Non-contrast computed tomography findings for identification of chronically occluded coronary artery bypass grafts

BACKGROUND

Detecting occlusions of coronary artery bypass grafts using non-contrast computed tomography (CT) series is understudied and underestimated.

PURPOSE

To evaluate morphological findings for the diagnosis of chronic coronary artery bypass graft occlusion on non-contrast CT and investigate performance statistics for potential use cases.

MATERIAL AND METHODS

Seventy-three patients with coronary artery bypass grafts who had CT angiography of the chest (non-contrast and arterial phases) were retrospectively included. Two readers applied pre-set morphologic findings to assess the patency of a bypass graft on non-contrast series. These findings included vessel shape (linear-band like), collapsed lumen and surgical graft marker without a visible vessel. Performance was tested using the simultaneously acquired arterial phase series as the ground truth.

RESULTS

The per-patient diagnostic accuracy for occlusion was 0.890 (95% confidence interval = 0.795-0.951). Venous grafts overall had an 88% accuracy. None of the left internal mammary artery to left anterior descending artery arterial graft occlusions were detected. The negative likelihood ratio for an occluded graft that is truly patent was 0.121, demonstrating a true post-test probability of 97% for identifying a patent graft as truly patent given a prevalence of 20% occlusion at a median 8.4 years post-surgery. Neither years post-surgery, nor number of vessels was associated with a significant decrease in reader accuracy.

CONCLUSION

Evaluation of coronary bypass grafts for chronic occlusion on non-contrast CT based off vessel morphology is feasible and accurate for venous grafts. Potential use cases include low-intermediate risk patients with chest pain or shortness of breath for whom non-contrast CT was ordered, or administration of iodine-based contrast is contraindicated.

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.

A deep convolutional neural network ensemble for composite identification of pulmonary nodules and incidental findings on routine PET/CT

AIM

To evaluate primary and secondary pathologies of interest using an artificial intelligence (AI) platform, AI-Rad Companion, on low-dose computed tomography (CT) series from integrated positron-emission tomography (PET)/CT to detect CT findings that might be overlooked.

MATERIALS AND METHODS

One hundred and eighty-nine sequential patients who had undergone PET/CT were included. Images were evaluated using an ensemble of convolutional neural networks (AI-Rad Companion, Siemens Healthineers, Erlangen, Germany). The primary outcome was detection of pulmonary nodules for which the accuracy, identity, and intra-rater reliability was calculated. For secondary outcomes (binary detection of coronary artery calcium, aortic ectasia, vertebral height loss), accuracy and diagnostic performance were calculated.

RESULTS

The overall per-nodule accuracy for detection of lung nodules was 0.847. The overall sensitivity and specificity for detection of lung nodules was 0.915 and 0.781. The overall per-patient accuracy for AI detection of coronary artery calcium, aortic ectasia, and vertebral height loss was 0.979, 0.966, and 0.840, respectively. The sensitivity and specificity for coronary artery calcium was 0.989 and 0.969. The sensitivity and specificity for aortic ectasia was 0.806 and 1.

CONCLUSION

The neural network ensemble accurately assessed the number of pulmonary nodules and presence of coronary artery calcium and aortic ectasia on low-dose CT series of PET/CT. The neural network was highly specific for the diagnosis of vertebral height loss, but not sensitive. The use of the AI ensemble can help radiologists and nuclear medicine physicians to catch CT findings that might be overlooked.

Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning

Background

Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED.

Methods

This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S. institution. A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression.

Results

Overall ICC was 0.820 (95% CI 0.790–0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861–0.920) for the neural network and 0.936 (95% CI 0.918–0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906).

Conclusion

The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07617-7.

A Case Report of Brachiocephalic Vein Spasm Secondary to Peripherally Inserted Central Catheter

Vascular spasm is well known and studied in the arterial system. There are only a few cases reported related to central venous spasms. We present the case of a 63-year-old male with an extensive medical history, including deep vein thrombosis (DVT), who underwent peripheral insertion of a central catheter in his left upper extremity with subsequent development of left upper extremity edema. The central catheter was removed before the patient underwent a contrast-enhanced computed tomography of the chest which revealed severe narrowing of the left brachiocephalic vein, consistent with venospasm in the clinical setting. Nitroglycerin might be useful to prevent vasospasm, or it might also be used for treatment. In our case, the catheter was removed, and no subsequent treatment was necessary.

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.

Feasibility of Coronary CT Angiography-derived Left Ventricular Long-Axis Shortening as an Early Marker of Ventricular Dysfunction in Transcatheter Aortic Valve Replacement

Purpose

To evaluate the value of using left ventricular (LV) long-axis shortening (LAS) derived from coronary CT angiography (CCTA) to predict mortality in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR).

Materials and Methods

Patients with severe AS who underwent CCTA for preprocedural TAVR planning between September 2014 and December 2019 were included in this retrospective study. CCTA covered the whole cardiac cycle in 10% increments. Image series reconstructed at end systole and end diastole were used to measure LV-LAS. All-cause mortality within 24 months of follow-up after TAVR was recorded. Cox regression analysis was performed, and hazard ratios (HRs) are presented with 95% CIs. The C index was used to evaluate model performance, and the likelihood ratio χ² test was performed to compare nested models.

Results

The study included 175 patients (median age, 79 years [IQR, 73-85 years]; 92 men). The mortality rate was 22% (38 of 175). When adjusting for predictive clinical confounders, it was found that LV-LAS could be used independently to predict mortality (adjusted HR, 2.83 [95% CI: 1.13, 7.07]; P = .03). In another model using the Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM), LV-LAS remained significant (adjusted HR, 3.38 [95 CI: 1.48, 7.72]; P = .004), and its use improved the predictive value of the STS-PROM, increasing the STS-PROM C index from 0.64 to 0.71 (χ² = 29.9 vs 19.7, P = .001). In a subanalysis of patients with a normal LV ejection fraction (LVEF), the significance of LV-LAS persisted (adjusted HR, 3.98 [95 CI: 1.56, 10.17]; P = .004).

Conclusion

LV-LAS can be used independently to predict mortality in patients undergoing TAVR, including those with a normal LVEF.Keywords: CT Angiography, Transcatheter Aortic Valve Implantation/Replacement (TAVI/TAVR), Cardiac, Outcomes Analysis, Cardiomyopathies, Left Ventricle, Aortic Valve Supplemental material is available for this article. © RSNA, 2022See also the commentary by Everett and Leipsic in this issue.

CT in Transcatheter-delivered Treatment of Valvular Heart Disease

Minimally invasive strategies to treat valvular heart disease have emerged over the past 2 decades. The use of transcatheter aortic valve replacement in the treatment of severe aortic stenosis, for example, has recently expanded from high- to low-risk patients and became an alternative treatment for those with prohibitive surgical risk. With the increase in transcatheter strategies, multimodality imaging, including echocardiography, CT, fluoroscopy, and cardiac MRI, are used. Strategies for preprocedural imaging strategies vary depending on the targeted valve. Herein, an overview of preprocedural imaging strategies and their postprocessing approaches is provided, with a focus on CT. Transcatheter aortic valve replacement is reviewed, as well as less established minimally invasive treatments of the mitral and tricuspid valves. In addition, device-specific details and the goals of CT imaging are discussed. Future imaging developments, such as peri-procedural fusion imaging, machine learning for image processing, and mixed reality applications, are presented.

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.

Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients With Complex Lung Disease: A Noninferiority Study

OBJECTIVES

The aim of the study is to investigate the performance of artificial intelligence (AI) convolutional neural networks (CNN) in detecting lung nodules on chest computed tomography of patients with complex lung disease, and demonstrate its noninferiority when compared against an experienced radiologist through clinically relevant assessments.

METHODS

A CNN prototype was used to retrospectively evaluate 103 complex lung disease cases and 40 control cases without reported nodules. Computed tomography scans were blindly evaluated by an expert thoracic radiologist; a month after initial analyses, 20 positive cases were re-evaluated with the assistance of AI. For clinically relevant applications: (1) AI was asked to classify each patient into nodules present or absent and (2) AI results were compared against standard radiology reports. Standard statistics were performed to determine detection performance.

RESULTS

AI was, on average, 27 seconds faster than the expert and detected 8.4% of nodules that would have been missed. AI had a sensitivity of 67.7%, similar to an accuracy reported for experienced radiologists. AI correctly classified each patient (nodules present/absent) with a sensitivity of 96.1%. When matched against radiology reports, AI performed with a sensitivity of 89.4%. Control group assessment demonstrated an overall specificity of 82.5%. When aided by AI, the expert decreased the average assessment time per case from 2:44 minutes to 35.7 seconds, while reporting an overall increase in confidence.

CONCLUSION

In a group of patients with complex lung disease, the sensitivity of AI is similar to an experienced radiologist and the tool helps detect previously missed nodules. AI also helps experts analyze for lung nodules faster and more confidently, a feature that is beneficial to patients and favorable to hospitals due to increased patient load and need for shorter turnaround times.

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality

Rationale and Objectives

The burden of coronavirus disease 2019 (COVID-19) airspace opacities is time consuming and challenging to quantify on computed tomography. The purpose of this study was to evaluate the ability of a deep convolutional neural network (dCNN) to predict inpatient outcomes associated with COVID-19 pneumonia.

Materials and Methods

A previously trained dCNN was tested on an external validation cohort of 241 patients who presented to the emergency department and received a chest computed tomography scan, 93 with COVID-19 and 168 without. Airspace opacity scoring systems were defined by the extent of airspace opacity in each lobe, totaled across the entire lungs. Expert and dCNN scores were concurrently evaluated for interobserver agreement, while both dCNN identified airspace opacity scoring and raw opacity values were used in the prediction of COVID-19 diagnosis and inpatient outcomes.

Results

Interobserver agreement for airspace opacity scoring was 0.892 (95% CI 0.834-0.930). Probability of each outcome behaved as a logistic function of the opacity scoring (25% intensive care unit admission at score of 13/25, 25% intubation at 17/25, and 25% mortality at 20/25). Length of hospitalization, intensive care unit stay, and intubation were associated with larger airspace opacity score (p = 0.032, 0.039, 0.036, respectively).

Conclusion

The tested dCNN was highly predictive of inpatient outcomes, performs at a near expert level, and provides added value for clinicians in terms of prognostication and disease severity.

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.

•

Automated detection software can quantify lung metastases on initial staging chest CT in breast cancer patients.

•

AI-detected lung metastases number and max diameter on CT at initial cancer staging were strong predictors of mortality.

•

AI detection and segmentation tool contributes to accurate individualized prognostication in breast cancer patients.

Performance of an Artificial Intelligence-Based Platform Against Clinical Radiology Reports for the Evaluation of Noncontrast Chest CT

RATIONALE AND OBJECTIVES

Research on implementation of artificial intelligence (AI) in radiology workflows and its impact on reports remains scarce. In this study, we aim to assess if an AI platform would perform better than clinical radiology reports in evaluating noncontrast chest computed tomography (CT) scans.

MATERIALS AND METHODS

Consecutive patients who had undergone noncontrast chest CT were retrospectively identified. The radiology reports were reviewed in a binary fashion for reporting of pulmonary lesions, pulmonary emphysema, aortic dilatation, coronary artery calcifications (CAC), and vertebral compression fractures (VCF). CT scans were then processed using an AI platform. The reports' findings and the AI results were subsequently compared to a consensus read by two board-certificated radiologists as reference.

RESULTS

A total of 100 patients (mean age: 64.2 ± 14.8 years; 57% males) were included in this study. Aortic segmentation and calcium quantification failed to be processed by AI in 2 and 3 cases, respectively. AI showed superior diagnostic performance in identifying aortic dilatation (AI: sensitivity: 96.3%, specificity: 81.4%, AUC: 0.89) vs (Reports: sensitivity: 25.9%, specificity: 100%, AUC: 0.63), p <0.001; and CAC (AI: sensitivity: 89.8%, specificity: 100, AUC: 0.95) vs (Reports: sensitivity: 75.4%, specificity: 94.9%, AUC: 0.85), p = 0.005. Reports had better performance than AI in identifying pulmonary lesions (Reports: sensitivity: 97.6%, specificity: 100%, AUC: 0.99) vs (AI: sensitivity: 92.8%, specificity: 82.4%, AUC: 0.88), p = 0.024; and VCF (Reports: sensitivity:100%, specificity: 100%, AUC: 1.0) vs (AI: sensitivity: 100%, specificity: 63.7%, AUC: 0.82), p <0.001. A comparable diagnostic performance was noted in identifying pulmonary emphysema on AI (sensitivity: 80.6%, specificity: 66.7%. AUC: 0.74) and reports (sensitivity: 74.2%, specificity: 97.1%, AUC: 0.86), p = 0.064.

CONCLUSION

Our results demonstrate that incorporating AI support platforms into radiology workflows can provide significant added value to clinical radiology reporting.

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 ​mL/m². AI-LAVi was associated with new-onset atrial fibrillation (AUC 0.86; OR 1.12, 95% CI 1.08-1.18, p ​< ​0.001), HF hospitalization (AUC 0.90; OR 1.07, 95% CI 1.04-1.13, p ​< ​0.001), and MACCE (AUC 0.68; OR 1.04, 95% CI 1.01-1.07, p ​= ​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.

Role of Gender on the Outcomes of ST-Elevation Myocardial Infarction Patients Following Primary Coronary Angioplasty

Background There are considerable differences in the prevalence of coronary artery disease (CAD) and its cardiovascular risk factors between men and women. Due to the significance of gender as a factor that potentially affects cardiovascular disorders and patient outcomes, the present study aimed to assess the baseline characteristics and outcomes of CAD patients in terms of gender distribution. Methods All consecutive patients diagnosed with ST-elevation myocardial infarction (MI) who had undergone primary percutaneous coronary intervention (PCI) in the previous two years in a comprehensive cardiology center were included. Data were retrospectively collected from the hospital record files. Color Doppler echocardiography, valvular involvement, and the type of coronary vessel involvement were also evaluated. Results In total, 557 consecutive patients (437 men and 120 women) were included with a mean age of 59.37 ± 26.23 years and 64.07 ± 11.60 years for men and women, respectively (p = 0.004). The prevalence of mitral regurgitation (MR) and tricuspid regurgitation (TR) was significantly higher among women than men. Conclusion Female patients who suffered from CAD and underwent PCI were older than men. Also, ischemic mitral regurgitation (MR) and tricuspid regurgitation (TR) were more prevalent among women, while smoking was more prevalent among men.

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.

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.

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.

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.

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.

Development of a Device-to-Image Registration Free Needle Guide for Magnetic Resonance Imaging-Guided Targeted Prostate Biopsy

Significant research has been done in the past decade for the development of magnetic resonance imaging (MRI) guided needle guide (NG) systems for prostate intervention. Most of these systems have been restricted to application in the lab environment with lack of progress toward clinical application. Bulky and complex designs can be attributed to this practice. These systems also demand complex technical setup and usage procedures, which require extra technical personnel during the intervention in addition to specialized training for physicians. Moreover, “device-to-image” registration, essential for accurate and precise targeting, further complicates the overall process while increasing total time for intervention. In order to address these limitations, a simplified, MRI-guided, transperineal prostate biopsy NG system was designed and developed for rapid adoption into the clinical environment. The system consists of a NG device and a software toolkit. It does not require any special intraprocedural technical expertise or dedicated training. Also, to simplify and shorten total procedure time, the device uses the unique concept of “fixed coordinate device” eliminating the need for any device-to-image registration making it clinically friendly. To verify the NG design along with the registration free feature, image quality tests and agar phantom-based targeting experiments were performed under the guidance of 3T MRI scanner. The imaging tests resulted in a distortion of less than 1% in presence of the device and an average change of 1.3% in signal-to-noise ratio. For targeting experiments, maximum in-plane error distance of 3.8 mm with a mean of 2.2 mm and standard deviation of 0.8 mm was observed. The results show that an MRI-compatible simplified intervention device without the need of device-to-image registration is technically feasible.

Fat-Finding Mission: Primary Pleomorphic Liposarcoma of the Heart and Pericardium

Primary cardiac liposarcomas are rare tumors with a poor prognosis and no well-defined imaging characteristics or treatment guidelines. Here, we present a case of primary pleomorphic liposarcoma of the heart and pericardium with multimodality imaging findings and our institution’s treatment approach. (Level of Difficulty: Intermediate.)

Machine Learning and Coronary Artery Calcium Scoring

PURPOSE OF REVIEW

To summarize current artificial intelligence (AI)-based applications for coronary artery calcium scoring (CACS) and their potential clinical impact.

RECENT FINDINGS

Recent evolution of AI-based technologies in medical imaging has accelerated progress in CACS performed in diverse types of CT examinations, providing promising results for future clinical application in this field. CACS plays a key role in risk stratification of coronary artery disease (CAD) and patient management. Recent emergence of AI algorithms, particularly deep learning (DL)-based applications, have provided considerable progress in CACS. Many investigations have focused on the clinical role of DL models in CACS and showed excellent agreement between those algorithms and manual scoring, not only in dedicated coronary calcium CT but also in coronary CT angiography (CCTA), low-dose chest CT, and standard chest CT. Therefore, the potential of AI-based CACS may become more influential in the future.

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.

Marijuana use and coronary artery disease in young adults

Background

Marijuana is the most popular drug of abuse in the United States. The association between its use and coronary artery disease has not yet been fully elucidated. This study aims to determine the frequency of coronary artery disease among young to middle aged adults presenting with chest pain who currently use marijuana as compared to nonusers.

Methods

In this retrospective study, 1,420 patients with chest pain or angina equivalent were studied. Only men between 18 and 40 years and women between 18 and 50 years of age without history of cardiac disease were included. All patients were queried about current or prior cannabis use and underwent coronary CT angiography. Each coronary artery on coronary CT angiography was assessed based on the CAD-RADS reporting system.

Results

A total of 146 (10.3%) out of 1,420 patients with chest pain were identified as marijuana users. Only 6.8% of the 146 marijuana users had evidence of coronary artery disease on coronary CT angiography. In comparison, the rate was 15.0% among the 1,274 marijuana nonusers (p = 0.008). After accounting for other cardiac risk factors in a multivariate analysis, the negative association between marijuana use and coronary artery disease on coronary CT angiography diminished (p = 0.12, 95% CI 0.299–1.15). A majority of marijuana users were younger than nonusers and had a lower frequency of hypertension and diabetes than nonusers. There was no statistical difference in lipid panel values between the two groups. Only 2 out of 10 marijuana users with coronary artery disease on coronary CT angiography had hemodynamically significant stenosis.

Conclusion

Among younger patients being evaluated for chest pain, self-reported cannabis use conferred no additional risk of coronary artery disease as detected on coronary CT angiography.

Left-sided Inferior Vena Cava with Hemiazygos Continuation to Left Superior Vena Cava

Congenital anomalies of superior (SVC) and inferior vena cava (IVC) are not uncommon and usually incidentally recognized. The normal embryogenesis is a complex process involving the formation of several anastomoses. Failure of certain vessels to develop or regress results in numerous caval variations and anomalies. Although these are usually without significant clinical implications, awareness of these anomalies is necessary to avoid diagnostic pitfalls and suggest the presence of other abnormalities and for the planning of vascular intervention or surgery. We present a very rare, caval anomaly, a left-sided IVC with hemiazygos continuation to left SVC in the absence of right SVC.

The Association between Transcatheter Aortic Valve Replacement (TAVR) Approach and New-Onset Bundle Branch Blocks

INTRODUCTION

Transcatheter aortic valve replacement (TAVR) has become a widely accepted treatment option for patients with severe aortic stenosis (AS) who are considered intermediate- and high-risk surgical candidates. The purpose of this study was to test the hypothesis that trans-apical TAVR would be associated with increased risk of new-onset intraventricular conduction delay (LBBB or RBBB).

METHODS

We conducted a retrospective observational study of consecutive patients undergoing TAVR at a large, single institution. The incidence of new LBBB or RBBB was compared between femoral and apical TAVR patients. Multivariate analysis was performed to account for confounding variables, which included age, gender, CAD, PAD, hypertension, and diabetes.

RESULTS

A total of 467 TAVR patients were included in the study, with 283 (60.6%) femoral approach and 184 (39.4%) apical approach. In univariate analysis, the apical approach (when compared to the femoral approach) was associated with a higher incidence of both new-onset LBBB (12.79 vs. 3.40%, p = 0.0002) and RBBB (5.49 vs. 0.81%, p = 0.0039). After controlling for potential confounding variables, the apical approach continued to be associated with a higher incidence of both new-onset LBBB (p = 0.0010) and RBBB (p = 0.0115). There was also a trend towards an association between diabetes and new-onset LBBB (p = 0.0513) in apical TAVR patients. In subgroup analysis, LBBB/RBBB occurring as a result of transapical TAVR was associated with more frequent hospitalizations > 30 days after TAVR, compared to transfemoral TAVR. Other post-procedural complications noted more frequently among patients undergoing transapical TAVR include arrhythmias including atrial fibrillation, peri-procedural myocardial infarction (within 72 h), mortality from unknown cause, and mortality from non-cardiac cause.

CONCLUSIONS

Relative to transfemoral TAVR, patients undergoing transapical TAVR are at increased risk for new-onset bundle branch block, peri-procedural myocardial infarction, rehospitalization, TAV-in-TAV deployment, and all-cause mortality at 1 year. Interventional cardiologists and cardiothoracic surgeons alike should take these findings into consideration when choosing which approach is most suitable for patients undergoing TAVR for severe aortic stenosis.

Anomalous Coronary Anatomy with Fistula Diagnosed on Coronary Computed Tomography Angiography

Anomalous coronary vasculature is a rare finding among the general population. Identifying such cases is important for preventing adverse outcomes such as sudden cardiac death. We present two rare cases of aberrant coronary anatomy. In Case 1, a 4-year-old male who presented with non-exertional chest pain was found to have anomalous coronary architecture on echocardiogram. Coronary computed tomography angiogram (CCTA) confirmed an anomalous origin of the left coronary artery from the right coronary sinus with a malignant interarterial course and myocardial bridging of the left anterior descending (LAD) artery. The patient underwent a successful surgical correction of the defects. In Case 2, a full-term infant female was born with a hypoplastic right ventricle and pulmonary atresia. CCTA showed a large fistula originating from the coronary sinus on the left that drained into the superior aspect of the mid right ventricular cavity, an anomalous bridge between the left and right atrial appendages, and five fistulous connections between various vessels. The patient was transferred to another facility for cardiac transplant.

A Case of Supernumerary Kidney

A supernumerary kidney is extremely rare, with less than 100 cases currently reported in the literature. When this variant is present, the additional renal parenchyma demonstrates its own collecting system, vascular supply, and distinct encapsulated parenchyma. Herein, we discuss the case of a supernumerary kidney in a 20-year-old male.

Funny Lumps, Flaming Pheo, and a Broken Heart: A Rare Case of Pheochromocytoma

Pheochromocytoma is the underlying etiology in 0.1% of hypertensive cases. However, it may be present in up to 5.7% of patients with neurofibromatosis I (NF1). The burst of catecholamines inherent in pheochromocytoma has significant effects on the mechanical and electrical activity of the myocardium. Different theories have been postulated for myocardial stunning in patients with pheochromocytoma that include microvascular spasm, impaired fatty acid metabolism, increased production of oxygen-derived free radicals and dynamic left ventricular mid-cavity obstruction. QT interval prolongation is seen in 16% to 35% of patients with pheochromocytoma. Takotsubo cardiomyopathy (TS) is now being increasingly identified and it may be responsible for up to 40% of cases of acute catecholamine cardiomyopathy. These manifestations may sometimes precede or cloud the typical triad of a headache, sweating, and tachycardia.

We herein present a case of a 42-year-old female with a unique combination of QT prolongation, torsades de pointes, and TS caused by pheochromocytoma in the background of NF1. All these complications are potentially reversible with the removal of the underlying adrenal tumor, underscoring the importance of a high suspicion for pheochromocytoma in patients with NF1.

Complex Vascular Ring Diagnosed on Cardiovascular MR in a 3-Day-Old Infant

Prenatal ultrasonography in the early third trimester showed an unusual branching pattern of the right aortic arch. Echocardiography performed 4 h after birth showed the right aortic arch with mirror-image branching, patent ductus arteriosus, and patent foramen ovale. Because the location of the ductus arteriosus was unclear on echocardiography, cardiovascular magnetic resonance imaging was performed 3 days after birth. Advanced techniques including contrast-enhanced time-resolved magnetic resonance angiography and 3D time-of-flight magnetic resonance angiography allowed accurate diagnosis of a vascular ring comprising ascending and descending aorta, right aortic arch with mirror-image branching, and diverticulum of Kommerell giving rise to a left ligamentum arteriosum. The infant had hiccups, but no other symptoms. The esophagram was negative for obstruction. The infant was closely monitored; however, she developed esophageal obstruction at 7 months of age because of the vascular ring. She underwent lysis of the left ligamentum arteriosum followed by aortopexy for relief of esophageal obstruction. This report shows the utility of neonatal cardiovascular magnetic resonance imaging to evaluate complex congenital aortic arch anomalies.

A Novel Imaging Finding in Williams Syndrome: The Coral Sign

A 16-year-old female, with a history of Williams syndrome, presented to our institution with a 2-week history of intermittent dizziness. Holter monitoring demonstrated occasional premature ventricular contractions with rare couplets and triplets as well as one short run of nonsustained ventricular tachycardia. Echocardiography revealed an abnormal and irregular left ventricular septum with multiple mobile, pedunculated muscular projections extending into the left ventricular cavity. Cardiac MR confirmed abnormally thickened trabeculations consisting of multiple parallel ridges of myocardium crossing the left ventricle. The appearance of these findings closely resembled bands of coral lining the ocean floor. As such, this finding can henceforth be known as the "coral sign." To our knowledge, no other reports of this finding in patients with Williams syndrome have been published.

Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks

Deep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the development of computer-aided detection and diagnosis (CAD) systems. These technologies have long been thought of as "second-opinion" tools for radiologists and clinicians. However, with significant improvements in deep neural networks, the diagnostic capabilities of learning algorithms are approaching levels of human expertise (radiologists, clinicians etc.), shifting the CAD paradigm from a "second opinion" tool to a more collaborative utility. This paper reviews recently developed CAD systems based on deep learning technologies for breast cancer diagnosis, explains their superiorities with respect to previously established systems, defines the methodologies behind the improved achievements including algorithmic developments, and describes remaining challenges in breast cancer screening and diagnosis. We also discuss possible future directions for new CAD models that continue to change as artificial intelligence algorithms evolve.

Invasive Cardiac Lipoma: a case report and review of literature

Background

Cardiac lipomas are rare benign tumors of the heart. They are usually asymptomatic and are thus most often diagnosed on autopsies. Symptoms, when present, depend upon the location within the heart. Typical locations are the endocardium of the right atrium and the left ventricle. Diagnostic modality of choice is cardiac MRI. Treatment guidelines have not yet been established due to the very low prevalence of these tumors and are thus guided by the patient’s symptomatology.

Case presentation

We describe a case of an invasive cardiac lipoma, wherein the initial symptom of the patient was shortness of breath. Although the echocardiogram visualized the tumor in the right atrium, a cardiac MRI was performed for better tissue characterization. The MRI revealed a large, fat containing, septated mass in the right atrium with invasion into the inter-atrial septum and inferior cavoatrial junction. There was also invasion of the coronary sinus along the inferior and left lateral aspect of the posterior atrioventricular groove. Although the mass appeared to represent a lipoma by imaging characteristics, the unusual extension into the coronary sinus led to consideration of a low-grade liposarcoma in the differential. Thus a pre-operative biopsy was performed along with MDM2 gene amplification to rule out a liposarcoma preceding surgical excision.

Conclusion

Cardiac lipomas are well-characterized on cardiac MRI, which is the diagnostic modality of choice. Typical locations are the right atrium and the left ventricle. However, in those with atypical features such as invasion of the coronary sinus, pre-operative biopsy for histopathologic confirmation is imperative to exclude well-differentiated liposarcoma. Our patient with a simple lipoma underwent partial resection to relieve symptoms. We discuss prognosis and treatment of symptomatic cardiac lipomas.

CT-guided Native Medical Renal Biopsy: Cortical Tangential versus Non-Tangential Approaches-A Comparison of Efficacy and Safety

Purpose To review a single-center experience with the cortical tangential approach during computed tomography (CT)-guided native medical renal biopsy and to evaluate its efficacy and safety compared with those of a non-cortical tangential approach. Materials and Methods This retrospective study received institutional review board approval, with a waiver of the HIPAA requirement for informed consent. The number of cores, glomeruli, and complications were reviewed in 431 CT-guided medical renal biopsies performed between July 2007 and September 2015. A biopsy followed a cortical tangential approach if the needle path was parallel to the renal cortical surface, at a depth closer to the renal capsule than the renal pelvic fat. A sample was considered adequate if the biopsy yielded at least 10 glomeruli at light microscopy, one glomerulus at immunofluorescence microscopy, and one glomerulus at electron microscopy. The χ² test, the t test, the Mann-Whitney test, and logistic regression modeling of sample adequacy were performed. Results One hundred fifty-six (36%) of 431 biopsies were performed with the cortical tangential approach. More cores were obtained for the cortical tangential group (2.6 vs 2.4, P = .001); biopsy needle gauge was not significantly different (P = .076). More adequate samples were obtained in the cortical tangential group (66.7% vs 49.8%, P = .001), with more glomeruli (23 vs 16, P = .014). Results were significant after controlling for needle gauge and number of cores (P = .008). The cortical tangential group had fewer complications (1.9% vs 7.3%, P = .018). Conclusion The cortical tangential approach, when applied to CT-guided native medical renal biopsies, results in higher rates of sample adequacy and lower rates of postprocedural complications. ^© RSNA, 2016.

Cardiac MR findings and potential diagnostic pitfalls in patients evaluated for arrhythmogenic right ventricular cardiomyopathy

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a familial cardiomyopathy characterized by fibrofatty replacement of the myocardium, ventricular tachycardia, and ventricular dysfunction that affects primarily the right ventricle (RV). This disease is not common but can be seen more frequently in young adults, and clinical manifestations range from no symptoms to lethal arrhythmia and sudden death. The diagnosis of ARVC is challenging and is based on the recently revised international task force criteria. Given the strengths of cardiac magnetic resonance (MR) imaging for depicting the RV, this modality plays an important role in the diagnosis of ARVC. Functional and structural abnormalities of the RV depicted with cardiac MR imaging constitute major and minor criteria in the revised task force criteria. Since the ARVC program was established at our center in 1998, there has been an increased awareness of a number of normal variants that are commonly misinterpreted as showing evidence for ARVC. On the basis of our clinical experience, the overdiagnosis of ARVC appears to reflect two fundamental problems: (a) a lack of awareness of diagnostic criteria that identify major and minor variables to be used for the diagnosis of ARVC, and (b) a lack of familiarity with the normal variants and mimics that may be misinterpreted as showing evidence of ARVC. The purpose of this article is to review the typical patterns of ventricular involvement in ARVC at cardiac MR imaging and to compare those with the patterns of normal variants and other diseases that can mimic ARVC. Online supplemental material is available for this article.

Myocardial T1 mapping: techniques and potential applications

Myocardial fibrosis is a common endpoint in a variety of cardiac diseases and a major independent predictor of adverse cardiac outcomes. Short of histopathologic analysis, which is limited by sampling bias, most diagnostic modalities are limited in their depiction of myocardial fibrosis. Cardiac magnetic resonance (MR) imaging has the advantage of providing detailed soft-tissue characterization, and a variety of novel quantification methods have further improved its usefulness. Contrast material-enhanced cardiac MR imaging depends on differences in signal intensity between regions of scarring and adjacent normal myocardium. Diffuse myocardial fibrosis lacks these differences in signal intensity. Measurement of myocardial T1 times (T1 mapping) with gadolinium-enhanced inversion recovery-prepared sequences may depict diffuse myocardial fibrosis and has good correlation with ex vivo fibrosis content. T1 mapping calculates myocardial T1 relaxation times with image-based signal intensities and may be performed with standard cardiac MR imagers and radiologic workstations. Myocardium with diffuse fibrosis has greater retention of contrast material, resulting in T1 times that are shorter than those in normal myocardium. Early studies have suggested that diffuse myocardial fibrosis may be distinguished from normal myocardium with T1 mapping. Large multicenter studies are needed to define the role of T1 mapping in developing prognoses and therapeutic assessments. However, given its strengths as a noninvasive method for direct quantification of myocardial fibrosis, T1 mapping may eventually play an important role in the management of cardiac disease.

Incidental pulmonary nodules on cardiac computed tomography: prognosis and use

BACKGROUND

Small asymptomatic lung nodules are found frequently in the course of cardiac computed tomography (CT) scanning. However, the utility of assessing and reporting incidental findings in healthy, asymptomatic subjects is unknown.

METHODS

The sample comprised 1023 60- to 69-year-old subjects free of clinical cardiovascular disease and cancer who participated in the Atherosclerotic Disease, VAscular functioN and genetiC Epidemiology Study. All subjects underwent cardiac CT for determination of coronary calcium between 2001 and 2004, and the first 459 subjects were assessed for incidental pulmonary findings. We used health plan clinical databases to ascertain 24-month health care use and clinical outcomes.

RESULTS

Noncalcified pulmonary nodules were reported in 81 of 459 subjects (18%). Chest CT was performed on 78% of participants in the 24 months after notification, compared with 2.5% in the previous 24 months. Chest x-ray use increased from 28% to 49%. The mean number of chest CT scans per subject was 1.3 (range, 0-5). Although no malignant lesions were diagnosed in the group who had pulmonary findings read, 1 lung cancer case was diagnosed in the group who did not have lung findings read. Among the 63 participants followed up by CT, the original lesion was not identified in 22 participants (35%), the lesion had decreased or remained stable in 39 participants (62%), and there was interval growth in 2 participants (3%).

CONCLUSION

Reporting noncalcified pulmonary nodules resulted in substantial rescanning that overwhelmingly revealed resolution or stability of pulmonary nodules, arguing for benign processes.

Post-harvest storage control of mango stem-end rot with fungicidal dips

Stem-end rot, Lasiodiplodia theobromae (Pat.) Griff. and Maubl. was controlled in harvested mango fruit for up to 4 weeks by fungicidal dipping of pared fruit followed by storage at 13�C. Stem-end rot was significantly (P< 0.05) reduced by a 0.025% prochloraz dip at an ambient water temperature of 31�C or by a 0.05% benomyl dip at 50�C water temperature, com pared with ambient water dipping at 31�C. A hot water dip at 50�C, or 0.1% thiabendazole at 50�C water tem perature, did not significantly control stem-end rot.