0.005). CONCLUSION: Measurement of the SVA performed in preprocedural CT scans has proven to be related to the onset of major arrhythmias after TAVI requiring permanent pacemaker implantation with high sensitivity (94%) and NPV (96%).

The Aftermath of COVID-19 Lockdown on Daily Life Activities in Orthopaedic Patients.

PURPOSE: Following the 2019 coronavirus (COVID-19) pandemic, many reports have investigated the psychosocial effects of the lockdown. The aim of our study was to investigate the effects of the lockdown on the daily life habits of orthopaedic patients. PATIENTS AND METHODS: Around 30 days after the start of the Italian lockdown, from April 17th to the end of the containment measures on May 18th, the Sapienza University of Rome carried out a 19-item survey on the effects the lockdown caused in patients suffering from an orthopaedic pathology (with regard to changes in lifestyle, pain, treatment) by stratifying the sample by age, sex, profession, pain location, pathogenesis and prior surgeries. RESULTS: A total of 292 subjects with orthopaedic pathologies took part in the survey. Taking into consideration the totality of the sample, physical activity was significantly reduced (p <0.001). The increase of a sedentary lifestyle was statistically significant (p <0.001), as well as an increase in pain (p <0.001) and the need to adjust the pharmacological treatment (p <0.001). The increase in pain was particularly significant in the 50-70 age group, among workers, and in polyarthralgic patients. About 55.48% stated that the lockdown period worsened their underlying pathological condition and 53.77% expressed the need to undergo a new clinical evaluation at the end of the lockdown. CONCLUSION: The impact of the COVID-19 pandemic lockdown and resulting sudden changes in lifestyle led to a change in patient perception of orthopedic pathology including a worsening of musculoskeletal pain requiring an adjustment or change in pharmacological and physical therapy.

Effects on pain of percutaneous treatment of cervical disc herniations using DiscoGel: A retrospective analysis.

BACKGROUND AND PURPOSE: Cervical discogenic pain originates from degenerated intervertebral discs and is a common condition in the middle-aged population. Cervical discs may herniate and give compressions to cervical nerves, with pain and functional limitation of the arms. DiscoGel is a device that can be useful in the treatment of cervical disc herniation, with very short operating time and low radiation dose. MATERIAL AND METHODS: Between March 2018 and April 2019 we performed this procedure on 38 patients with non-fissurated cervical herniation using 0.3-0.4 mL of DiscoGel injected under fluoroscopic guidance. The most common discs affected were C5-C6, C6-C7 and C4-C5. Outcomes were evaluated with Visual Analogue Scale (VAS) and Neuropathic Pain Symptom Inventory (NPSI) scores at 3, 6 and 12 months follow-up. A magnetic resonance imaging (MRI) scan of the cervical spine was performed 3 months after the procedure. RESULTS: Postoperative examinations showed: VAS 2.15 ± 1.34 and NPSI 2.29 ± 0.71.Postoperative MRI performed 3 months after the procedure showed a good improvement of cervical disc herniation or bulging or protrusion. The mean dose area product (DAP) was 2803 mGy/cm2 with a mean fluoroscopy time of 4 minutes 22 seconds.Conclusion DiscoGel is a suitable approach for non-fissurated cervical disc herniations, especially in patients that are not suitable for open surgery, with excellent postoperative results, fast recovery and a low radiation dose.

Artificial Intelligence: the "Trait D'Union" in Different Analysis Approaches of Autism Spectrum Disorder Studies.

Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition affecting approximately 1 out of 70 (range 1:59 - 1:89) children worldwide. It is characterized by a delay in cognitive capabilities, repetitive and restricted behaviors and deficit in communication and social interaction. Several factors seem to be associated with ASD development; its heterogeneous nature makes the diagnosis difficult and slow since it is essentially based on screening tools focused on stereotypical and repetitive behaviors, gait, facial emotion expression and speech assessments. Recently, artificial intelligence (AI) has been widely used to investigate ASD with the overall goal of simplifying and speeding up the diagnostic process as well as making earlier access to therapies possible. The aim of this review is to provide an overview of the state-of-the-art research in the ASD field, identifying and describing machine learning (ML) approaches in ASD literature that could be used by clinicians to improve diagnostic capability and treatment efficiency. A systematic search was conducted and the resulting articles were subdivided into several categories reflecting the different fields of study associated with ASD research. The existing literature has widely demonstrated the potential of ML in several types of ASD study analyses: behavior, gait, speech, facial emotion expression, neuroimaging, genetics, and metabolomics. Therefore, AI techniques are becoming increasingly implemented and accepted, so highlighting the power of ML approaches to extract and obtain knowledge from a large volume of data. This makes ML a promising tool for future ASD research and clinical endeavors suggesting possible avenues for improving ASD screening, diagnostic and therapeutic tools.

Texture Analysis in the Evaluation of COVID-19 Pneumonia in Chest X-Ray Images: A Proof of Concept Study.

BACKGROUND: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in an early phase of the disease. Texture analysis might be an additional tool for the evaluation of Chest X-ray in patients with clinical suspicion of Covid-19 related pneumonia. OBJECTIVE: To evaluate the diagnostic performance of texture analysis and machine learning models for the diagnosis of Covid-19 interstitial pneumonia in Chest X-ray images. METHODS: Chest X-ray images were accessed from a publicly available repository(https://www.kaggle. com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented using a polygonal region of interest covering both lung areas, using MaZda, a freely available software for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 Chest X-ray images were selected for the final analysis. RESULTS: Six models, namely NB, GLM, DL, GBT, ANN, and PLS-DA were selected and ensembled. According to Youden's index, the Covid-19 Ensemble Machine Learning Score showing the highest area under the curve (0.971±0.015) was 132.57. Assuming this cut-off the Ensemble model performance was estimated by evaluating both true and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to -100, although the accuracy resulted lower (90.6%), the Ensemble Machine Learning showed 100% sensitivity, with 80% specificity. CONCLUSION: Texture analysis of Chest X-ray images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay the ground for future research works in this field and help to develop more rapid and accurate screening tools for these patients.

Radiological pitfalls associated with the diagnosis of usual interstitial pneumonia pattern on high-resolution computed tomography and associated findings: experience from a single Italian center.

BACKGROUND: The diagnostic algorithm for idiopathic pulmonary fibrosis (IPF) based on high-resolution computed tomography (HRCT) findings and multidisciplinary discussion (MDD) has been well established. PURPOSE: To identify the causes of disagreement between non-thoracic and thoracic radiologist involved in MDD for the imaging diagnosis of usual interstitial pneumonia (UIP) patterns and associated findings on HRCT and to improve the understanding of IPF by non-expert radiologists through a more systematic approach to HRCT. MATERIAL AND METHODS: This study included 68 patients who underwent MDD for suspected IPF. We compared the first reports generated before MDD by non-expert radiologists with the CT pattern and associated findings of IPF reported by thoracic radiologist involved in MDD. RESULTS: Regarding the diagnosis of CT pattern by non-expert radiologists, 30/68 patients received a discordant diagnosis, and in another 28 reports, all features of the CT pattern were described without reaching a diagnostic conclusion. The first report was concordant in only 10 patients. For 63 cases in which associated findings were reported by expert radiologists in MDD, we documented discrepancies in 47 cases where associated findings were considered absent by the first non-thoracic radiologist. CONCLUSION: We found significant discrepancies in the imaging diagnosis of UIP patterns and associated findings on HRCT between non-expert and thoracic radiologists included in MDD. Therefore, in this study, we analyzed and suggested diagnostic strategies to improve non-expert radiologists' approach to HRCT.

Trans-septal course of anomalous left main coronary artery originating from single right coronary ostium presenting with atrial fibrillation in a severely obese patient: a case report.

BACKGROUND: To present a case of anomalous origin of the left coronary artery evaluated with invasive coronary angiography (ICA) and ECG-gated coronary computed tomography (CCT). CASE PRESENTATION: A patient (55 years old, male) with a past medical history of respiratory failure and atrial fibrillation underwent ICA to rule out coronary artery disease. Subsequently, the patient underwent ECG-gated CCT to evaluate a suspected anomalous aortic origin of the left coronary artery, since the interventional cardiologist was not able to properly identify the left coronary artery and its distal branches. CCT showed left coronary artery originating from the right coronary Valsalva sinus, coursing within the interventricular septum and emerging at the middle segment of the interventricular sulcus, where the left anterior descending and circumflex arteries originated. CONCLUSION: The case we presented highlights the value of ECG-gated CCT in the evaluation of coronary anomaly anatomy and thus risk stratification derived by proper coronary anatomy assessment. Although ICA was not helpful in the diagnosis, it also has a pivotal role regarding the therapeutic management of this condition.

Association of plaque calcification pattern and attenuation with instability features and coronary stenosis and calcification grade.

BACKGROUND AND AIMS: Coronary computed tomography (CT) allows calculating coronary artery calcium score (CACS). However, other CT features might be more strongly related to plaque vulnerability and risk of future coronary events. This study investigated the association of plaque calcification pattern and attenuation with plaque instability features, coronary artery disease (CAD) grade and CACS. METHODS: One-hundred patients with coronary stenosis associated with calcified plaques were considered for this analysis. CACS, CAD grade, calcification pattern and attenuation, features of plaque instability, and epicardial adipose tissue (EAT) thickness and attenuation were assessed with non-contrast and contrast-enhanced CT angiography. RESULTS: Of 373 calcified plaques, 131 were responsible for the highest degree of coronary stenosis (1.31 ± 0.53 per patient). Participants were stratified according to the features of the highest-grade lesion(s) into patients with large (35%), spotty (52%) or mixed (13%) calcification pattern and tertiles of plaque calcification attenuation (using the mean value for multiple lesions). Patients with large calcification pattern or higher plaque calcification attenuation had higher stenosis and CACS grade (and EAT attenuation), but lower plaque instability score, whereas those with spotty calcification pattern or lower plaque calcification attenuation had lower stenosis and CACS grade (and EAT attenuation), but higher plaque instability score. Among the instability features, low attenuation and napkin-ring sign, but not positive remodeling, were associated with a spotty pattern and a lower calcification attenuation. CONCLUSIONS: Both the pattern and attenuation of calcification should be considered, in addition to CACS, for risk stratification of heavily calcified high-risk patients with non-critical coronary stenosis.

Is postoperative hyponatremia a real threat for total hip and knee arthroplasty surgery?

Postoperative hyponatremia (POH) is thought to be a fearsome complication of orthopedic surgery. Primary aim of this cohort study was to evaluate the incidence of POH and its clinical relevance in elective surgery, outlining differences between total knee arthroplasty (TKA) and total hip arthroplasty, looking for the presence of any risk factor commonly related to POH.Four hundred two patients that underwent total hip arthroplasty and total knee arthroplasty performed between 2016 and 2017 were retrospectively examined. Serum electrolytes, hemoglobin, hematocrit, glucose, and creatinine were evaluated preoperatively and at day 0-I-II from surgery. Age, sex, body mass index, comorbidities, drugs, surgery data, transfusions, postoperative symptoms, and length of stay (LOS) were determined. All surgeries were performed by the same equipe. Patients had the same perioperative management, excluded those that took thiazides, already at risk of POH.Patients were divided in 2 groups: group A, patients with normal postoperative natremia (294 patients) and group B, patients who developed POH (108, 26.9%); 66.7% of these developed POH within 24 hours postoperatively. In group B mean postoperative natremia was 133.38 (127.78-134.85) mmol/L. Two patients (1.8%) developed moderate hyponatremia, no severe hyponatremia was documented. Type of surgery, operation time, LOS, and presence of postoperative symptoms did not show statistically significant differences within groups. At multivariate logistic analysis chronic use of thiazides was the only variable associated to a decreased risk of developing POH (OR = 0.39; P = .03). Hemoglobin postoperative values (OR = 1.22; P = .03), the need of postoperative transfusion (OR = 2.50; P = .02) and diabetes (OR = 2.70; P = .01) were associated to an increased risk of POH.Although 26.9% of our patients exhibited POH, the onset of this disorder had no implication on postoperative symptoms and on LOS. Diabetes and transfusion are factors most often associated to POH.

Antibiotic-Loaded Hydrogel Coating to Reduce Early Postsurgical Infections in Aseptic Hip Revision Surgery: A Retrospective, Matched Case-Control Study.

Periprosthetic joint infections (PJIs) are a cause of frequent implant failure in revision hip replacement surgery. The purpose of this study is to evaluate the onset of early postoperative infections in patients who underwent hip surgery with cementless prostheses treated with an antibiotic loaded hydrogel on their surface, in addition to systemic prophylaxis, and compare them to a control group. The secondary objective was to evaluate the onset of any local and systemic adverse effects and interference with bone ingrowth processes and functional recovery. A retrospective observational study was conducted on patients who underwent revision hip surgery by performing a 1:1 match between patients treated with an antibiotic hydrogel (ALH) and the control patients. The incidence of PJIs was assessed with a minimum of six months follow-up. Seventeen patients treated with the ALH were compared with 17 patients from the control group. No PJIs were reported in the ALH group versus the six cases encountered in the control group (p < 0.0001). No significant differences were reported with regard to prosthetic osseointegration and functional results, nor were there side effects in the ALH group. Despite the low sample size, the use of on-site prophylaxis with ALH has proven effective and safe in reducing the risk of PJIs in patients with a high risk for infections. Further studies are needed to validate these results in other implant-related surgeries.

Association of segmental T2 ratio and late gadolinium enhancement in patients with acute myocarditis: a feasibility study.

BACKGROUND: Cardiac magnetic resonance is a valuable tool in the diagnosis of acute myocarditis, but dyspnea or chest pain often reduce patient's compliance, so definition of faster magnetic resonance protocols is of paramount importance. METHODS: Short tau inversion recovery (STIR) and phase sensitive inversion recovery images for the assessment of late gadolinium enhancement (LGE)of 22 patients with clinical suspicion of acute myocarditis were retrospectively evaluated. Signal intensity in STIR images was measured by 2 readers by placing region of interests (ROIs) within the area of maximal signal intensity in each myocardial segment derived from the ACC/AHA segmental scheme. Segmental T2 ratio was assessed with the formula: signal intensity of myocardium/signal intensity of muscle. Receiver operating characteristic (ROC) curves were used to compare diagnostic performance of T2 Signal intensity and T2 ratio in predicting the presence of LGE in each myocardial segment. Bland-Altman analysis was used to assess inter reader agreement. RESULTS: Signal intensity in STIR images showed an area under the curve (AUC) of 0.54 (95% CI: 0.44-0.63) for reader 1 and 0.53(95% CI: 0.44-0.63) for reader 2. Segmental T2 ratio showed an AUC of 0.8 (95% CI: 0.73-0.87) for reader 1 and 0.77 (95% CI: 0.71-0.84) for reader 2. Bland-Altman analysis showed good agreement for both T2 signal intensity (mean difference =-18.5 reader1 vs. reader 2 and 2SD=247.3) and T2 ratio (mean difference=0.03 vs. reader2 and 2SD=0.9). CONCLUSIONS: Segmental T2 ratio showed a good diagnostic accuracy in predicting the presence of LGE in patients with clinical suspicion of acute myocarditis and might be a promising approach in reducing scan times with no reduction in diagnostic accuracy.