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.

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 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.

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.

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.

Impact of Artificial Intelligence Assistance on Chest CT Interpretation Times: A Prospective Randomized Study.

BACKGROUND. Deep learning-based convolutional neural networks have enabled major advances in development of artificial intelligence (AI) software applications. Modern AI applications offer comprehensive multiorgan evaluation. OBJECTIVE. The purpose of this article was to evaluate the impact of an automated AI platform integrated into clinical workflow for chest CT interpretation on radiologists' interpretation times when evaluated in a real-world clinical setting. METHODS. In this prospective single-center study, a commercial AI software solution was integrated into clinical workflow for chest CT interpretation. The software provided automated analysis of cardiac, pulmonary, and musculoskeletal findings, including labeling, segmenting, and measuring normal structures as well as detecting, labeling, and measuring abnormalities. AI-annotated images and autogenerated summary results were stored in the PACS and available to interpreting radiologists. A total of 390 patients (204 women, 186 men; mean age, 62.8 ± 13.3 [SD] years) who underwent out-patient chest CT between January 19, 2021, and January 28, 2021, were included. Scans were randomized using 1:1 allocation between AI-assisted and non-AI-assisted arms and were clinically interpreted by one of three cardiothoracic radiologists (65 scans per arm per radiologist; total of 195 scans per arm) who recorded interpretation times using a stopwatch. Findings were categorized according to review of report impressions. Interpretation times were compared between arms. RESULTS. Mean interpretation times were significantly shorter in the AI-assisted than in the non-AI-assisted arm for all three readers (289 ± 89 vs 344 ± 129 seconds, p < .001; 449 ± 110 vs 649 ± 82 seconds, p < .001; 281 ± 114 vs 348 ± 93 seconds, p = .01) and for readers combined (328 ± 122 vs 421 ± 175 seconds, p < .001). For readers combined, the mean difference was 93 seconds (95% CI, 63-123 seconds), corresponding with a 22.1% reduction in the AI-assisted arm. Mean interpretation time was also shorter in the AI-assisted arm compared with the non-AI-assisted arm for contrast-enhanced scans (83 seconds), noncontrast scans (104 seconds), negative scans (84 seconds), positive scans without significant new findings (117 seconds), and positive scans with significant new findings (92 seconds). CONCLUSION. Cardiothoracic radiologists exhibited a 22.1% reduction in chest CT interpretations times when they had access to results from an automated AI support platform during real-world clinical practice. CLINICAL IMPACT. Integration of the AI support platform into clinical workflow improved radiologist efficiency.

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.

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.

Automated Dual-energy Computed Tomography-based Extracellular Volume Estimation for Myocardial Characterization in Patients With Ischemic and Nonischemic Cardiomyopathy.

OBJECTIVES: We aimed to validate and test a prototype algorithm for automated dual-energy computed tomography (DECT)-based myocardial extracellular volume (ECV) assessment in patients with various cardiomyopathies. METHODS: This retrospective study included healthy subjects (n=9; 61±10 y) and patients with cardiomyopathy (n=109, including a validation cohort n=60; 68±9 y; and a test cohort n=49; 69±11 y), who had previously undergone cardiac DECT. Myocardial ECV was calculated using a prototype-based fully automated algorithm and compared with manual assessment. Receiver-operating characteristic analysis was performed to test the algorithm's ability to distinguish healthy subjects and patients with cardiomyopathy. RESULTS: The fully automated method led to a significant reduction of postprocessing time compared with manual assessment (2.2±0.4 min and 9.4±0.7 min, respectively, P <0.001). There was no significant difference in ECV between the automated and manual methods ( P =0.088). The automated method showed moderate correlation and agreement with the manual technique ( r =0.68, intraclass correlation coefficient=0.66). ECV was significantly higher in patients with cardiomyopathy compared with healthy subjects, regardless of the method used ( P <0.001). In the test cohort, the automated method yielded an area under the curve of 0.98 for identifying patients with cardiomyopathies. CONCLUSION: Automated ECV estimation based on DECT showed moderate agreement with the manual method and matched with previously reported ECV values for healthy volunteers and patients with cardiomyopathy. The automatically derived ECV demonstrated an excellent diagnostic performance to discriminate between healthy and diseased myocardium, suggesting that it could be an effective initial screening tool while significantly reducing the time of assessment.

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.

Computed tomographic assessment of right ventricular long axis strain for prognosis after transcatheter aortic valve replacement.

OBJECTIVES: To investigate the predictive value of right ventricular long axis strain (RV-LAS) derived by cardiac computed tomography angiography (CCTA) for mortality in patients with aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). METHODS: We retrospectively included patients with severe AS undergoing TAVR (n = 168, median 79 years). Parameters of RV function including RV-LAS and RV ejection fraction (RVEF) were assessed using pre-procedural systolic and diastolic CCTA series. The tricuspid annulus diameter (TAD) and diameter of the main pulmonary artery (mPA) were also assessed. All-cause mortality was recorded post-TAVR. Cox regression was used and results are presented with hazard ratio (HR) and 95% confidence interval (CI). Harrell's c-index was used to assess the performance of different models and the likelihood ratio test was used to compare nested models. RESULTS: Thirty-eight deaths (22.6%) occurred over a median follow-up of 21 months. RV-LAS > -11.42% (HR 2.86, 95% CI 1.44-5.67, p = 0.003), LVEF (HR 0.98, 95% CI 0.96-0.996; p = 0.02), TAD (HR 1.05, 95% CI 1.01-1.10, p = 0.02) and mPA diameter (HR 1.09, 95% CI 1.02-1.16, p = 0.01) were associated with mortality on univariable analysis. In a multivariable model, only RV-LAS (HR 2.36, 95% CI 1.04-5.36, p = 0.04) remained as an independent predictor of all-cause mortality. RV-LAS significantly improved the predictive power of the Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) (c-index 0.700 vs 0.637; p = 0.01). CONCLUSION: RV-LAS was an independent predictor of all-cause mortality in patients with severe AS undergoing TAVR, outperformed anatomical markers such as TAD and mPA diameter, and could potentially improve the current risk-stratifying tool.

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.

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.

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.

Utility of Functional and Volumetric Left Atrial Parameters Derived From Preprocedural Cardiac CTA in Predicting Mortality After Transcatheter Aortic Valve Replacement.

BACKGROUND. Cardiac CTA is required for preprocedural workup before transcatheter aortic valve replacement (TAVR) and can be used to assess functional parameters of the left atrium (LA). OBJECTIVE. We aimed to evaluate the utility of functional and volumetric LA parameters derived from cardiac CTA to predict mortality in patients with severe aortic stenosis (AS) undergoing TAVR. METHODS. This retrospective study included 175 patients with severe AS (92 men, 83 women; median age, 79.0 years) who underwent cardiac CTA for clinical pre-TAVR assessment. A postdoctoral research fellow calculated maximum and minimum LA volumes using biplane area-length measurements; these values were indexed to body surface area, and maximum and minimum LA volume index (LAVImax and LAVImin, respectively) values were calculated. The LA emptying fraction (LAEF) was automatically calculated. All-cause mortality within a 24-month follow-up period after TAVR was recorded. To identify parameters predictive of mortality, Cox regression analysis was performed, and results were summarized by hazard ratio (HR) and 95% CI. The Harrell C-index was used to assess model performance. A radiology resident repeated the measurements in a random sample of 20% (n = 35) of the cases, and interobserver agreement was computed using the intraclass correlation coefficient (ICC). RESULTS. Thirty-eight deaths (21.7%) were recorded within a median follow-up of 21 months. LAVImax (HR, 1.02 [95% CI, 1.01-1.04]; p = .01), LAVImin (HR, 1.02 [95% CI, 1.01-1.04]; p < .001), and LAEF (HR, 0.97 [95% CI, 0.95-0.99]; p = .002) were predictive of mortality in univariable analysis. After adjusting for clinical parameters, only LAEF (HR, 0.97 [95% CI, 0.94-0.99]; p = .02) independently predicted mortality. The C-index of the Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) significantly increased from 0.636 to 0.683, 0.694, and 0.700 when incorporating into the model LAVImax, LAVImin, and LAEF, respectively. The ICC for maximum and minimum LA volumes and LAEF ranged from 0.94 to 0.99. CONCLUSION. LAEF derived from preprocedural cardiac CTA independently predicts mortality in patients with severe AS undergoing TAVR. CLINICAL IMPACT. Cardiac CTA-derived LA function, evaluated during pre-TAVR workup, can be used to assess preprocedural risk and may improve risk stratification in post-TAVR surveillance.

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.

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.