Ankle synovial chondromatosis: Clinical, radiological, and surgical findings: A case report.

Synovial chondromatosis is a rare benign condition defined by the presence of cartilaginous lesions in the synovium of joints, tendon sheaths, and bursae. It most typically affects large joints, such as the knee, hip, and shoulder, but it is also reported in smaller joints. Nonetheless, ankle involvement is relatively uncommon. A complete history and clinical, physical, and radiographic examinations are usually used to determine the diagnosis. Hence, we reported a case of a young patient with left ankle primary synovial chondromatosis who presented with a left ankle mass and chronic pain.

Recurrent primary intracranial synovial sarcoma, a case report and review of the literature.

Synovial sarcoma (SS) occurs in various parts of the body, predominantly in the extremities. It also occurs in organs without synovial structures. The intracranial disease has been reported as metastasis, but primary intracranial SS has been reported rarely. We report a patient with hemiplegia and a mass on the brain CT. Pathology showed SS with no extracranial pathology.

Biomechanical Properties of Heel Pad, Metatarsal Head Soft-Tissue and Foot Ulcers in Patients with Systemic Sclerosis: A Case Control Study.

Background: Systemic sclerosis is a chronic disease of connective tissue accompanied by fibrosis of the skin and inner organs and an increased risk of foot ulcers. Biomechanical indices such as soft-tissue thickness and compressibility may correlate with the risk of this phenomenon. Objective: The aim of this study was to assess heel pad and first metatarsal head (MTH) soft-tissue thickness and compressibility index (CI) in scleroderma patients compared to matched healthy individuals. Not all patients had foot ulcers. Methods: Heel pad thickness in standing (loaded) and lying (unloaded) positions were measured in 40 scleroderma patients by means of a lateral foot radiograph. CI was measured as the ratio of loaded to unloaded thickness. The Soft-tissue thickness of the first MTH was measured by ultrasound. Results were compared with 40 healthy controls of matched age and body mass index. All patients' diagnoses were made based on the American College of Rheumatology classification criteria. Results: Forty scleroderma patients (36 females, 4 males) with the following demographics were studied; mean age (SD) 45(12), mean body mass index 25.5 (4), and mean disease duration=10(9.6) years; only 8 (20%) had digital ulcers. Patients' heel pad thickness and CI in the dominant side and MTH soft-tissue thickness on both sides were significantly different compared to the control group. Comparison of results in patients with and without foot ulcers also showed a significant difference in soft-tissue thickness. Thickness was negatively associated with disease duration, but the CI did not change over time. Conclusion: Soft-tissue thickness of the foot decreases in scleroderma patients and is associated with foot ulcers and digital ulcers in the hands.

Investigating centering, scan length, and arm position impact on radiation dose across 4 countries from 4 continents during pandemic: Mitigating key radioprotection issues.

PURPOSE: Optimization of CT scan practices can help achieve and maintain optimal radiation protection. The aim was to assess centering, scan length, and positioning of patients undergoing chest CT for suspected or known COVID-19 pneumonia and to investigate their effect on associated radiation doses. METHODS: With respective approvals from institutional review boards, we compiled CT imaging and radiation dose data from four hospitals belonging to four countries (Brazil, Iran, Italy, and USA) on 400 adult patients who underwent chest CT for suspected or known COVID-19 pneumonia between April 2020 and August 2020. We recorded patient demographics and volume CT dose index (CTDIvol) and dose length product (DLP). From thin-section CT images of each patient, we estimated the scan length and recorded the first and last vertebral bodies at the scan start and end locations. Patient mis-centering and arm position were recorded. Data were analyzed with analysis of variance (ANOVA). RESULTS: The extent and frequency of patient mis-centering did not differ across the four CT facilities (>0.09). The frequency of patients scanned with arms by their side (11-40% relative to those with arms up) had greater mis-centering and higher CTDIvol and DLP at 2/4 facilities (p = 0.027-0.05). Despite lack of variations in effective diameters (p = 0.14), there were significantly variations in scan lengths, CTDIvol and DLP across the four facilities (p < 0.001). CONCLUSIONS: Mis-centering, over-scanning, and arms by the side are frequent issues with use of chest CT in COVID-19 pneumonia and are associated with higher radiation doses.

Association between ratio for diameters of pulmonary artery to ascending aorta bifurcation in chest CT scan and number of involved vessels in coronary angiography.

OBJECTIVE: Coronary artery disease (CAD) is an important cause of mortality and morbidity, therefore, recognizing its severity and related factors is important. This study was performed to evaluate the association between ratio for diameters of pulmonary artery to ascending aorta bifurcation in chest CT scan and number of involved vessels in coronary angiography. In this observational cross-sectional comparative study, 110 patients who were under coronary angiography in Firoozgar Hospital in 2017 were enrolled, and the association between ratio for diameters of pulmonary artery to ascending aorta bifurcation in their chest CT scan and number of involved vessels in angiography were assessed. RESULTS: In this study, number of involved vessels in angiography was related to PA/Ao ratio (P = 0.001) and further vessels were accompanied with higher ratio. It may be concluded that, a higher ratio for diameters of pulmonary artery to ascending aorta bifurcation in chest CT scan is related to higher number of involved vessels in coronary angiography, and it may have a predictive role.

Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome.

To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 105 females; 136 males) with RT-PCR-positive COVID-19 pneumonia who underwent non-contrast chest CT at one of the two tertiary care hospitals (site A: Massachusetts General Hospital, USA; site B: Firoozgar Hospital Iran). We recorded patient age, gender, comorbid conditions, laboratory values, intensive care unit (ICU) admission, mechanical ventilation, and final outcome (recovery or death). Two thoracic radiologists reviewed all chest CTs to record type, extent of pulmonary opacities based on the percentage of lobe involved, and severity of respiratory motion artifacts. Thin-section CT images were processed with the prototype (Siemens Healthineers) to obtain quantitative features including lung volumes, volume and percentage of all-type and high-attenuation opacities (≥ -200 HU), and mean HU and standard deviation of opacities within a given lung region. These values are estimated for the total combined lung volume, and separately for each lung and each lung lobe. Multivariable analyses of variance (MANOVA) and multiple logistic regression were performed for data analyses. About 26% of chest CTs (62/241) had moderate to severe motion artifacts. There were no significant differences in the AUCs of quantitative features for predicting disease severity with and without motion artifacts (AUC 0.94-0.97) as well as for predicting patient outcome (AUC 0.7-0.77) (p > 0.5). Combination of the volume of all-attenuation opacities and the percentage of high-attenuation opacities (AUC 0.76-0.82, 95% confidence interval (CI) 0.73-0.82) had higher AUC for predicting ICU admission than the subjective severity scores (AUC 0.69-0.77, 95% CI 0.69-0.81). Despite a high frequency of motion artifacts, quantitative features of pulmonary opacities from chest CT can help differentiate patients with favorable and adverse outcomes.

Serum level of Vitamin D is associated with COVID-19 mortality rate in hospitalized patients.

BACKGROUND: Due to widespread of coronavirus disease 2019 (COVID-19) infection, identification of its risk factors and clinical characteristics are important. The aim of the present study was to assess Vitamin D levels in individuals with severe acute respiratory syndrome coronavirus-19 infection and to report on its potential as a predictive marker. MATERIALS AND METHODS: All patients, diagnosed with COVID-19 infection from February 16 to March 21, 2020, and referred to Firoozgar Hospital, Tehran, Iran, were enrolled in this study. Vitamin D analysis was undertaken on patient serum samples using a commercial kit (Pars Azmoon Co., Tehran, Iran). SPSS v. 22 was used for statistical analysis. RESULTS: Vitamin D serum concentration was analyzed in a total of 317 patients whose mean age ± standard deviation was 62.05 ± 15 years and with 62.5% being male. A significant association of Vitamin D level and death was observed. Higher levels of serum Vitamin D had protection against death (odds ratio = 0.955 [95% confidence interval = 0.923-0.988], P = 0.008). CONCLUSION: As a preliminary study in the Iranian population who suffered COVID-19 disease, we identified that Vitamin D deficiency was associated with a higher death rate and intensive care unit admission.

Integrative analysis for COVID-19 patient outcome prediction.

While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia. The source code of our work is available at https://github.com/DIAL-RPI/COVID19-ICUPrediction.

Variations in CT Utilization, Protocols, and Radiation Doses in COVID-19 Pneumonia: Results from 28 Countries in the IAEA Study.

Background There is lack of guidance on specific CT protocols for imaging patients with coronavirus disease 2019 (COVID-19) pneumonia. Purpose To assess international variations in CT utilization, protocols, and radiation doses in patients with COVID-19 pneumonia. Materials and Methods In this retrospective data collection study, the International Atomic Energy Agency coordinated a survey between May and July 2020 regarding CT utilization, protocols, and radiation doses from 62 health care sites in 34 countries across five continents for CT examinations performed in patients with COVID-19 pneumonia. The questionnaire obtained information on local prevalence, method of diagnosis, most frequent imaging, indications for CT, and specific policies on use of CT in COVID-19 pneumonia. Collected data included general information (patient age, weight, clinical indication), CT equipment (CT make and model, year of installation, number of detector rows), scan protocols (body region, scan phases, tube current and potential), and radiation dose descriptors (CT dose index and dose length product). Descriptive statistics and generalized estimating equations were performed. Results Data from 782 patients (median age, 59 years [interquartile range, 15 years]) from 54 health care sites in 28 countries were evaluated. Less than one-half of the health care sites used CT for initial diagnosis of COVID-19 pneumonia and three-fourths used CT for assessing disease severity. CT dose index varied based on CT vendors (7-11 mGy; P < .001), number of detector rows (8-9 mGy; P < .001), year of CT installation (7-10 mGy; P = .006), and reconstruction techniques (7-10 mGy; P = .03). Multiphase chest CT examinations performed at 20% of sites (11 of 54) were associated with higher dose length product compared with single-phase chest CT examinations performed in 80% of sites (43 of 54) (P = .008). Conclusion CT use, scan protocols, and radiation doses in patients with coronavirus disease 2019 pneumonia showed wide variation across health care sites within the same and between different countries. Many patients were imaged multiple times and/or with multiphase CT scan protocols. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Lee in this issue.

Association of Musculoskeletal and Radiological Features with Clinical and Serological Findings in Systemic Sclerosis: A Single-Centre Registry Study.

AIM: Systemic sclerosis (SSc) is a chronic connective tissue disease with the clinical hallmark of skin thickening and tethering. Correlation of musculoskeletal features with other parameters should be considered in SSc patients. METHODS: We reviewed the records of all patients who had more than one visit and standard anteroposterior radiography of hand. We used univariate analysis, and factors with p<0.05 were included in logistic regression to find out dependent factors. RESULTS: Overall, 180 SSc patients were enrolled in our study, 161 (89.4%) of whom were women. Median age (IQR) was 47.0 years (16), and 52% had diffuse subtype of the disease. In multivariate analysis, tendon friction rubs (TFRs) was associated with the presence of calcinosis, muscle tenderness, and flexion contracture (FC) on physical examination (p<0.05). Arthritis showed no differences in the two subtypes of the disease (p=0.98), and in multivariate analysis, there were no correlations between radiographic arthritis and serological and clinical features. The radiographic results indicated that disease duration correlated with joint erosion, acro-osteolysis, resorption of distal ulna, calcinosis and radiologic FC (p< 0.05). Acro-osteolysis was more frequent in the dcSSc subtype, TFRs, and anti-TOPO I antibody. Radiologic FC showed association with skin score, calcinosis and haematocrit <30% (p<0.05). Joint flexion on radiography was associated with disease duration, modified Rodnan skin score, calcinosis, and low haematocrit (P<0.01). CONCLUSION: Disease duration was a main dependent factor for developing joint erosion, acro-osteolysis, bone resorption, calcinosis, and flexion contracture on hand radiography. Acro-osteolysis presented in the severe form of the disease. Acro-osteolysis was the only dependent variable associated with bone demineralization.

Clinical and imaging features predict mortality in COVID-19 infection in Iran.

The new coronavirus disease 2019 (COVID-19) pandemic has challenged many healthcare systems around the world. While most of the current understanding of the clinical features of COVID-19 is derived from Chinese studies, there is a relative paucity of reports from the remaining global health community. In this study, we analyze the clinical and radiologic factors that correlate with mortality odds in COVID-19 positive patients from a tertiary care center in Tehran, Iran. A retrospective cohort study of 90 patients with reverse transcriptase-polymerase chain reaction (RT-PCR) positive COVID-19 infection was conducted, analyzing demographics, co-morbidities, presenting symptoms, vital signs, laboratory values, chest radiograph findings, and chest CT features based on mortality. Chest radiograph was assessed using the Radiographic Assessment of Lung Edema (RALE) scoring system. Chest CTs were assessed according to the opacification pattern, distribution, and standardized severity score. Initial and follow-up Chest CTs were compared if available. Multiple logistic regression was used to generate a prediction model for mortality. The 90 patients included 59 men and 31 women (59.4 ± 16.6 years), including 21 deceased and 69 surviving patients. Among clinical features, advanced age (p = 0.02), low oxygenation saturation (p<0.001), leukocytosis (p = 0.02), low lymphocyte fraction (p = 0.03), and low platelet count (p = 0.048) were associated with increased mortality. High RALE score on initial chest radiograph (p = 0.002), presence of pleural effusions on initial CT chest (p = 0.005), development of pleural effusions on follow-up CT chest (p = 0.04), and worsening lung severity score on follow-up CT Chest (p = 0.03) were associated with mortality. A two-factor logistic model using patient age and oxygen saturation was created, which demonstrates 89% accuracy and area under the ROC curve of 0.86 (p<0.0001). Specific demographic, clinical, and imaging features are associated with increased mortality in COVID-19 infections. Attention to these features can help optimize patient management.

Computed Tomography Radiomics Can Predict Disease Severity and Outcome in Coronavirus Disease 2019 Pneumonia.

PURPOSE: This study aimed to assess if computed tomography (CT) radiomics can predict the severity and outcome of patients with coronavirus disease 2019 (COVID-19) pneumonia. METHODS: This institutional ethical board-approved study included 92 patients (mean age, 59 ± 17 years; 57 men, 35 women) with positive reverse transcription polymerase chain reaction assay for COVID-19 infection who underwent noncontrast chest CT. Two radiologists evaluated all chest CT examinations and recorded opacity type, distribution, and extent of lobar involvement. Information on symptom duration before hospital admission, the period of hospital admission, presence of comorbid conditions, laboratory data, and outcomes (recovery or death) was obtained from the medical records. The entire lung volume was segmented on thin-section Digital Imaging and Communication in Medicine images to derive whole-lung radiomics. Data were analyzed using multiple logistic regression with receiver operator characteristic area under the curve (AUC) as the output. RESULTS: Computed tomography radiomics (AUC, 0.99) outperformed clinical variables (AUC, 0.89) for prediction of the extent of pulmonary opacities related to COVID-19 pneumonia. Type of pulmonary opacities could be predicted with CT radiomics (AUC, 0.77) but not with clinical or laboratory data (AUC, <0.56; P > 0.05). Prediction of patient outcome with radiomics (AUC, 0.85) improved to an AUC of 0.90 with the addition of clinical variables (patient age and duration of presenting symptoms before admission). Among clinical variables, the combination of peripheral capillary oxygen saturation on hospital admission, duration of symptoms, platelet counts, and patient age provided an AUC of 0.81 for predicting patient outcomes. CONCLUSIONS: Radiomics from noncontrast CT reliably predict disease severity (AUC, 0.99) and outcome (AUC, 0.85) in patients with COVID-19 pneumonia.

Integrative Analysis for COVID-19 Patient Outcome Prediction.

While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia. The source code of our work is available at https://github.com/DIAL-RPI/COVID19-ICUPrediction.

Left renal Ewing's sarcoma: A case study and a review of imaging literature.

Extra-skeletal Ewing's sarcoma is among the rarest tumors in adults. The primary sites of the tumor dictates symptoms and signs, thus early treatments are compromised when more common tumors are lined up as differentials by the location. We present a case of a 35-year-old pregnant female who developed a renal Ewing sarcoma during pregnancy. A prior simple left kidney cyst in an ultrasound with no tumor signs was spotted. A month after her cesarean section she visited a doctor when she was sent and admitted for surgery with renal cell carcinoma as the primary diagnosis to the Firoozgar hospital. Histology confirmed the final diagnosis. To this end, she completed the standard chemotherapy for the renal Ewing sarcoma with pulmonary metastasis when she was re-evaluated for the general bone pain, diagnosed with multiple bone metastases, and ultimately approached her palliative care. She expired after 2 months. This study demonstrates: a gently progressive mass; palpable in late stages; introduced rise in mean corpuscular volume and lactate dehydrogenase with no drop in the hematocrit. In conclusion, any random parenchymal and/or cortical thickening in primary ultrasound and/or computed tomography demonstrating a cyst-whether displaying internal echo or not- with suggested signs should be furtherly evaluated.

CT Radiomics, Radiologists, and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia.

PURPOSE: To compare prediction of disease outcome, severity, and patient triage in coronavirus disease 2019 (COVID-19) pneumonia with whole lung radiomics, radiologists' interpretation, and clinical variables. MATERIALS AND METHODS: This institutional review board-approved retrospective study included 315 adult patients (mean age, 56 years [range, 21-100 years], 190 men, 125 women) with COVID-19 pneumonia who underwent noncontrast chest CT. All patients (inpatients, n = 210; outpatients, n = 105) were followed-up for at least 2 weeks to record disease outcome. Clinical variables, such as presenting symptoms, laboratory data, peripheral oxygen saturation, and comorbid diseases, were recorded. Two radiologists assessed each CT in consensus and graded the extent of pulmonary involvement (by percentage of involved lobe) and type of opacities within each lobe. Radiomics were obtained for the entire lung, and multiple logistic regression analyses with areas under the curve (AUCs) as outputs were performed. RESULTS: Most patients (276/315, 88%) recovered from COVID-19 pneumonia; 36/315 patients (11%) died, and 3/315 patients (1%) remained admitted in the hospital. Radiomics differentiated chest CT in outpatient versus inpatient with an AUC of 0.84 (P < .005), while radiologists' interpretations of disease extent and opacity type had an AUC of 0.69 (P < .0001). Whole lung radiomics were superior to the radiologists' interpretation for predicting patient outcome in terms of intensive care unit (ICU) admission (AUC: 0.75 vs 0.68) and death (AUC: 0.81 vs 0.68) (P < .002). The addition of clinical variables to radiomics improved the AUC to 0.84 for predicting ICU admission. CONCLUSION: Radiomics from noncontrast chest CT were superior to radiologists' assessment of extent and type of pulmonary opacities in predicting COVID-19 pneumonia outcome, disease severity, and patient triage.© RSNA, 2020.

Ovarian Malignant Mixed Germ Cell Tumor: A Case of Unusual Presentation as Molar Pregnancy.

BACKGROUND: This research was conducted to introduce a patient with rare ovarian mixed germ cell tumor, presented as molar pregnancy. CASE PRESENTATION: The patient was a 16 year old woman admitted with diagnosis of molar pregnancy. Abdominal enlargement was the only complaint. She had a large pelvic mass in physical examination. The first diagnosis was molar pregnancy due to previous ultrasonic reports and positive ßeta HCG. Urine pregnancy test was positive. As suction curettage was performed for her, surprisingly, the size of uterus was normal and no molar tissue was found in pathologic examination. At intraoperative ultrasound exam, an extra-uterine heterogeneous mass was found. Extra-uterine mass was confirmed by CT and MRI done after suction curettage. Mixed germ cell tumor was confirmed by histological examination after laparatomy and removing tumoral mass. Finally, she received Bleomycin, Etoposide and Cisplatin (BEP) regimen in four courses and Vincristine, Actinomycin D (Dactinomycin) and Cyclophosphamide (VAC) regimen in two courses and Diphereline for saving the other ovary. CONCLUSION: Some young patients misinterpret the early symptoms of an ovarian neoplasm as those of pregnancy which can lead to a delay in the diagnosis.