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A better understanding of temporal relationships between chest CT and labs may provide a reference for disease severity over the disease course. Generalized curves of lung opacity volume and density over time can be used as standardized references from well before symptoms develop to over a month after recovery, when residual lung opacities remain. 739 patients with COVID-19 underwent CT and RT-PCR in an outbreak setting between January 21st and April 12th, 2020. 29 of 739 patients had serial exams (121 CTs and 279 laboratory measurements) over 50 ± 16 days, with an average of 4.2 sequential CTs each. Sequential volumes of total lung, overall opacity and opacity subtypes (ground glass opacity [GGO] and consolidation) were extracted using deep learning and manual segmentation. Generalized temporal curves of CT and laboratory measurements were correlated. Lung opacities appeared 3.4 ± 2.2 days prior to symptom onset. Opacity peaked 1 day after symptom onset. GGO onset was earlier and resolved later than consolidation. Lactate dehydrogenase, and C-reactive protein peaked earlier than procalcitonin and leukopenia. The temporal relationships of quantitative CT features and clinical labs have distinctive patterns and peaks in relation to symptom onset, which may inform early clinical course in patients with mild COVID-19 pneumonia, or may shed light upon chronic lung effects or mechanisms of medical countermeasures in clinical trials.

To compare needle placement performance using an augmented reality (AR) navigation platform implemented on smartphone or smartglasses devices to that of CBCT-guided fluoroscopy in a phantom. An AR application was developed to display a planned percutaneous needle trajectory on the smartphone (iPhone7) and smartglasses (HoloLens1) devices in real time. Two AR-guided needle placement systems and CBCT-guided fluoroscopy with navigation software (XperGuide, Philips) were compared using an anthropomorphic phantom (CIRS, Norfolk, VA). Six interventional radiologists each performed 18 independent needle placements using smartphone (n = 6), smartglasses (n = 6), and XperGuide (n = 6) guidance. Placement error was defined as the distance from the needle tip to the target center. Placement time was recorded. For XperGuide, dose-area product (DAP, mGy*cm2) and fluoroscopy time (sec) were recorded. Statistical comparisons were made using a two-way repeated measures ANOVA. The placement error using the smartphone, smartglasses, or XperGuide was similar (3.98 ± 1.68 mm, 5.18 ± 3.84 mm, 4.13 ± 2.38 mm, respectively, p = 0.11). Compared to CBCT-guided fluoroscopy, the smartphone and smartglasses reduced placement time by 38% (p = 0.02) and 55% (p = 0.001), respectively. The DAP for insertion using XperGuide was 3086 ± 2920 mGy*cm2, and no intra-procedural radiation was required for augmented reality. Smartphone- and smartglasses-based augmented reality reduced needle placement time and radiation exposure while maintaining placement accuracy compared to a clinically validated needle navigation platform.

Objectives The early infection dynamics of patients with SARS-CoV-2 are not well understood. We aimed to investigate and characterize associations between clinical, laboratory, and imaging features of asymptomatic and pre-symptomatic patients with SARS-CoV-2. Methods Seventy-four patients with RT-PCR-proven SARS-CoV-2 infection were asymptomatic at presentation. All were retrospectively identified from 825 patients with chest CT scans and positive RT-PCR following exposure or travel risks in outbreak settings in Japan and China. CTs were obtained for every patient within a day of admission and were reviewed for infiltrate subtypes and percent with assistance from a deep learning tool. Correlations of clinical, laboratory, and imaging features were analyzed and comparisons were performed using univariate and multivariate logistic regression. Results Forty-eight of 74 (65%) initially asymptomatic patients had CT infiltrates that pre-dated symptom onset by 3.8 days. The most common CT infiltrates were ground glass opacities (45/48; 94%) and consolidation (22/48; 46%). Patient body temperature (p < 0.01), CRP (p < 0.01), and KL-6 (p = 0.02) were associated with the presence of CT infiltrates. Infiltrate volume (p = 0.01), percent lung involvement (p = 0.01), and consolidation (p = 0.043) were associated with subsequent development of symptoms. Conclusions COVID-19 CT infiltrates pre-dated symptoms in two-thirds of patients. Body temperature elevation and laboratory evaluations may identify asymptomatic patients with SARS-CoV-2 CT infiltrates at presentation, and the characteristics of CT infiltrates could help identify asymptomatic SARS-CoV-2 patients who subsequently develop symptoms. The role of chest CT in COVID-19 may be illuminated by a better understanding of CT infiltrates in patients with early disease or SARS-CoV-2 exposure. Key Points • Forty-eight of 74 (65%) pre-selected asymptomatic patients with SARS-CoV-2 had abnormal chest CT findings. • CT infiltrates pre-dated symptom onset by 3.8 days (range 1–5). • KL-6, CRP, and elevated body temperature identified patients with CT infiltrates. Higher infiltrate volume, percent lung involvement, and pulmonary consolidation identified patients who developed symptoms.

Image-guided robotics for biopsy and ablation aims to minimize procedure times, reduce needle manipulations, radiation, and complications, and enable treatment of larger and more complex tumors, while facilitating standardization for more uniform and improved outcomes. Robotic navigation of needles enables standardized and uniform procedures which enhance reproducibility via real-time precision feedback, while avoiding radiation exposure to the operator. Robots can be integrated with computed tomography (CT), cone beam CT, magnetic resonance imaging, and ultrasound and through various techniques, including stereotaxy, table-mounted, floor-mounted, and patient-mounted robots. The history, challenges, solutions, and questions facing the field of interventional radiology (IR) and interventional oncology are reviewed, to enable responsible clinical adoption and value definition via ergonomics, workflows, business models, and outcome data. IR-integrated robotics is ready for broader adoption. The robots are coming!

To characterize the hepatic and abdominal angiographic anatomy of woodchucks and vascular changes associated with hepatocellular carcinoma (HCC).Twenty-nine woodchucks (23 with viral-associated HCC, 6 without) underwent multiphasic computed tomography (CT). Fourteen woodchucks (8 with HCC) also underwent diagnostic angiography. Hepatic arterial diameters were measured on the CT scans. Woodchucks were divided into 3 groups: non-tumor-bearing, largest tumor supplied by the right hepatic artery (RHA), and largest tumor supplied by the left hepatic artery (LHA). Statistical analysis with a repeated measures model was performed to determine the effects of tumor location (right, left), vessel measured (RHA, LHA), and interaction between the 2 on vessel diameter. Lobar arteries supplying HCC were compared with those that did not.CT anatomy and normal and variant vascular anatomy were defined. In woodchucks with HCC, LHA and RHA supplying tumors had mean diameters of 2.0 mm ± 0.3 and 1.6 mm ± 0.3 versus 1.5 mm ± 0.3 and 1.1 mm ± 0.2 for non-tumor-supplying arteries (P = .0002 and P.0001), respectively. Lobar arteries supplying tumors were similarly ectatic. The right lateral lobe artery had the most profound increase in the mean diameter when supplying tumors, measuring 1.7 mm ± 0.1 versus 1.0 mm ± 0.1 in the non-tumor-supplying artery (P.0001). There were no differences in the diameters of the aorta and celiac, common, and proper hepatic arteries between tumor- and non-tumor-bearing woodchucks. An angiographic atlas of the abdominal vessels was generated.HCC tumoral vasculature in woodchucks was ectatic compared with normal vasculature. This phenomenon recapitulates human HCC and may facilitate investigation of transcatheter and drug delivery therapies in an HCC animal model.

Purpose Chest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak. Methods A retrospective study of X-rays, clinical, and laboratory data was performed from 48 SARS-CoV-2 RT-PCR positive patients (age 60±17 years, 15 women) between February 22 and March 6, 2020 from a tertiary care hospital in Milan, Italy. Sixty-five chest X-rays were reviewed by two radiologists for alveolar and interstitial opacities and classified by severity on a scale from 0 to 3. Clinical factors (age, symptoms, comorbidities) were investigated for association with opacity severity and also with placement of central line or endotracheal tube. Deep learning models were then trained for two tasks: lung segmentation and opacity detection. Imaging characteristics were compared to clinical datapoints using the unpaired student's t-test or Mann-Whitney U test. Cohen's kappa analysis was used to evaluate the concordance of deep learning to conventional radiologist interpretation. Results Fifty-six percent of patients presented with alveolar opacities, 73% had interstitial opacities, and 23% had normal X-rays. The presence of alveolar or interstitial opacities was statistically correlated with age (P = 0.008) and comorbidities (P = 0.005). The extent of alveolar or interstitial opacities on baseline X-ray was significantly associated with the presence of endotracheal tube (P = 0.0008 and P = 0.049) or central line (P = 0.003 and P = 0.007). In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial opacities. Conclusion Chest X-ray analysis in an acute COVID-19 outbreak showed that the severity of opacities was associated with advanced age, comorbidities, as well as acuity of care. Artificial intelligence tools based upon deep learning of COVID-19 chest X-rays are feasible in the acute outbreak setting.

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations., Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Here, the authors present a multinational study on the application of deep learning algorithms for COVID-19 diagnosis against multiple lung conditions as controls.

To compare needle placement performance using an augmented reality (AR) navigation platform implemented on smartphone or smartglasses devices to that of CBCT-guided fluoroscopy in a phantom.An AR application was developed to display a planned percutaneous needle trajectory on the smartphone (iPhone7) and smartglasses (HoloLens1) devices in real time. Two AR-guided needle placement systems and CBCT-guided fluoroscopy with navigation software (XperGuide, Philips) were compared using an anthropomorphic phantom (CIRS, Norfolk, VA). Six interventional radiologists each performed 18 independent needle placements using smartphone (n = 6), smartglasses (n = 6), and XperGuide (n = 6) guidance. Placement error was defined as the distance from the needle tip to the target center. Placement time was recorded. For XperGuide, dose-area product (DAP, mGy*cmThe placement error using the smartphone, smartglasses, or XperGuide was similar (3.98 ± 1.68 mm, 5.18 ± 3.84 mm, 4.13 ± 2.38 mm, respectively, p = 0.11). Compared to CBCT-guided fluoroscopy, the smartphone and smartglasses reduced placement time by 38% (p = 0.02) and 55% (p = 0.001), respectively. The DAP for insertion using XperGuide was 3086 ± 2920 mGy*cmSmartphone- and smartglasses-based augmented reality reduced needle placement time and radiation exposure while maintaining placement accuracy compared to a clinically validated needle navigation platform.

Trauma is a significant contributor to mortality, especially in the young. Pelvic trauma with pelvic ring fractures may result in associated arterial injury, necessitating endovascular intervention. As a result, interventional radiology plays a critical role in partnering with trauma providers in the care of these patients. Management is determined by the acuity of the patient's clinical status, radiographs, ultrasound, and the results of computed tomography imaging when available. Numerous embolic agents are available for treatment of arterial hemorrhage.

The early infection dynamics of patients with SARS-CoV-2 are not well understood. We aimed to investigate and characterize associations between clinical, laboratory, and imaging features of asymptomatic and pre-symptomatic patients with SARS-CoV-2.Seventy-four patients with RT-PCR-proven SARS-CoV-2 infection were asymptomatic at presentation. All were retrospectively identified from 825 patients with chest CT scans and positive RT-PCR following exposure or travel risks in outbreak settings in Japan and China. CTs were obtained for every patient within a day of admission and were reviewed for infiltrate subtypes and percent with assistance from a deep learning tool. Correlations of clinical, laboratory, and imaging features were analyzed and comparisons were performed using univariate and multivariate logistic regression.Forty-eight of 74 (65%) initially asymptomatic patients had CT infiltrates that pre-dated symptom onset by 3.8 days. The most common CT infiltrates were ground glass opacities (45/48; 94%) and consolidation (22/48; 46%). Patient body temperature (p0.01), CRP (p0.01), and KL-6 (p = 0.02) were associated with the presence of CT infiltrates. Infiltrate volume (p = 0.01), percent lung involvement (p = 0.01), and consolidation (p = 0.043) were associated with subsequent development of symptoms.COVID-19 CT infiltrates pre-dated symptoms in two-thirds of patients. Body temperature elevation and laboratory evaluations may identify asymptomatic patients with SARS-CoV-2 CT infiltrates at presentation, and the characteristics of CT infiltrates could help identify asymptomatic SARS-CoV-2 patients who subsequently develop symptoms. The role of chest CT in COVID-19 may be illuminated by a better understanding of CT infiltrates in patients with early disease or SARS-CoV-2 exposure.• Forty-eight of 74 (65%) pre-selected asymptomatic patients with SARS-CoV-2 had abnormal chest CT findings. • CT infiltrates pre-dated symptom onset by 3.8 days (range 1-5). • KL-6, CRP, and elevated body temperature identified patients with CT infiltrates. Higher infiltrate volume, percent lung involvement, and pulmonary consolidation identified patients who developed symptoms.

Rationale and Objectives To investigate residents' knowledge of adverse effects of ionizing radiation, frequency of their education on radiation safety, and their use of radioprotective equipment. Materials and Methods Residents from 15/16 residency programs at Emory University were asked to complete a resident radiation safety survey through SurveyMonkey®. The associations between the residents' knowledge and use of radioprotective equipment with residents' specialty and year of training were investigated. Results Response rate was 32.5% (173/532 residents). Thirty-nine percent residents reported radiation safety is discussed in their residency curriculum at least every 6 months. Ninety-five percent believed in a link between radiation exposure and development of cancer. Overall and Radiology residents' knowledge about specific estimated dose effects (correct responses) was limited: radiation dose associated with fetus brain malformation in pregnancy (10% vs. 26%), risk of developing cataract in interventional personnel (27% vs. 47%), lifetime risk of cancer mortality from a single abdominal computed tomography (CT) in children (22% vs. 29%), greater radiosensitivity of children compared to adults (35% vs. 50%), and relative radiation dose from an abdominal CT compared to a chest x-ray (51% vs. 48%). Radiology residents had modestly higher knowledge. There was no significant difference in residents' knowledge across their postgraduate training years. Use of lead thyroid shields was reported by 86% (97% radiology vs. 80% nonradiology; P = .03) and radiation-monitoring badges in 39% (68% radiology vs. 15% nonradiology; P Conclusions Although radiology residents scored higher, knowledge of radiation safety for patients and healthcare workers is limited among residents regardless of medical specialty. These findings emphasize the need for educational initiatives.

This investigation evaluates the impact of the no oral contrast abdominopelvic CT examination (NOCAPE) on radiology turn around time (TAT), emergency department (ED) length of stay (LOS), and patient safety metrics. During a 12-month period at two urban teaching hospitals, 6,409 ED abdominopelvic (AP) CTs were performed to evaluate acute abdominal pain. NOCAPE represented 70.9 % of all ED AP CT examinations with intravenous contrast. Data collection included patient demographics, use of intravenous (IV) and/or oral contrast, order to complete and order to final interpretation TAT, ED LOS, admission, recall and bounce back rates, and comparison and characterization of impressions. The NOCAPE pathway reduced median order to complete TAT by 32 min (22.9 %) compared to IV and oral contrast AP CT examinations (traditional pathway) (P

To investigate residents' knowledge of adverse effects of ionizing radiation, frequency of their education on radiation safety, and their use of radioprotective equipment.Residents from 15/16 residency programs at Emory University were asked to complete a resident radiation safety survey through SurveyMonkey(®). The associations between the residents' knowledge and use of radioprotective equipment with residents' specialty and year of training were investigated.Response rate was 32.5% (173/532 residents). Thirty-nine percent residents reported radiation safety is discussed in their residency curriculum at least every 6 months. Ninety-five percent believed in a link between radiation exposure and development of cancer. Overall and Radiology residents' knowledge about specific estimated dose effects (correct responses) was limited: radiation dose associated with fetus brain malformation in pregnancy (10% vs. 26%), risk of developing cataract in interventional personnel (27% vs. 47%), lifetime risk of cancer mortality from a single abdominal computed tomography (CT) in children (22% vs. 29%), greater radiosensitivity of children compared to adults (35% vs. 50%), and relative radiation dose from an abdominal CT compared to a chest x-ray (51% vs. 48%). Radiology residents had modestly higher knowledge. There was no significant difference in residents' knowledge across their postgraduate training years. Use of lead thyroid shields was reported by 86% (97% radiology vs. 80% nonradiology; P = .03) and radiation-monitoring badges in 39% (68% radiology vs. 15% nonradiology; P.001) of the residents.Although radiology residents scored higher, knowledge of radiation safety for patients and healthcare workers is limited among residents regardless of medical specialty. These findings emphasize the need for educational initiatives.

Hospital readmission within 30 days of an index hospitalization is receiving increased scrutiny as a marker of poor-quality patient care. This study identifies factors associated with 30-day readmission after general surgery procedures.Using standard National Surgical Quality Improvement Project protocol, preoperative, intraoperative, and postoperative outcomes were collected on patients undergoing inpatient general surgery procedures at a single academic center between 2009 and 2011. Data were merged with our institutional clinical data warehouse to identify unplanned 30-day readmissions. Demographics, comorbidities, type of procedure, postoperative complications, and ICD-9 coding data were reviewed for patients who were readmitted. Univariate and multivariate analysis was used to identify risk factors associated with 30-day readmission.One thousand four hundred and forty-two general surgery patients were reviewed. One hundred and sixty-three (11.3%) were readmitted within 30 days of discharge. The most common reasons for readmission were gastrointestinal problem/complication (27.6%), surgical infection (22.1%), and failure to thrive/malnutrition (10.4%). Comorbidities associated with risk of readmission included disseminated cancer, dyspnea, and preoperative open wound (p0.05 for all variables). Surgical procedures associated with higher rates of readmission included pancreatectomy, colectomy, and liver resection. Postoperative occurrences leading to increased risk of readmission were blood transfusion, postoperative pulmonary complication, wound complication, sepsis/shock, urinary tract infection, and vascular complications. Multivariable analysis demonstrates that the most significant independent risk factor for readmission is the occurrence of any postoperative complication (odds ratio = 4.20; 95% CI, 2.89-6.13).Risk factors for readmission after general surgery procedures are multifactorial, however, postoperative complications appear to drive readmissions in surgical patients. Taking appropriate steps to minimize postoperative complications will decrease postoperative readmissions.