Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT.

This study evaluated a deep neural network (DNN) algorithm for automated aortic diameter quantification and aortic dissection detection in chest computed tomography (CT). A total of 100 patients (median age: 67.0 [interquartile range 55.3/73.0] years; 60.0% male) with aortic aneurysm who underwent non-enhanced and contrast-enhanced electrocardiogram-gated chest CT were evaluated. All the DNN measurements were compared to manual assessment, overall and between the following subgroups: (1) ascending (AA) vs. descending aorta (DA); (2) non-obese vs. obese; (3) without vs. with aortic repair; (4) without vs. with aortic dissection. Furthermore, the presence of aortic dissection was determined (yes/no decision). The automated and manual diameters differed significantly (p < 0.05) but showed excellent correlation and agreement (r = 0.89; ICC = 0.94). The automated and manual values were similar in the AA group but significantly different in the DA group (p < 0.05), similar in obese but significantly different in non-obese patients (p < 0.05) and similar in patients without aortic repair or dissection but significantly different in cases with such pathological conditions (p < 0.05). However, in all the subgroups, the automated diameters showed strong correlation and agreement with the manual values (r > 0.84; ICC > 0.9). The accuracy, sensitivity and specificity of DNN-based aortic dissection detection were 92.1%, 88.1% and 95.7%, respectively. This DNN-based algorithm enabled accurate quantification of the largest aortic diameter and detection of aortic dissection in a heterogenous patient population with various aortic pathologies. This has the potential to enhance radiologists' efficiency in clinical practice.

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.

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.

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.

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.

Comparison of 2D and 3D quiescent-interval slice-selective non-contrast MR angiography in patients with peripheral artery disease.

OBJECTIVE: To evaluate the potential clinical benefit of the superior spatial resolution of 3D prototype thin-slab stack-of-stars (tsSOS) quiescent-interval slice-selective (QISS) MRA over standard 2D-QISS MRA for the detection peripheral artery disease (PAD), using computed tomography angiography (CTA) as reference. MATERIALS AND METHODS: Twenty-three patients (70 ± 8 years, 18 men) with PAD who had previously undergone run-off CTA were prospectively enrolled. Patients underwent non-contrast MRA using 2D-QISS and tsSOS-QISS at 1.5 T. Eighteen arterial segments were evaluated for subjective and objective image quality (normalized signal-to-noise, nSNR), vessel sharpness, and area under the curve (AUC) for > 50% stenosis detection. RESULTS: Overall subjective image quality ratings for the entire run-off were not different between tsSOS-QISS and 2D-QISS (3 [3; 4] vs 4 [3; 4], respectively; P = 0.813). Sharpness of primary branch vessels demonstrated improved image quality using tsSOS-QISS compared with 2D-QISS (4 [3; 4] vs 3 [2; 3], P = 0.008). Objective image quality measures were not different between 2D-QISS and tsSOS-QISS (nSNR 5.0 ± 1.9 vs 4.2 ± 1.8; P = 0.132). AUCs for significant stenosis detection by tsSOS-QISS and 2D-QISS were 0.877 and 0.856, respectively (P = 0.336). DISCUSSION: The prototype 3D tsSOS-QISS technique provides similar accuracy in patients with PAD to a standard commercially available 2D-QISS technique, indicating that the use of relatively thick slices does not limit the diagnostic performance of 2D-QISS. However, subjective image quality for branch vessel depiction is improved using the 3D approach.

Assessment of Intramyocardial Fat Content Using Computed Tomography: Is There a Relationship With Obesity?

BACKGROUND: Fat deposition in the liver and the skeletal muscle are linked to cardiovascular risk factors. Fat content in tissues can be estimated by measuring attenuation on noncontrast computed tomography (CT). Quantifying intramyocardial fat content is of interest as it may be related to myocardial dysfunction or development of heart failure. We hypothesized that myocardial fat content would correlate with severity of obesity, liver fat, and components of the metabolic syndrome. METHODS: We measured attenuation values on 121 noncontrast CT scans from the spleen, liver, erector spinae muscle, and myocardial septum. A chart review was performed for patient demographics and clinical characteristics. We tested for correlations between attenuation values in each of the tissues and various clinical parameters. RESULTS: We studied 78 females and 43 males, with a mean age of 54.5±11.2 years. Weak, but significant inverse Spearman correlation between body mass index and attenuation values were found in the liver (ρ=-0.228, P=0.012), spleen (ρ=-0.225, P=0.017), and erector spinae muscle (ρ=-0.211, P=0.022) but not in the myocardial septum (ρ=0.012, P=0.897). Mean attenuation in the nonobese group versus obese group (body mass index >30 kg/m2) were 41.1±5.0 versus 42.3±6.9 (P=0.270) in myocardial septum, 56.1±8.7 versus 51.7±10.9 (P=0.016) in the liver, 43.9±8.9 versus 40.1±10.4 (P=0.043) in the spleen, and 41.7±8.3 versus 39.0±8.8 (P=0.087) in the erector spinae muscle. CONCLUSIONS: Although CT is a theoretically appealing modality to assess fat content of the myocardium, we did not find a relationship between myocardial CT attenuation and obesity, or other cardiovascular risk factors. These findings suggest that the degree of myocardial fat accumulation in obesity or metabolic syndrome is too small to be detected with this modality.

Compressed sensing acceleration of cardiac cine imaging allows reliable and reproducible assessment of volumetric and functional parameters of the left and right atrium.

OBJECTIVES: To compare volumetric and functional parameters of the atria derived from highly accelerated compressed sensing (CS)-based cine sequences in comparison to conventional (Conv) cine imaging. METHODS: CS and Conv cine sequences were acquired in 101 subjects (82 healthy volunteers (HV) and 19 patients with heart failure with reduced ejection fraction (HFrEF)) using a 3T MR scanner in this single-center study. Time-volume analysis of the left (LA) and right atria (RA) were performed in both sequences to evaluate atrial volumes and function (total, passive, and active emptying fraction). Inter-sequence and inter- and intra-reader agreement were analyzed using correlation, intraclass correlation (ICC), and Bland-Altman analysis. RESULTS: CS-based cine imaging led to a 69% reduction of acquisition time. There was significant difference in atrial parameters between CS and Conv cine, e.g., LA minimal volume (LAVmin) (Conv 24.0 ml (16.7-32.7), CS 25.7 ml (19.2-35.2), p < 0.0001) or passive emptying fraction (PEF) (Conv 53.9% (46.7-58.4), CS 49.0% (42.0-54.1), p < 0.0001). However, there was high correlation between the techniques, yielding good to excellent ICC (0.76-0.99) and small mean of differences in Bland-Altman analysis (e.g. LAVmin - 2.0 ml, PEF 3.3%). Measurements showed high inter- (ICC > 0.958) and intra-rater (ICC > 0.934) agreement for both techniques. CS-based parameters (PEF AUC = 0.965, LAVmin AUC = 0.864) showed equivalent diagnostic ability compared to Conv cine imaging (PEF AUC = 0.989, LAVmin AUC = 0.859) to differentiate between HV and HFrEF. CONCLUSION: Atrial volumetric and functional evaluation using CS cine imaging is feasible with relevant reduction of acquisition time, therefore strengthening the role of CS in clinical CMR for atrial imaging. KEY POINTS: • Reliable assessment of atrial volumes and function based on compressed sensing cine imaging is feasible. • Compressed sensing reduces scan time and has the potential to overcome obstacles of conventional cine imaging. • No significant differences for subjective image quality, inter- and intra-rater agreement, and ability to differentiate healthy volunteers and heart failure patients were detected between conventional and compressed sensing cine imaging.

Radiomics: The Next Frontier of Cardiac Computed Tomography.

Radiomics uses advanced image analysis to extract massive amounts of quantitative information from digital images, which is not otherwise distinguishable to the human eye. The mined data can be used to explore and establish new and undiscovered correlations between these imaging features and clinical end points. Cardiac computed tomography (CT) is a first-line imaging modality for evaluating coronary artery disease and has a primary role in the assessment of cardiac structures. Conventional interpretation of cardiac CT images relies mostly on subjective and qualitative analysis, as well as basic geometric quantification. To date, some proof-of-concept studies have demonstrated the feasibility and diagnostic performance of cardiac CT radiomics analysis. This review describes the current literature on radiomics in cardiac CT and discusses its advantages, challenges, and future directions. Although much evidences are needed in this field, cardiac CT radiomics has a lot to offer to patients and physicians with potential to define cardiac disease phenotypes on imaging with higher precision.

Measurement accuracy of prototype non-contrast, compressed sensing-based, respiratory motion-resolved whole heart cardiovascular magnetic resonance angiography for the assessment of thoracic aortic dilatation: comparison with computed tomography angiography.

BACKGROUND: Patients with thoracic aortic dilatation who undergo annual computed tomography angiography (CTA) are subject to repeated radiation and contrast exposure. The purpose of this study was to evaluate the feasibility of a non-contrast, respiratory motion-resolved whole-heart cardiovascular magnetic resonance angiography (CMRA) technique against reference standard CTA, for the quantitative assessment of cardiovascular anatomy and monitoring of disease progression in patients with thoracic aortic dilatation.  METHODS: Twenty-four patients (68.6 ± 9.8 years) with thoracic aortic dilatation prospectively underwent clinical CTA and research 1.5T CMRA between July 2017 and November 2018. Scans were repeated in 15 patients 1 year later. A prototype free-breathing 3D radial balanced steady-state free-precession whole-heart CMRA sequence was used in combination with compressed sensing-based reconstruction. Area, circumference, and diameter measurements were obtained at seven aortic levels by two experienced and two inexperienced readers. In addition, area and diameter measurements of the cardiac chambers, pulmonary arteries and pulmonary veins were also obtained. Agreement between the two modalities was assessed with intraclass correlation coefficient (ICC) analysis, Bland-Altman plots and scatter plots. RESULTS: Area, circumference and diameter measurements on a per-level analysis showed good or excellent agreement between CTA and CMRA (ICCs > 0.84). Means of differences on Bland-Altman plots were: area 0.0 cm2 [- 1.7; 1.6]; circumference 1.0 mm [- 10.0; 12.0], and diameter 0.6 mm [- 2.6; 3.6]. Area and diameter measurements of the left cardiac chambers showed good agreement (ICCs > 0.80), while moderate to good agreement was observed for the right chambers (all ICCs > 0.56). Similar good to excellent inter-modality agreement was shown for the pulmonary arteries and veins (ICC range 0.79-0.93), with the exception of the left lower pulmonary vein (ICC < 0.51). Inter-reader assessment demonstrated mostly good or excellent agreement for both CTA and CMRA measurements on a per-level analysis (ICCs > 0.64). Difference in maximum aortic diameter measurements at baseline vs follow up showed excellent agreement between CMRA and CTA (ICC = 0.91). CONCLUSIONS: The radial whole-heart CMRA technique combined with respiratory motion-resolved reconstruction provides comparable anatomical measurements of the thoracic aorta and cardiac structures as the reference standard CTA. It could potentially be used to diagnose and monitor patients with thoracic aortic dilatation without exposing them to radiation or contrast media.

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

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

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

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

Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow Considerations.

The constantly increasing number of computed tomography (CT) examinations poses major challenges for radiologists. In this article, the additional benefits and potential of an artificial intelligence (AI) analysis platform for chest CT examinations in routine clinical practice will be examined. Specific application examples include AI-based, fully automatic lung segmentation with emphysema quantification, aortic measurements, detection of pulmonary nodules, and bone mineral density measurement. This contribution aims to appraise this AI-based application for value-added diagnosis during routine chest CT examinations and explore future development perspectives.

Role of Early PET/CT Imaging with 68Ga-PSMA in Staging and Restaging of Prostate Cancer.

Ga-68 Prostate-Specific Membrane Antigen PET/CT is a new tool for the assessment of prostate cancer. Standard imaging time is 60 minutes post injection of radiotracer. At 60 minutes, there is physiologic accumulation of radiotracer in the urinary bladder which may cause some lesions in its vicinity to be obscured. Our aim is to determine if early imaging at 3 minutes in addition to standard imaging at 60 minutes can improve the detection of PSMA-avid lesions. A retrospective review of 167 consecutive patients was conducted. Overall, 115 patients (68.9%) were ruled to have prostate cancer based on imaging as seen on early or standard PET/CT images. In 106/115 (64%), the lesions were detected on both early and standard imaging; in 8/115 (6.9%), the lesions were only detected on early imaging; in 1/115 (0.6%) the lesion was detected only on standard imaging. The addition of early imaging significantly improved the overall detection rate of PSMA-avid lesions (p = 0.039). The ratio of patients with lesions detected on early imaging but not on standard imaging in restaging group was 7/88 and was higher than that in staging group 1/79 (p = 0.043). We recommend early imaging in addition to the standard imaging in Ga-68 PSMA PET/CT, particularly in patients presenting for restaging of prostate cancer.

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.