Spectral segmentation and radiomic features predict carotid stenosis and ipsilateral ischemic burden from DECT angiography

PURPOSEThe purpose of this study is to compare spectral segmentation, spectral radiomic, and single- energy radiomic features in the assessment of internal and common carotid artery (ICA/CCA) stenosis and prediction of surgical outcome.METHODSOur ethical committee–approved, Health Insurance Portability and Accountability Act (HIPAA)- compliant study included 85 patients (mean age, 73 ± 10 years; male : female, 56 : 29) who under- went contrast-enhanced, dual-source dual-energy CT angiography (DECTA) (Siemens Definition Flash) of the neck for assessing ICA/CCA stenosis. Patients with a prior surgical or interventional treatment of carotid stenosis were excluded. Two radiologists graded the severity of carotid ste- nosis on DECTA images as mild (70%) stenosis. Thin-section, low- and high-kV DICOM images from the arterial phase acquisi- tion were processed with a dual-energy CT prototype (DTA, eXamine, Siemens Healthineers) to generate spectral segmentation and radiomic features over regions of interest along the entire length (volume) and separately at a single-section with maximum stenosis. Multiple logistic regressions and area under the receiver operating characteristic curve (AUC) were used for data analysis.RESULTSAmong 85 patients, 22 ICA/CCAs had normal luminal dimensions and 148 ICA/CCAs had luminal stenosis (mild stenosis: 51, moderate: 38, severe: 59). For differentiating non-severe and severe ICA/CCA stenosis, radiomic features (volume: AUC=0.94, 95% CI 0.88-0.96; section: AUC=0.92, 95% CI 0.86-0.93) were significantly better than spectral segmentation features (volume: AUC = 0.86, 95% CI 0.74-0.87; section: AUC = 0.68, 95% CI 0.66-0.78) (P < .001). Spectral radiomic features predicted revascularization procedure (AUC = 0.77) and the presence of ipsilateral intra- cranial ischemic changes (AUC = 0.76).CONCLUSIONSpectral segmentation and radiomic features from DECTA can differentiate patients with differ- ent luminal ICA/CCA stenosis grades

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

Preoperative Renal Parenchyma Volume as a Predictor of Kidney Function Following Nephrectomy of Complex Renal Masses

Background: The von Hippel-Lindau disease (VHL) is a hereditary cancer syndrome with multifocal, bilateral cysts and solid tumors of the kidney. Surgical management may include multiple extirpative surgeries, which ultimately results in parenchymal volume loss and subsequent renal function decline. Recent studies have utilized parenchyma volume as an estimate of renal function prior to surgery for renal cell carcinoma; however, it is not yet validated for surgically altered kidneys with multifocal masses and complex cysts such as are present in VHL. Objective: We sought to validate a magnetic resonance imaging (MRI)-based volumetric analysis with mercaptoacetyltriglycine (MAG-3) renogram and postoperative renal function. Design, setting, and participants: We identified patients undergoing renal surgery at the National Cancer Institute from 2015 to 2020 with preoperative MRI. Renal tumors, cysts, and parenchyma of the operated kidney were segmented manually using ITK-SNAP software. Outcome measurements and statistical analysis: Serum creatinine and urinalysis were assessed preoperatively, and at 3- and 12-mo follow-up time points. Estimated glomerular filtration rate (eGFR) was calculated using serum creatinine-based CKD-EPI 2021 equation. A statistical analysis was conducted on R Studio version 4.1.1. Results and limitations: Preoperative MRI scans of 113 VHL patients (56% male, median age 48 yr) were evaluated between 2015 and 2021. Twelve (10.6%) patients had a solitary kidney at the time of surgery; 59 (52%) patients had at least one previous partial nephrectomy on the renal unit. Patients had a median of three (interquartile range [IQR]: 2–5) tumors and five (IQR: 0–13) cysts per kidney on imaging. The median preoperative GFR was 70 ml/min/1.73 m2 (IQR: 58–89). Preoperative split renal function derived from MAG-3 studies and MRI split renal volume were significantly correlated (r = 0.848, p < 0.001). On the multivariable analysis, total preoperative parenchymal volume, solitary kidney, and preoperative eGFR were significant independent predictors of 12-mo eGFR. When only considering patients with two kidneys undergoing partial nephrectomy, preoperative parenchymal volume and eGFR remained significant predictors of 12-mo eGFR. Conclusions: A parenchyma volume analysis on preoperative MRI correlates well with renogram split function and can predict long-term renal function with added benefit of anatomic detail and ease of application. Patient summary: Prior to kidney surgery, it is important to understand the contribution of each kidney to overall kidney function. Nuclear medicine scans are currently used to measure split kidney function. We demonstrated that kidney volumes on preoperative magnetic resonance imaging can also be used to estimate split kidney function before surgery, while also providing essential details of tumor and kidney anatomy

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&lt;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&lt;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

Deep learning in chest radiography: Detection of findings and presence of change.

BACKGROUND:Deep learning (DL) based solutions have been proposed for interpretation of several imaging modalities including radiography, CT, and MR. For chest radiographs, DL algorithms have found success in the evaluation of abnormalities such as lung nodules, pulmonary tuberculosis, cystic fibrosis, pneumoconiosis, and location of peripherally inserted central catheters. Chest radiography represents the most commonly performed radiological test for a multitude of non-emergent and emergent clinical indications. This study aims to assess accuracy of deep learning (DL) algorithm for detection of abnormalities on routine frontal chest radiographs (CXR), and assessment of stability or change in findings over serial radiographs. METHODS AND FINDINGS:We processed 874 de-identified frontal CXR from 724 adult patients (> 18 years) with DL (Qure AI). Scores and prediction statistics from DL were generated and recorded for the presence of pulmonary opacities, pleural effusions, hilar prominence, and enlarged cardiac silhouette. To establish a standard of reference (SOR), two thoracic radiologists assessed all CXR for these abnormalities. Four other radiologists (test radiologists), unaware of SOR and DL findings, independently assessed the presence of radiographic abnormalities. A total 724 radiographs were assessed for detection of findings. A subset of 150 radiographs with follow up examinations was used to asses change over time. Data were analyzed with receiver operating characteristics analyses and post-hoc power analysis. RESULTS:About 42% (305/ 724) CXR had no findings according to SOR; single and multiple abnormalities were seen in 23% (168/724) and 35% (251/724) of CXR. There was no statistical difference between DL and SOR for all abnormalities (p = 0.2-0.8). The area under the curve (AUC) for DL and test radiologists ranged between 0.837-0.929 and 0.693-0.923, respectively. DL had lowest AUC (0.758) for assessing changes in pulmonary opacities over follow up CXR. Presence of chest wall implanted devices negatively affected the accuracy of DL algorithm for evaluation of pulmonary and hilar abnormalities. CONCLUSIONS:DL algorithm can aid in interpretation of CXR findings and their stability over follow up CXR. However, in its present version, it is unlikely to replace radiologists due to its limited specificity for categorizing specific findings