Dual-energy CT iodine overlay improves efficiency of oral contrast leak assessment.

BACKGROUND: Evaluation for gastrointestinal leak is a frequent imaging indication, and dual-energy computed tomography (DECT) with oral or rectally administered contrast can be used to improve efficiency and diagnostic confidence. PURPOSE: To assess the value of the DECT iodine overlay (IO) reconstruction as a stand-alone image set compared to routine CT in assessing oral or rectal contrast leak from the gastrointestinal system. MATERIAL AND METHODS: A blinded, retrospective audit study was performed by three readers who each interpreted 50 studies performed for assessment of oral or rectal contrast leak that were acquired using DECT. Each reader independently assessed both the routine CT images and the images of the reconstructed IO for contrast leak in random order with a six-week "wash-out period" between readings. Clinical follow-up provided the reference standard. Readers recorded the presence/absence of a leak, diagnostic confidence, image quality score, and interpretation time for each image set. RESULTS: Pooled data for overall accuracy in identification of a leak increased from 0.81 (95% confidence interval [CI]=0.74-0.87) for routine CT to 0.91 (95% CI=0.85-0.95) with IO, and the area under the curve (AUC) was significantly higher for IO than routine CT (P = 0.015). Readers required significantly less time to interpret IO than routine CT (median improvement of 12.5 s per image using pooled data; P < 0.001) while maintaining diagnostic confidence and perceived image quality. CONCLUSION: Use of DECT IO reconstructions for identification of oral or rectal contrast leak requires less time to interpret than routine CT with improved accuracy and maintained diagnostic confidence and perceived image quality.

Quantitative myocardial T2 mapping adds value to Japanese circulation society diagnostic criteria for active cardiac sarcoidosis.

Noninvasive identification of active myocardial inflammation in patients with cardiac sarcoidosis plays a key role in management but remains elusive. T2 mapping is a proposed solution, but the added value of quantitative myocardial T2 mapping for active cardiac sarcoidosis is unknown. Retrospective cohort analysis of 56 sequential patients with biopsy-confirmed extracardiac sarcoidosis who underwent cardiac MRI for myocardial T2 mapping. The presence or absence of active myocardial inflammation in patients with CS was defined using a modified Japanese circulation society criteria within one month of MRI. Myocardial T2 values were obtained for the 16 standard American Heart Association left ventricular segments. The best model was selected using logistic regression. Receiver operating characteristic curves and dominance analysis were used to evaluate the diagnostic performance and variable importance. Of the 56 sarcoidosis patients included, 14 met criteria for active myocardial inflammation. Mean basal T2 value was the best performing model for the diagnosis of active myocardial inflammation in CS patients (pR2 = 0.493, AUC = 0.918, 95% CI 0.835-1). Mean basal T2 value > 50.8 ms was the most accurate threshold (accuracy = 0.911). Mean basal T2 value + JCS criteria was significantly more accurate than JCS criteria alone (AUC = 0.981 vs. 0.887, p = 0.017). Quantitative regional T2 values are independent predictors of active myocardial inflammation in CS and may add additional discriminatory capability to JCS criteria for active disease.

Computed Tomography Assessment of Gastric Band Slippage.

Background: The purpose of this study was to develop and validate reliable computed tomography (CT) imaging criteria for the diagnosis of gastric band slippage. Material and Methods: We retrospectively evaluated 67 patients for gastric band slippage using CT. Of these, 14 had surgically proven gastric band slippage (study group), 22 had their gastric bands removed for reasons other than slippage (control group 1), and 31 did not require removal (control group 2). All of the studies were read independently by two radiologists in a blinded fashion. The "O" sign, phi angle, amount of inferior displacement from the esophageal hiatus, and gastric pouch size were used to create CT diagnostic criteria. Standard statistical methods were used. Results: There was good overall interobserver agreement for diagnosis of gastric band slippage using CT diagnostic criteria (kappa = 0.83). Agreement was excellent for the "O" sign (kappa = 0.93) and phi angle (intraclass correlation coefficient = 0.976). The "O" sign, inferior displacement from the hiatus >3.5 cm, and gastric pouch volume >55 cm3 each had 100% positive predictive value. A phi angle <20° or >60° had the highest negative predictive value (NPV) (98%). Of all CT diagnostic criteria, enlarged gastric pouch size was most correlated with band slippage with an AUC of 0.991. Conclusion: All four imaging parameters were useful in evaluating for gastric band slippage on CT, with good interobserver agreement. Of these parameters, enlarged gastric pouch size was most correlated with slippage and abnormal phi angle had the highest NPV.

A novel dual balloon rectal catheter for use in the postoperative assessment of low rectal anastomoses following low anterior resection.

Following low anterior resection (LAR) of the colon, an image-guided assessment of the anastomosis for leak is typically performed using an enema via a rectal catheter, whether by CT or fluoroscopy. However, there is potential for poor assessment due to inappropriate catheter positioning as well as potential risk that the anastomosis becomes compromised by the balloon inflation. This article discusses the adaptation of a novel double-balloon catheter (originally designed by a member of our institution for use in pediatric intussusception reduction) for assessment of low rectal anastomoses. The goal of this technical note is to demonstrate our experience with this catheter, primarily through example cases, and explain its potential for optimizing colon distension, minimizing improper catheter placement, and potentially reducing the risk of iatrogenic anastomosis disruption.

Diagnostic accuracy and performance of artificial intelligence in measuring left atrial volumes and function on multiphasic CT in patients with atrial fibrillation.

OBJECTIVES: To evaluate the effectiveness of a novel artificial intelligence (AI) algorithm for fully automated measurement of left atrial (LA) volumes and function using cardiac CT in patients with atrial fibrillation. METHODS: We included 79 patients (mean age 63 ± 12 years; 35 with atrial fibrillation (AF) and 44 controls) between 2017 and 2020 in this retrospective study. Images were analyzed by a trained AI algorithm and an expert radiologist. Left atrial volumes were obtained at cardiac end-systole, end-diastole, and pre-atrial contraction, which were then used to obtain LA function indices. Intraclass correlation coefficient (ICC) analysis of the LA volumes and function parameters was performed and receiver operating characteristic (ROC) curve analysis was used to compare the ability to detect AF patients. RESULTS: The AI was significantly faster than manual measurement of LA volumes (4 s vs 10.8 min, respectively). Agreement between the manual and automated methods was good to excellent overall, and there was stronger agreement in AF patients (all ICCs ≥ 0.877; p < 0.001) than controls (all ICCs ≥ 0.799; p < 0.001). The AI comparably estimated LA volumes in AF patients (all within 1.3 mL of the manual measurement), but overestimated volumes by clinically negligible amounts in controls (all by ≤ 4.2 mL). The AI's ability to distinguish AF patients from controls using the LA volume index was similar to the expert's (AUC 0.81 vs 0.82, respectively; p = 0.62). CONCLUSION: The novel AI algorithm efficiently performed fully automated multiphasic CT-based quantification of left atrial volume and function with similar accuracy as compared to manual quantification. Novel CT-based AI algorithm efficiently quantifies left atrial volumes and function with similar accuracy as manual quantification in controls and atrial fibrillation patients. KEY POINTS: • There was good-to-excellent agreement between manual and automated methods for left atrial volume quantification. • The AI comparably estimated LA volumes in AF patients, but overestimated volumes by clinically negligible amounts in controls. • The AI's ability to distinguish AF patients from controls was similar to the manual methods.

Tumor burden of lung metastases at initial staging in breast cancer patients detected by artificial intelligence as a prognostic tool for precision medicine.

BACKGROUND: Determination of the total number and size of all pulmonary metastases on chest CT is time-consuming and as such has been understudied as an independent metric for disease assessment. A novel artificial intelligence (AI) model may allow for automated detection, size determination, and quantification of the number of pulmonary metastases on chest CT. OBJECTIVE: To investigate the utility of a novel AI program applied to initial staging chest CT in breast cancer patients in risk assessment of mortality and survival. METHODS: Retrospective imaging data from a cohort of 226 subjects with breast cancer was assessed by the novel AI program and the results validated by blinded readers. Mean clinical follow-up was 2.5 years for outcomes including cancer-related death and development of extrapulmonary metastatic disease. AI measurements including total number of pulmonary metastases and maximum nodule size were assessed by Cox-proportional hazard modeling and adjusted survival. RESULTS: 752 lung nodules were identified by the AI program, 689 of which were identified in 168 subjects having confirmed lung metastases (Lmet+) and 63 were identified in 58 subjects without confirmed lung metastases (Lmet-). When compared to the reader assessment, AI had a per-patient sensitivity, specificity, PPV and NPV of 0.952, 0.639, 0.878, and 0.830. Mortality in the Lmet + group was four times greater compared to the Lmet-group (p = 0.002). In a multivariate analysis, total lung nodule count by AI had a high correlation with overall mortality (OR 1.11 (range 1.07-1.15), p < 0.001) with an AUC of 0.811 (R2 = 0.226, p < 0.0001). When total lung nodule count and maximum nodule diameter were combined there was an AUC of 0.826 (R2 = 0.243, p < 0.001). CONCLUSION: Automated AI-based detection of lung metastases in breast cancer patients at initial staging chest CT performed well at identifying pulmonary metastases and demonstrated strong correlation between the total number and maximum size of lung metastases with future mortality. CLINICAL IMPACT: As a component of precision medicine, AI-based measurements at the time of initial staging may improve prediction of which breast cancer patients will have negative future outcomes.

Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes.

BACKGROUND: Low-dose computed tomography (LDCT) are performed routinely for lung cancer screening. However, a large amount of nonpulmonary data from these scans remains unassessed. We aimed to validate a deep learning model to automatically segment and measure left atrial (LA) volumes from routine NCCT and evaluate prediction of cardiovascular outcomes. METHODS: We retrospectively evaluated 273 patients (median age 69 years, 55.5% male) who underwent LDCT for lung cancer screening. LA volumes were quantified by three expert cardiothoracic radiologists and a prototype AI algorithm. LA volumes were then indexed to the body surface area (BSA). Expert and AI LA volume index (LAVi) were compared and used to predict cardiovascular outcomes within five years. Logistic regression with appropriate univariate statistics were used for modelling outcomes. RESULTS: There was excellent correlation between AI and expert results with an LAV intraclass correlation of 0.950 (0.936-0.960). Bland-Altman plot demonstrated the AI underestimated LAVi by a mean 5.86 â€‹mL/m2. AI-LAVi was associated with new-onset atrial fibrillation (AUC 0.86; OR 1.12, 95% CI 1.08-1.18, p â€‹< â€‹0.001), HF hospitalization (AUC 0.90; OR 1.07, 95% CI 1.04-1.13, p â€‹< â€‹0.001), and MACCE (AUC 0.68; OR 1.04, 95% CI 1.01-1.07, p â€‹= â€‹0.01). CONCLUSION: This novel deep learning algorithm for automated measurement of LA volume on lung cancer screening scans had excellent agreement with manual quantification. AI-LAVi is significantly associated with increased risk of new-onset atrial fibrillation, HF hospitalization, and major adverse cardiac and cerebrovascular events within 5 years.

Coronary CT Fractional Flow Reserve before Transcatheter Aortic Valve Replacement: Clinical Outcomes.

Background The role of CT angiography-derived fractional flow reserve (CT-FFR) in pre-transcatheter aortic valve replacement (TAVR) assessment is uncertain. Purpose To evaluate the predictive value of on-site machine learning-based CT-FFR for adverse clinical outcomes in candidates for TAVR. Materials and Methods This observational retrospective study included patients with severe aortic stenosis referred to TAVR after coronary CT angiography (CCTA) between September 2014 and December 2019. Clinical end points comprised major adverse cardiac events (MACE) (nonfatal myocardial infarction, unstable angina, cardiac death, or heart failure admission) and all-cause mortality. CT-FFR was obtained semiautomatically using an on-site machine learning algorithm. The ability of CT-FFR (abnormal if ≤0.75) to predict outcomes and improve the predictive value of the current noninvasive work-up was assessed. Survival analysis was performed, and the C-index was used to assess the performance of each predictive model. To compare nested models, the likelihood ratio χ2 test was performed. Results A total of 196 patients (mean age ± standard deviation, 75 years ± 11; 110 women [56%]) were included; the median time of follow-up was 18 months. MACE occurred in 16% (31 of 196 patients) and all-cause mortality in 19% (38 of 196 patients). Univariable analysis revealed CT-FFR was predictive of MACE (hazard ratio [HR], 4.1; 95% CI: 1.6, 10.8; P = .01) but not all-cause mortality (HR, 1.2; 95% CI: 0.6, 2.2; P = .63). CT-FFR was independently associated with MACE (HR, 4.0; 95% CI: 1.5, 10.5; P = .01) when adjusting for potential confounders. Adding CT-FFR as a predictor to models that include CCTA and clinical data improved their predictive value for MACE (P = .002) but not all-cause mortality (P = .67), and it showed good discriminative ability for MACE (C-index, 0.71). Conclusion CT angiography-derived fractional flow reserve was associated with major adverse cardiac events in candidates for transcatheter aortic valve replacement and improved the predictive value of coronary CT angiography assessment. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Choe in this issue.

Normative Values of Pediatric Thoracic Aortic Diameters Indexed to Body Surface Area Using Computed Tomography.

PURPOSE: The purpose of this study was to establish normative values for the thoracic aorta diameter in pediatric patients from birth to 18 years of age using computed tomography (CT) measurements and to create nomograms related to body surface area (BSA). METHODS: A total of 623 pediatric patients without cardiovascular disease (42.1% females; from 3 d to 18 y old) with high-quality, non-electrocardiogram-gated, contrast-enhanced CT imaging of the chest were retrospectively evaluated. Systematic measurements of the aortic diameter at predetermined levels were recorded, and demographic data including age, sex, ethnicity, and BSA were collected. Reference graphs plotting BSA over aortic diameter included the mean and Z -3 to Z +3, where Z represents SDs from the mean. RESULTS: The study population was divided into 2 groups (below 2 and greater than or equal to 2 y old). There were no significant differences in average aortic measurements between males and females. Both age groups exhibited significant positive correlations among all size-related metrics (all P <0.001) with BSA having the highest correlation. For both groups, the average orthogonal thoracic aortic diameters at each level of the thoracic aorta were used to create nomograms. CONCLUSION: This study establishes clinically applicable, BSA-specific reference values of the normal thoracic aorta for the pediatric population from CT imaging.

Incidental Serous Cystadenoma Presenting as a Photopenic Pancreatic Defect on 18F-Fluciclovine PET/CT.

ABSTRACT: 18F-fluciclovine (Axumin) PET/CT has been widely used for the evaluation of biochemically recurrent prostate cancer following prior treatment. While lymph node and visceral organ metastases typically show increased radiotracer uptake, altered patterns of normal physiologic activity may also provide insight into other disease processes. We present a case of an incidental pancreatic head mass presenting as a photopenic defect on a staging 18F-fluciclovine PET/CT, which was subsequently confirmed to be a benign serous cystadenoma using multisequence MRI.

The diagnostic value of non-contrast magnetic resonance coronary angiography in the assessment of coronary artery disease: A systematic review and meta-analysis.

PURPOSE: The current literature reports a wide range of diagnostic accuracy of non-contrast magnetic resonance coronary angiography (NC-MRCA) for the assessment of coronary artery disease (CAD). We aimed to compare the clinical effectiveness of NC-MRCA with that of invasive coronary angiography (ICA) in patients with suspected CAD using a systematic review and meta-analysis. METHODS: Two investigators independently extracted 36 published manuscripts between 2010 and 2019. Databases including Medline, Web of Knowledge, Google Scholar, Scopus, and Cochrane were searched using pre-established keywords. Analysis of the data followed the PRISMA statement for reporting systematic reviews and meta-analyses and primary analysis followed the Mantel-Hansel methodology. Correctness of classification for detecting coronary artery stenosis ≥50% (CAS) was measured using ICA as the gold standard. RESULTS: A total of five studies met inclusion criteria, with a total of 417 patients and 2883 coronary segments. The pooled per patient sensitivity and specificity of NC-MRCA for CAS in suspected patients was 90.3% (95% CI 85.6-95.1%) and 77.9% (95% CI 69.5-86.3%). Pooled per vessel assessment of NC- MRCA revealed a sensitivity of 83.7% (95%CI 79.7-87.8%) and specificity of 90.0% (95%CI 86.7-93.4%). Per-segment assessment of NC-MRCA showed a pooled sensitivity of 81.6% (95% CI 76.8-86.4) and specificity of 97.0% (95% CI 95.5-98.5). Mild to moderate heterogeneity was noted in most diagnostic parameters with larger heterogeneity noted in the per-segment analyses. There was less heterogeneity in sensitivity and NPV than specificity and PPV. CONCLUSION: According to this meta-analysis, non-contrast coronary MRA resulted in adequate screening in patients with suspected CAD with high sensitivity and specificity. This result was true for per-patient, per-vessel, and per-segment assessment.

Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value.

BACKGROUND: Artificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT. METHODS: A total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen's kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis. RESULTS: Agreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen's kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUCMACE = 0.911, AUCLung Cancer = 0.942). CONCLUSION: We conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists.

Predictive value of initial imaging and staging with long-term outcomes in young adults diagnosed with colorectal cancer.

PURPOSE: To evaluate how initial abdominopelvic CT findings and staging correlate with outcomes in a cohort of patients aged 18-40 years. METHODS: We evaluated all young adult patients at a single tertiary center diagnosed with histopathologically confirmed CRC who also had CT of the abdomen and pelvis at the time of initial diagnosis. Demographics, symptoms, CT findings, staging, treatments, and outcomes at 1 year and 5 years were recorded. RESULTS: Of 91 patients who met initial inclusion criteria, 81.8% had a mass present on CT, with an average size of 4.8 cm ± 2.9. A majority of patients were surgical stage III or IV (64.3%). Advanced AJCC stage was more common with rectal tumors and metastatic disease on initial CT (p < 0.0001). In a subgroup analysis, almost all patients initially staged 4A or higher had progression of disease. At the final follow-up visit, by RECIST 1.1 criteria, 58.8% had progressive disease, 35.3% complete response, and 3.9% stable disease. The overall 5-year survival rate in this subgroup was 40% with lower survival probability with increasing stage (p = 0.0001). CONCLUSION: Most young adult patients presented with large tumors on imaging, increasing the likelihood of identification on CT. Tumors initially presenting in the rectum with enlarged lymph nodes and/or with distant metastases on CT were more often associated with advanced surgical stage and poorer prognosis. A majority of patients presented at an advanced stage, most commonly stage 4A, and had progression of disease at follow-up.

Prevalence of Abnormal Coronary Findings on Coronary Computed Tomography Angiography Among Young Adults Presenting With Chest Pain.

PURPOSE: We evaluated the prevalence of coronary stenosis on coronary computed tomography angiography (CCTA) in patients aged 18 to 30 years, who presented to the emergency department with chest pain. We also examined the risk factors potentially associated with abnormal coronary findings on CCTA in this age group. MATERIALS AND METHODS: A total of 884 patients were retrospectively evaluated. Indication for CCTA was guided by our hospital's chest pain protocol based on ACC/AHA guidelines. These were performed using the standard technique and interpreted based on CAD-RADS guidelines. Scans were identified as abnormal if atherosclerotic coronary artery disease (CAD), myocardial bridging (MB), or any anatomic coronary artery anomaly were present. RESULTS: Twenty-two percent of patients had a coronary abnormality on CCTA. The most common abnormality was MB (17.3%), followed by CAD (4.4%) and coronary anomalies (1.5%). A small minority had stenosis (2.8%), most commonly caused by CAD. Most cases with stenosis were minimal to mild (72%) with 0.8% having coronary stenosis ≥50%. Age and male sex were risk factors for both coronary artery stenosis (odds ratio: 1.32 and 4.50, 95% confidence interval: 1.03-1.69, and 1.23-16.46, P=0.028 and 0.023, respectively) and CAD (odds ratio: 1.52 and 3.67, 95% confidence interval: 1.14-2.04, and 1.26-10.66, P=0.005 and 0.017, respectively). CONCLUSIONS: Epicardial coronary stenosis is rarely the cause of chest pain among young adult patients presenting to the emergency department. Age and male sex were both risk factors for coronary artery stenosis/disease in this age group.

Mapping the Ultrasound Landscape to Define Point-of-Care Ultrasound and Diagnostic Ultrasound: A Proposal From the Society of Radiologists in Ultrasound and ACR Commission on Ultrasound.

Current descriptions of ultrasound evaluations, including use of the term "point-of-care ultrasound" (POCUS), are imprecise because they are predicated on distinctions based on the device used to obtain images, the location where the images were obtained, the provider who obtained the images, or the focus of the examination. This is confusing because it does not account for more meaningful distinctions based on the setting, comprehensiveness, and completeness of the evaluation. In this article, the Society of Radiologists in Ultrasound and the members of the American College of Radiology Ultrasound Commission articulate a map of the ultrasound landscape that divides sonographic evaluations into four distinct categories on the basis of setting, comprehensiveness, and completeness. Details of this classification scheme are elaborated, including important clarifications regarding what ensures comprehensiveness and completeness. Practical implications of this framework for future research and reimbursement paradigms are highlighted.

Improving CT-Derived Fractional Flow Reserve Analysis: A Quality Improvement Initiative.

Objectives The aim of this study was to identify factors and quality improvement strategies to improve coronary computed tomography angiography (CCTA) studies referred for fractional flow reserve derived from CT angiography (FFRCT) analysis. Methods Thirty randomly selected CCTAs were analyzed for quality control. A uniform CCTA protocol was implemented by an in-house steering committee, emphasizing the importance of adequate heart rate control and nitroglycerine usage. Sixty additional randomly selected CCTAs were evaluated for quality at multiple time points during intervention, and FFRCT acceptance rate was analyzed at the conclusion. Results Prior to the implementation of this quality improvement program, our overall institution-specific percent acceptance rate was 76.1% for FFRCT compared to the national average of >95%. Post-intervention, this was improved to an average acceptance rate of 90% for FFRCT analysis. Conclusions Establishment and strict adherence to CCTA imaging protocols with appropriate training and adequate buy-in of CT technologists and nurses is a viable way of improving the quality of imaging and subsequent patient care.

The Future of Concurrent Automated Coronary Artery Calcium Scoring on Screening Low-Dose Computed Tomography.

Low-dose computed tomography (LDCT) has been extensively validated for lung cancer screening in selected patient populations. Additionally, the use of gated cardiac CT to assess coronary artery calcium (CAC) burden has been validated to determine a patient's risk for major cardiovascular adverse events. This is typically performed by calculating an Agatston score based on density and overall burden of calcified plaque within the coronary arteries. Patients that qualify for LDCT for lung cancer screening commonly share major risk factors for coronary artery disease and would frequently benefit from an additional gated cardiac CT for the assessment of CAC. Given the widespread use of LDCT for lung cancer screening, we evaluated current literature regarding the use of non-gated chest CT, specifically LDCT, for the detection and grading of coronary artery calcifications. Additionally, given the evolving and increasing use of artificial intelligence (AI) in the interpretation of radiologic studies, current literature for the use of AI in CAC assessment was reviewed.  We reviewed primary scientific literature dating up to April 2020 using Pubmed and Google Scholar, with the search terms low dose CT, lung cancer screening, coronary artery calcium, EKG/cardiac gated CT, deep learning, machine learning, and AI. These publications were then independently evaluated by each member of our team. Overall, there was a consensus within these papers that LDCT for lung cancer screening plays a role in the evaluation of CAC. Most studies note the inherent problems with the evaluation of the density of coronary calcifications on LDCT to give an accurate numeric calcium or Agatston score. The current method of evaluating CAC on LDCT involves using a qualitative categorical system (none, mild, moderate, or severe). When performed by cardiac imaging experts, this method broadly correlates with traditional CAC score groups (0, 1 to 100, 101 to 400, and > 400). Furthermore, given the high sensitivity of a properly protocolled LDCT for coronary calcium, a negative study for CAC precludes the need for a dedicated gated CT assessment. However, qualitative methods are not as accurate or reproducible when performed by general radiologists. The implementation of AI in the LDCT screening process has the potential to give a quantifiable and reproducible numeric value to the calcium score, based on whole heart volume scoring of calcium. This more closely aligns with the Agatston score and serves as a better guide for treatment and risk assessment using current guidelines. We conclude that CAC should be assessed on all LDCT performed for lung cancer screening and that a qualitative categorical scoring system should be provided in the impression for each patient. Early studies involving AI for the assessment of CAC are promising, but more extensive studies are needed before a final recommendation for its use can be given. The implementation of an accurate, automated AI CAC assessment tool would improve radiologist compliance and ease of overall workflow. Ultimately, the potential end result would be improved turnaround time, better patient outcomes, and reduced healthcare costs by maximizing preventative care in this high-risk population.

Juvenile xanthogranuloma of the subdural spaces mimicking chronic hematoma with positive FDG uptake on PET: case report.

The authors report an unusual presentation of juvenile xanthogranuloma (JXG), a non-Langerhans cell histiocytosis of infancy and early childhood. This entity typically presents as a cutaneous head or neck nodule but can manifest with more systemic involvement including in the central nervous system. However, currently there is limited information regarding specific imaging features differentiating JXG from other neuropathological entities, with diagnosis typically made only after tissue sampling. The authors reviewed the initial images of a young patient with shunt-treated hydrocephalus and enlarging, chronic, extraaxial processes presumed to reflect subdural collections from overshunting, and they examine the operative discovery of a mass lesion that was pathologically proven to be JXG. Their results incorporate the important associated histological and advanced imaging features, including previously unreported metabolic activity on FDG PET. Ultimately, the case underscores the need to consider JXG in differential diagnoses of pediatric intracranial masses and highlights the potential role of PET in the initial diagnosis and response to treatment.

Reconstitution of CD4 T cells in bronchoalveolar lavage fluid after initiation of highly active antiretroviral therapy.

The massive depletion of gastrointestinal-tract CD4 T cells is a hallmark of the acute phase of HIV infection. In contrast, the depletion of the lower-respiratory-tract mucosal CD4 T cells as measured in bronchoalveolar lavage (BAL) fluid is more moderate and similar to the depletion of CD4 T cells observed in peripheral blood (PB). To understand better the dynamics of disease pathogenesis and the potential for the reconstitution of CD4 T cells in the lung and PB following the administration of effective antiretroviral therapy, we studied cell-associated viral loads, CD4 T-cell frequencies, and phenotypic and functional profiles of antigen-specific CD4 T cells from BAL fluid and blood before and after the initiation of highly active antiretroviral therapy (HAART). The major findings to emerge were the following: (i) BAL CD4 T cells are not massively depleted or preferentially infected by HIV compared to levels for PB; (ii) BAL CD4 T cells reconstitute after the initiation of HAART, and their infection frequencies decrease; (iii) BAL CD4 T-cell reconstitution appears to occur via the local proliferation of resident BAL CD4 T cells rather than redistribution; and (iv) BAL CD4 T cells are more polyfunctional than CD4 T cells in blood, and their functional profile is relatively unchanged after the initiation of HAART. Taken together, these data suggest mechanisms for mucosal CD4 T-cell depletion and interventions that might aid in the reconstitution of mucosal CD4 T cells.