Aberrant cortical morphology patterns are associated with cognitive impairment in patients with chronic heart failure.

A mounting body of evidences suggests that patients with chronic heart failure (HF) frequently experience cognitive impairments, but the neuroanatomical mechanism underlying these impairments remains elusive. In this retrospective study, 49 chronic HF patients and 49 healthy controls (HCs) underwent brain structural MRI scans and cognitive assessments. Cortical morphology index (cortical thickness, complexity, sulcal depth and gyrification) were evaluated. Correlations between cortical morphology and cognitive scores and clinical variables were explored. Logistic regression analysis was employed to identify risk factors for predicting 3-year major adverse cardiovascular events. Compared with HCs, patients with chronic HF exhibited decreased cognitive scores (p < .001) and decreased cortical thickness, sulcal depth and gyrification in brain regions involved cognition, sensorimotor, autonomic nervous system (family-wise error correction, all p values <.05). Notably, HF duration and New York Heart Association (NYHA) demonstrated negative correlations with abnormal cortex morphology, particularly HF duration and thickness in left precentral gyrus (r = -.387, p = .006). Cortical morphology characteristics exhibited positive associations with global cognition, particularly cortical thickness in left pars opercularis (r = .476, p < .001). NYHA class is an independent risk factor for adverse outcome (p = .001). The observed correlation between abnormal cortical morphology and global cognition suggested that cortical morphology may serve as a promising imaging biomarker and provide insights into neuroanatomical underpinnings of cognitive impairment in patients with chronic HF.

Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque Characterization.

Coronary artery disease is a leading cause of death worldwide. Major adverse cardiac events are associated not only with coronary luminal stenosis but also with atherosclerotic plaque components. Coronary computed tomography angiography (CCTA) enables non-invasive evaluation of atherosclerotic plaque along the entire coronary tree. However, precise and efficient assessment of plaque features on CCTA is still a challenge for physicians in daily practice. Artificial intelligence (AI) refers to algorithms that can simulate intelligent human behavior to improve clinical work efficiency. Recently, cardiovascular imaging has seen remarkable advancements with the use of AI. AI-assisted CCTA has the potential to facilitate the clinical workflow, offer objective and repeatable quantitative results, accelerate the interpretation of reports, and guide subsequent treatment. Several AI algorithms have been developed to provide a comprehensive assessment of atherosclerotic plaques. This review serves to highlight the cutting-edge applications of AI-assisted CCTA in atherosclerosis plaque characterization, including detecting obstructive plaques, assessing plaque volumes and vulnerability, monitoring plaque progression, and providing risk assessment. Finally, this paper discusses the current problems and future directions for implementing AI in real-world clinical settings.

Late gadolinium enhanced cardiac MR derived radiomics approach for predicting all-cause mortality in cardiac amyloidosis: a multicenter study.

OBJECTIVES: To evaluate the prognostic value of radiomics features based on late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) images in patients with cardiac amyloidosis (CA). METHODS: This retrospective study included 120 CA patients undergoing CMR at three institutions. Radiomics features were extracted from global and three different segments (base, mid-ventricular, and apex) of left ventricular (LV) on short-axis LGE images. Primary endpoint was all-cause mortality. The predictive performance of the radiomics features and semi-quantitative and quantitative LGE parameters were compared by ROC. The AUC was used to observe whether Rad-score had an incremental value for clinical stage. The Kaplan-Meier curve was used to further stratify the risk of CA patients. RESULTS: During a median follow-up of 12.9 months, 30% (40/120) patients died. There was no significant difference in the predictive performance of the radiomics model in different LV sections in the validation set (AUCs of the global, basal, middle, and apical radiomics model were 0.75, 0.77, 0.76, and 0.77, respectively; all p > 0.05). The predictive performance of the Rad-score of the base-LV was better than that of the LGE total enhancement mass (AUC:0.77 vs. 0.54, p < 0.001) and LGE extent (AUC: 0.77 vs. 0.53, p = 0.004). Rad-score combined with Mayo stage had better predictive performance than Mayo stage alone (AUC: 0.86 vs. 0.81, p = 0.03). Rad-score (≥ 0.66) contributed to the risk stratification of all-cause mortality in CA. CONCLUSIONS: Compared to quantitative LGE parameters, radiomics can better predict all-cause mortality in CA, while the combination of radiomics and Mayo stage could provide higher predictive accuracy. CLINICAL RELEVANCE STATEMENT: Radiomics analysis provides incremental value and improved risk stratification for all-cause mortality in patients with cardiac amyloidosis. KEY POINTS: • Radiomics in LV-base was superior to LGE semi-quantitative and quantitative parameters for predicting all-cause mortality in CA. • Rad-score combined with Mayo stage had better predictive performance than Mayo stage alone or radiomics alone. • Rad-score ≥ 0.66 was associated with a significantly increased risk of all-cause mortality in CA patients.

Cardiac MR Feature Tracking Derived Strain Analysis can Predict Return to Training following Exertional Heatstroke.

BACKGROUND: Exertional heatstroke (EHS) is increasingly common in young trained soldiers. However, the prognosis marker in EHS patients remains unclear. To evaluate cardiac MRI feature tracking (CMR-FT) derived left ventricle (LV) strain as a biomarker for return to training (RTT) in trained soldiers with EHS in a prospective CMR cohort. METHODS: Trained soldiers (participants) with EHS underwent cardiac MR cine sequences between June 2020 and August 2023. Two-dimensional (2D) LV strain parameters were derived. At 3 months after index CMR, the participants with persistent cardiac symptoms including chest pain, dyspnea, palpitations, syncope, and recurrent heat-related illness were defined as non-RTT. Multivariable logistic regression analysis is used to develop a predictive RTT model. The performance of different models was compared using the area under curve (AUC). RESULTS: A total of 80 participants (median age, 21 years; interquartile range (IQR), 20-23 years) and 27 health controls (median age, 21 years; IQR, 20-22 years) were prospectively included. Of the 77 participants, 32 (41.6%) had persistent cardiac symptoms and were not able to RTT at 3 months follow-up after experiencing EHS. The 2D global longitudinal strain (GLS) was significantly impaired in EHS participants compared to the healthy control group (-15.81 ± 1.67% vs -16.93 ± 1.22%, P =.001), which also showed significantly statistical differences between participants with RTT and non-RTT (-14.99 ± 3.54% vs -16.53 ± 1.43%, P <.001). 2D-GLS (≤ -15.00%) (odds ratio, 1.53; 95% confidence interval (CI): 1.08, 2.17; P =.016) was an independent predictor for RTT even after adjusting known risk factors. 2D-GLS provided incremental prognostic value over the clinical model and conventional CMR parameters model (AUCs: 0.72 vs 0.88, P =.013; 0.79 vs 0.88, P =.023; respectively). CONCLUSIONS: Two-dimensional global longitudinal strain (≤ -15.00%) is an incremental prognostic CMR biomarker to predict return to training in exertional heatstroke soldiers.

A Coronary CT Angiography Radiomics Model to Identify Vulnerable Plaque and Predict Cardiovascular Events.

Background A noninvasive coronary CT angiography (CCTA)-based radiomics technique may facilitate the identification of vulnerable plaques and patients at risk for future adverse events. Purpose To assess whether a CCTA-based radiomic signature (RS) of vulnerable plaques defined with intravascular US was associated with increased risk for future major adverse cardiac events (MACE). Materials and Methods In a retrospective study, an RS of vulnerable plaques was developed and validated using intravascular US as the reference standard. The RS development data set included patients first undergoing CCTA and then intravascular US within 3 months between June 2013 and December 2020 at one tertiary hospital. The development set was randomly assigned to training and validation sets at a 7:3 ratio. Diagnostic performance was assessed internally and externally from three tertiary hospitals using the area under the curve (AUC). The prognostic value of the RS for predicting MACE was evaluated in a prospective cohort with suspected coronary artery disease between April 2018 and March 2019. Multivariable Cox regression analysis was used to evaluate the RS and conventional anatomic plaque features (eg, segment involvement score) for predicting MACE. Results The RS development data set included 419 lesions from 225 patients (mean age, 64 years ± 10 [SD]; 68 men), while the prognostic cohort included 1020 lesions from 708 patients (mean age, 62 years ± 11; 498 men). Sixteen radiomic features, including two shape features and 14 textural features, were selected to build the RS. The RS yielded a moderate to good AUC in the training, validation, internal, and external test sets (AUC = 0.81, 0.75, 0.80, and 0.77, respectively). A high RS (≥1.07) was independently associated with MACE over a median 3-year follow-up (hazard ratio, 2.01; P = .005). Conclusion A coronary CT angiography-derived radiomic signature of coronary plaque enabled the detection of vulnerable plaques that were associated with increased risk for future adverse cardiac outcomes. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by De Cecco and van Assen in this issue.

Image Improved Intravoxel Incoherent Motion MRI With Optimized Trigger Delays Based on Strain Curve Analysis to Evaluate Myocardial Microvascular Dysfunction of Exertional Heat Illness.

BACKGROUND: Intravoxel incoherent motion (IVIM) MRI has not been widely used and its role in evaluating exertional heat illness (EHI)-related myocardial involvement remains unknown. PURPOSE: To investigate the feasibility of strain curve-derived trigger delay (TD) IVIM-MRI and its role in assessing myocardial diffusion and microvascular perfusion of EHI patients. STUDY TYPE: Prospective. SUBJECTS: A total of 42 male EHI patients (median age: 21 years) and 22 age- and sex-matched healthy controls (HC). FIELD STRENGTH/SEQUENCE: A 3-T, diffusion-weighted spin-echo echo-planar-imaging sequence. ASSESSMENT: IVIM-MRI was acquired by conventional TD method (group A) or strain curve-based TD method (group B) in random order. IVIM image quality was evaluated on a 3-point Likert scale (1, nondiagnostic; 2, moderate; 3, good). Technical success was defined as image quality score = 3. IVIM-MRI-derived parameters (pseudo diffusion in the capillaries [D*], perfusion fraction [f], and slow apparent diffusion coefficient [D]) were compared between EHI and HC. STATISTICAL TESTS: Student's t-tests, chi-square tests, one-way analysis of variance, receiver operating characteristic (ROC) curve analysis, Pearson's correlation coefficient (r). The statistical significance level was set at P < 0.05. RESULTS: IVIM-MRI image quality score (median [interquartile range]: 3 [2, 3] vs. 2 [1-3]) and technical success rate (61.9%[13/21] vs. 28.6%[6/21]) were significantly improved in group B. EHI patients showed significantly decreased D* (118.1 ± 23.3 × 10-3  mm2 /sec vs. 142.7 ± 42.6 × 10-3  mm2 /sec) and f values (0.42 ± 0.12 vs. 0.51 ± 0.11) and significantly higher D values (3.0 ± 0.9 × 10-3  mm2 /sec vs. 2.5 ± 0.6 × 10-3  mm2 /sec) compared to HC. Relative to D and D*, f showed the most robust efficacy for detecting EHI-related myocardial injury with the highest area under the ROC curve (0.906: 95% confidence interval, 0.799, 0.967) and sensitivity of 88.5% and specificity of 85.6%. CONCLUSION: The strain curve-based TD method significantly improved image quality and technical success rate of IVIM-MRI, and f value may be an effective biomarker to assess myocardial microcirculation abnormalities of EHI patients. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 3.

The prognostic value of CT-derived fractional flow reserve in coronary artery bypass graft: a retrospective multicenter study.

OBJECTIVES: To investigate the predictive value of CT-derived fractional flow reserve (FFRCT) in anastomosis occlusion after coronary artery bypass graft (CABG) surgery. METHODS: Patients undergoing CABG with both pre- and post-operative coronary computed tomographic angiography (CCTA) were retrospectively included. Preoperative CCTA studies were used to evaluate anatomical and FFRCT information of target vessels. A diameter stenosis (DS) ≥ 70% or left main > 50% was considered to be anatomically severe, while FFRCT value ≤ 0.80 be functionally significant. The primary endpoint was anastomosis occlusion evaluated on post-operative CCTA during follow-up. Predictors of anastomosis occlusion were assessed by the multivariate binary logistic regression with generalized estimating equations. RESULTS: A total of 270 anastomoses were identified in 88 enrolled patients. Forty-one anastomoses from 30 patients exhibited occlusion during a follow-up of 15.3 months after CABG. The occluded group had significantly increased prevalence of non-severe DS (58.5% vs. 40.2%; p = 0.023) and non-significant FFRCT (48.8% vs. 10.0%; p < 0.001). Multivariable analysis indicated FFRCT ≤ 0.80 (odds ratio [OR]: 0.10, 95% CI: 0.03-0.33; p < 0.001) and older age (OR: 0.92, 95% CI: 0.87-0.97; p = 0.001) were predictors for bypass patency during follow-up, while myocardial infarction history and anastomosis to a local lesion or bifurcation (all p value < 0.05) were predictors of occlusion. Adding FFRCT into the model based on the clinical and anatomical predictors had an improved AUC of 0.848 (p = 0.005). CONCLUSIONS: FFRCT ≤ 0.80 was associated with a significant risk reduction of anastomosis occlusion after CABG. Preoperative judgment of the hemodynamic significance may improve the CABG surgery strategy and reduce graft failure. KEY POINTS: • FFRCT ≤ 0.80 was associated with a significant risk reduction of anastomosis occlusion after CABG. • The addition of FFRCT into the integrated model including clinical (age and history of myocardial infarction) and anatomical CCTA indicators (local lesion and bifurcation) significantly improved the model performance with an AUC of 0.848 (p = 0.005). • Preoperative judgment of the hemodynamic significance may help improve the decision-making and surgery planning in patients indicated for CABG and significantly reduce graft failure, without an extra radiation exposure and risk of invasive procedure.

Multiparametric cardiac magnetic resonance reveals persistent myocardial inflammation in patients with exertional heat illness.

OBJECTIVES: To explore the clinical potential of multiparametric cardiac magnetic resonance (CMR) in evaluating myocardial inflammation in patients with exertional heat illness (EHI). METHODS: This prospective study enrolled 28 males with EHI (18 patients with exertional heat exhaustion (EHE) and 10 with exertional heat stroke (EHS)) and 18 age-matched male healthy controls (HC). All subjects underwent multiparametric CMR, and 9 patients had follow-up CMR measurements 3 months after recovery from EHI. CMR-derived left ventricular geometry, function, strain, native T1, extracellular volume (ECV), T2, T2*, and late gadolinium enhancement (LGE) were obtained and compared among different groups. RESULTS: Compared with HC, EHI patients showed increased global ECV, T2, and T2* values (22.6% ± 4.1 vs. 19.7% ± 1.7; 46.8 ms ± 3.4 vs. 45.1 ms ± 1.2; 25.5 ms ± 2.2 vs. 23.8 ms ± 1.7; all p < 0.05). Subgroup analysis showed that ECV was higher in the EHS patients than those in EHE and HC groups (24.7% ± 4.9 vs. 21.4% ± 3.2, 24.7% ± 4.9 vs. 19.7% ± 1.7; both p < 0.05). Repeated CMR measurements at 3 months after baseline CMR showed persistently higher ECV than HC (p = 0.042). CONCLUSIONS: With multiparametric CMR, EHI patients demonstrated increased global ECV, T2, and persistent myocardial inflammation at 3-month follow-up after EHI episode. Therefore, multiparametric CMR might be an effective method in evaluating myocardial inflammation in patients with EHI. CLINICAL RELEVANCE STATEMENT: This study showed persistent myocardial inflammation after an exertional heat illness (EHI) episode demonstrated by multiparametric CMR, which is a potential promising method to evaluate the severity of myocardial inflammation and guide return to work, play, or duty in EHI patients. KEY POINTS: • EHI patients showed an increased global extracellular volume (ECV), late gadolinium enhancement, and T2 value, indicating myocardial edema and fibrosis. • ECV was higher in the exertional heat stroke patients than exertional heat exhaustion and healthy control groups (24.7% ± 4.9 vs. 21.4% ± 3.2, 24.7% ± 4.9 vs. 19.7% ± 1.7; both p < 0.05). • EHI patients showed persistent myocardial inflammation with higher ECV than healthy controls 3 months after index CMR (22.3% ± 2.4 vs. 19.7% ± 1.7, p = 0.042).

Time-dependent cardiac structural and functional changes after kidney transplantation: a multi-parametric cardiac magnetic resonance study.

OBJECTIVES: To map time-dependent cardiac structural and functional change patterns after renal transplantation (KT) using cardiac magnetic resonance (CMR). METHODS: Fifty-three patients with pre-KT and post-KT CMR exams were retrospectively analyzed. Patients were divided into three groups according to the time of post-KT CMR: group 1 (3 months post-KT, n = 16), group 2 (6 months post-KT, n = 21), and group 3 (over 9 months post-KT, n = 16). Twenty-one age- and sex-matched healthy controls (HC) were recruited for the study. CMR-derived left ventricular (LV) volumes, LV mass index (LVMi), LV ejection fraction (LVEF), global radial strain (GRS), global circumferential strain (GCS), global longitudinal strain (GLS), and native T1 value were compared. The association between the changes of CMR parameters was assessed. RESULTS: LVMi post-KT decreased in groups 2 (p < 0.001) and 3 (p = 0.004) but both groups had higher LVMi values compared to HC (both p < 0.001). GLS post-KT was decreased in group 1 (p = 0.021), but slightly increased in group 2 (p = 0.728) and group 3 (p = 0.100) without significant difference. GLS post-KT in group 3 was not different from HC (p = 0.104). LVEF, GRS, and GCS post-KT in groups 2 and 3 significantly increased and showed no significant difference from HC. The post-KT native T1 value in all three groups significantly decreased; however, no group showed any significant difference from HC. The change of LVEF was associated with the change of GCS, GRS, and GLS. CONCLUSIONS: Although GRS, GCS, GLS, and native T1 values reversed to normal level, LVMi remained impaired in median 14 months after KT. KEY POINTS: • Kidney transplantation has favorable effects on cardiac structure and function. • In a median 14 months of follow-up after KT, left ventricle strain and native T1 value reversed to normal level while LV mass index (LVMi) did not. Left ventricular hypertrophy may help to explain why KT recipients are still at increased cardiovascular risk. • The reason for the decrease of native T1 value after KT may be more than myocardial fibrosis and needs to be further studied.

Influence of diabetes mellitus on the diagnostic performance of machine learning-based coronary CT angiography-derived fractional flow reserve: a multicenter study.

OBJECTIVES: To examine the diagnostic accuracy of machine learning-based coronary CT angiography-derived fractional flow reserve (FFRCT) in diabetes mellitus (DM) patients. METHODS: In total, 484 patients with suspected or known coronary artery disease from 11 Chinese medical centers were retrospectively analyzed. All patients underwent CCTA, FFRCT, and invasive FFR. The patients were further grouped into mild (25~49 %), moderate (50~69 %), and severe (≥ 70 %) according to CCTA stenosis degree and Agatston score < 400 and Agatston score ≥ 400 groups according to coronary artery calcium severity. Propensity score matching (PSM) was used to match DM (n  = 112) and non-DM (n  = 214) groups. Sensitivity, specificity, accuracy, and area under the curve (AUC) with 95 % confidence interval (CI) were calculated and compared. RESULTS: Sensitivity, specificity, accuracy, and AUC of FFRCT were 0.79, 0.96, 0.87, and 0.91 in DM patients and 0.82, 0.93, 0.89, and 0.89 in non-DM patients without significant difference (all p > 0.05) on a per-patient level. The accuracies of FFRCT had no significant difference among different coronary stenosis subgroups and between two coronary calcium subgroups (all p > 0.05) in the DM and non-DM groups. After PSM grouping, the accuracies of FFRCT were 0.88 in the DM group and 0.87 in the non-DM group without a statistical difference (p > 0.05). CONCLUSIONS: DM has no negative impact on the diagnostic accuracy of machine learning-based FFRCT. KEY POINTS: • ML-based FFRCT has a high discriminative accuracy of hemodynamic ischemia, which is not affected by DM. • FFRCT was superior to the CCTA alone for the detection of ischemia relevance of coronary artery stenosis in both DM and non-DM patients. • Coronary calcification had no significant effect on the diagnostic accuracy of FFRCT to detect ischemia in DM patients.

One-year outcomes of CCTA alone versus machine learning-based FFRCT for coronary artery disease: a single-center, prospective study.

OBJECTIVES: To explore downstream management and outcomes of machine learning (ML)-based CT derived fractional flow reserve (FFRCT) strategy compared with an anatomical coronary computed tomography angiography (CCTA) alone assessment in participants with intermediate coronary artery stenosis. METHODS: In this prospective study conducted from April 2018 to March 2019, participants were assigned to either the CCTA or FFRCT group. The primary endpoint was the rate of invasive coronary angiography (ICA) that demonstrated non-obstructive disease at 90 days. Secondary endpoints included coronary revascularization and major adverse cardiovascular events (MACE) at 1-year follow-up. RESULTS: In total, 567 participants were allocated to the CCTA group and 566 to the FFRCT group. At 90 days, the rate of ICA without obstructive disease was higher in the CCTA group (33.3%, 39/117) than that (19.8%, 19/96) in the FFRCT group (risk difference [RD] = 13.5%, 95% confidence interval [CI]: 8.4%, 18.6%; p = 0.03). The ICA referral rate was higher in the CCTA group (27.5%, 156/567) than in the FFRCT group (20.3%, 115/566) (RD = 7.2%, 95% CI: 2.3%, 12.1%; p = 0.003). The revascularization-to-ICA ratio was lower in the CCTA group than that in the FFRCT group (RD = 19.8%, 95% CI: 14.1%, 25.5%, p = 0.002). MACE was more common in the CCTA group than that in the FFRCT group at 1 year (HR: 1.73; 95% CI: 1.01, 2.95; p = 0.04). CONCLUSION: In patients with intermediate stenosis, the FFRCT strategy appears to be associated with a lower rate of referral for ICA, ICA without obstructive disease, and 1-year MACE when compared to the anatomical CCTA alone strategy. KEY POINTS: • In stable patients with intermediate stenosis, ML-based FFRCT strategy was associated with a lower referral ICA rate, a lower normalcy rate of ICA, and higher revascularization-to-ICA ratio than the CCTA strategy. • Compared with the CCTA strategy, ML-based FFRCTshows superior outcome prediction value which appears to be associated with a lower rate of 1-year MACE. • ML-based FFRCT strategy as a non-invasive "one-stop-shop" modality may be the potential to change diagnostic workflows in patients with suspected coronary artery disease.

Functional CAD-RADS using FFRCT on therapeutic management and prognosis in patients with coronary artery disease.

OBJECTIVES: To propose a novel functional Coronary Artery Disease-Reporting and Data System (CAD-RADS) category system integrated with coronary CT angiography (CCTA)-derived fractional flow reserve (FFRCT) and to validate its effect on therapeutic decision and prognosis in patients with coronary artery disease (CAD). METHODS: Firstly, we proposed a novel functional CAD-RADS and evaluated the performance of functional CAD-RADS for guiding treatment strategies with actual clinical treatment as a reference standard in a retrospective multicenter cohort with CCTA and invasive FFR performed in all patients (n = 466). Net reclassification improvement (NRI) of functional CAD-RADS over anatomical CAD-RADS was calculated. Secondly, the prognostic value of functional CAD-RADS in a prospective two-arm cohort (566 [FFRCT arm] vs. 567 [CCTA arm]) was calculated, after a 1-year follow-up, functional CAD-RADS in FFRCT arm (n = 513) and anatomical CAD-RADS in CCTA arm (n = 511) to determine patients at risk of adverse outcomes were compared with a Cox hazard proportional model. RESULTS: Functional CAD-RADS demonstrated superior value over anatomical CAD-RADS (AUC: 0.828 vs. 0.681, p < 0.001) and comparable performance to FFR (AUC: 0.828 vs. 0.848, p = 0.253) in guiding therapeutic decisions. Functional CAD-RADS resulted in the revision of management plan as determined by anatomical CAD-RADS in 30.0% of patients (n = 140) (NRI = 0.369, p < 0.001). Functional CAD-RADS was an independent predictor for 1-year outcomes with indexes of concordance of 0.795 and the corresponding value was 0.751 in anatomical CAD-RADS. CONCLUSION: The novel functional CAD-RADS gained incremental value in guiding therapeutic decision-making compared with anatomical CAD-RADS and comparable power in 1-year prognosis with anatomical CAD-RADS in a real-world scenario. KEY POINTS: • The novel functional CAD-RADS category system with FFRCT integrated into the anatomical CAD-RADS categories was originally proposed. • The novel functional CAD-RADS category system was validated superior value over anatomical CAD-RADS (AUC: 0.828 vs. 0.681, p < 0.001) in guiding therapeutic decisions and revised management plan in 30.0% of patients as determined by anatomical CAD-RADS (net reclassification improvement index = 0.369, p < 0.001). • Functional CAD-RADS was an independent predictor with an index of concordance of 0.795 and 0.751 in anatomical CAD-RADS for 1-year prognosis of adverse outcomes.

CHIMGEN: a Chinese imaging genetics cohort to enhance cross-ethnic and cross-geographic brain research.

The Chinese Imaging Genetics (CHIMGEN) study establishes the largest Chinese neuroimaging genetics cohort and aims to identify genetic and environmental factors and their interactions that are associated with neuroimaging and behavioral phenotypes. This study prospectively collected genomic, neuroimaging, environmental, and behavioral data from more than 7000 healthy Chinese Han participants aged 18-30 years. As a pioneer of large-sample neuroimaging genetics cohorts of non-Caucasian populations, this cohort can provide new insights into ethnic differences in genetic-neuroimaging associations by being compared with Caucasian cohorts. In addition to micro-environmental measurements, this study also collects hundreds of quantitative macro-environmental measurements from remote sensing and national survey databases based on the locations of each participant from birth to present, which will facilitate discoveries of new environmental factors associated with neuroimaging phenotypes. With lifespan environmental measurements, this study can also provide insights on the macro-environmental exposures that affect the human brain as well as their timing and mechanisms of action.

Detection of renal allograft fibrosis with MRI: arterial spin labeling outperforms reduced field-of-view IVIM.

OBJECTIVE: To compare the value of reduced field-of-view (FOV) intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) and arterial spin labeling (ASL) for assessing renal allograft fibrosis and predicting long-term dysfunction. METHODS: This prospective study included 175 renal transplant recipients undergoing reduced FOV IVIM DWI, ASL, and biopsies. Renal allograft fibrosis was categorized into ci0, ci1, ci2, and ci3 fibrosis according to biopsy results. A total of 83 participants followed for a median of 39 (IQR, 21-42) months were dichotomized into stable and impaired allograft function groups based on follow-up estimated glomerular filtration rate. Total apparent diffusion coefficient (ADCT), pure diffusion ADC, pseudo-perfusion ADC, perfusion fraction f from IVIM DWI, and renal blood flow (RBF) from ASL were calculated and compared. The area under the receiver operating characteristic curve (AUC) was calculated to assess the diagnostic and predictive performances. RESULTS: RBF was different in ci0 vs ci1 (147.9 ± 46.3 vs 126.0 ± 49.4 ml/min/100 g, p = .02) and ci2 vs ci3 (92.9 ± 46.9 vs 70.8 ± 37.8 ml/min/100 g, p = .03). RBF in the stable group was higher than that in the impaired group (144.73 ± 49.33 vs 102.19 ± 47.58 ml/min/100 g, p < .001). AUCs in distinguishing renal allograft fibrosis and predicting long-term allograft dysfunction for RBF were higher than cortical ADCT (ci0 vs ci1-3, 0.76 vs 0.59, p < .001; ci0-1 vs ci2-3, 0.79 vs 0.68, p = .01; ci0-2 vs ci3, 0.79 vs 0.68, p = .01; 0.76 vs 0.60, p = .04, respectively). CONCLUSION: Compared to reduced FOV IVIM DWI, ASL was a more promising technique for noninvasively distinguishing renal allograft fibrosis degree and predicting long-term allograft dysfunction. KEY POINTS: • Compared to total ADC from rFOV IVIM DWI, RBF from ASL can distinguish no fibrosis (ci0) vs mild fibrosis (ci1) (p = .02) and moderate fibrosis (ci2) vs severe fibrosis (ci3) (p = .04). • RBF had superior performance than diffusion parameters in discriminating fibrosis (no fibrosis [ci0] vs fibrosis [ci1-3], mild fibrosis [ci0-1] vs moderate to severe fibrosis [ci2-3], non-severe [ci0-2] vs severe [ci3] fibrosis; AUC = 0.76 vs 0.59, p < .001; 0.79 vs 0.68, p = .01; 0.79 vs 0.68, p = .01). • Compared to reduced FOV IVIM DWI, ASL was a more promising technique for noninvasively predicting long-term allograft dysfunction (AUC = 0.76 vs 0.60, p = .04).

Automatic quantification of myocardium and pericardial fat from coronary computed tomography angiography: a multicenter study.

OBJECTIVES: To develop a deep learning-based method for simultaneous myocardium and pericardial fat quantification from coronary computed tomography angiography (CCTA) for the diagnosis and treatment of cardiovascular disease (CVD). METHODS: We retrospectively identified CCTA data obtained between May 2008 and July 2018 in a multicenter (six centers) CVD study. The proposed method was evaluated on 422 patients' data by two studies. The first overall study involves training model on CVD patients and testing on non-CVD patients, as well as training on non-CVD patients and testing on CVD patients. The second study was performed using the leave-center-out approach. The method performance was evaluated using Dice similarity coefficient (DSC), Jaccard index (JAC), 95% Hausdorff distance (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and the center of mass distance (CMD). The robustness of the proposed method was tested using the nonparametric Kruskal-Wallis test and post hoc test to assess the equality of distribution of DSC values among different tests. RESULTS: The automatic segmentation achieved a strong correlation with contour (ICC and R > 0.97, p value < 0.001 throughout all tests). The accuracy of the proposed method remained high through all the tests, with the median DSC higher than 0.88 for pericardial fat and 0.96 for myocardium. The proposed method also resulted in mean MSD, RMSD, HD95, and CMD of less than 1.36 mm for pericardial fat and 1.00 mm for myocardium. CONCLUSIONS: The proposed deep learning-based segmentation method enables accurate simultaneous quantification of myocardium and pericardial fat in a multicenter study. KEY POINTS: • Deep learning-based myocardium and pericardial fat segmentation method tested on 422 patients' coronary computed tomography angiography in a multicenter study. • The proposed method provides segmentations with high volumetric accuracy (ICC and R > 0.97, p value < 0.001) and similar shape as manual annotation by experienced radiologists (median Dice similarity coefficient ≥ 0.88 for pericardial fat and 0.96 for myocardium).

Feasibility and prognostic role of machine learning-based FFRCT in patients with stent implantation.

OBJECTIVES: To investigate the feasibility and prognostic implications of coronary CT angiography (CCTA) derived fractional flow reserve (FFRCT) in patients who have undergone stents implantation. METHODS: Firstly, the feasibility of FFRCT in stented vessels was validated. The diagnostic performance of FFRCT in identifying hemodynamically in-stent restenosis (ISR) in 33 patients with invasive FFR ≤ 0.88 as reference standard, intra-group correlation coefficient (ICC) between FFRCT and FFR was calculated. Secondly, prognostic value was assessed with 115 patients with serial CCTA scans after PCI. Stent characteristics (location, diameter, length, etc.), CCTA measurements (minimum lumen diameter [MLD], minimum lumen area [MLA], ISR), and FFRCT measurements (FFRCT, ΔFFRCT, ΔFFRCT/stent length) both at baseline and follow-up were recorded. Longitudinal analysis included changes of MLD, MLA, ISR, and FFRCT. The primary endpoint was major adverse cardiovascular events (MACE). RESULTS: Per-patient accuracy of FFRCT was 0.85 in identifying hemodynamically ISR. FFRCT had a good correlation with FFR (ICC = 0.84). 15.7% (18/115) developed MACE during 25 months since follow-up CCTA. Lasso regression identified age and follow-up ΔFFRCT/length as candidate variables. In the Cox proportional hazards model, age (hazard ratio [HR], 1.102 [95% CI, 1.032-1.177]; p = 0.004) and follow-up ΔFFRCT/length (HR, 1.014 [95% CI, 1.006-1.023]; p = 0.001) were independently associated with MACE (c-index = 0.856). Time-dependent ROC analysis showed AUC was 0.787 (95% CI, 0.594-0.980) at 25 months to predict adverse outcome. After bootstrap validation with 1000 resamplings, the bias-corrected c-index was 0.846. CONCLUSIONS: Noninvasive ML-based FFRCT is feasible in patients following stents implantation and shows prognostic value in predicting adverse events after stents implantation in low-moderate risk patients. KEY POINTS: • Machine-learning-based FFRCT is feasible to evaluate the functional significance of in-stent restenosis in patients with stent implantation. • Follow-up △FFRCT along with the stent length might have prognostic implication in patients with stent implantation and low-to-moderate risk after 2 years follow-up. The prognostic role of FFRCT in patients with moderate-to-high or high risk needs to be further studied. • FFRCT might refine the clinical pathway of patients with stent implantation to invasive catheterization.

Serial coronary CT angiography-derived fractional flow reserve and plaque progression can predict long-term outcomes of coronary artery disease.

OBJECTIVE: To investigate the utility of coronary CT angiography-derived fractional flow reserve (FFRCT) and plaque progression in patients undergoing serial coronary CT angiography for predicting major adverse cardiovascular events (MACE). METHODS: This retrospective study evaluated patients suspected or known coronary artery disease who underwent serial coronary CT angiography examinations between January 2006 and December 2017 and followed up until June 2019. The primary endpoint was MACE, defined as acute coronary syndrome, rehospitalization due to progressive angina, percutaneous coronary intervention, or cardiac death. FFRCT and plaque parameters were analyzed on a per-vessel and per-patient basis. Univariable and multivariable COX regression analysis determined predictors of MACE. The prognostic value of FFRCT and plaque progression were assessed in nested models. RESULTS: Two hundred eighty-four patients (median age, 61 years (interquartile range, 54-70); 202 males) were evaluated. MACE was observed in 45 patients (15.8%, 45/284). By Cox multivariable regression modeling, vessel-specific FFRCT ≤ 0.80 was associated with a 2.4-fold increased risk of MACE (HR (95% CI): 2.4 (1.3-4.4); p = 0.005) and plaque progression was associated with a 9-fold increased risk of MACE (HR (95% CI): 9 (3.5-23); p < 0.001) after adjusting for clinical and imaging risk factors. FFRCT and plaque progression improved the prediction of events over coronary artery calcium (CAC) score and high-risk plaques (HRP) in the receiver operating characteristics analysis (area under the curve: 0.70 to 0.86; p = 0.002). CONCLUSIONS: Fractional flow reserve and plaque progression assessed by serial coronary CT angiography predicted the risk of future MACE. KEY POINTS: • Vessel-specific CT angiography-derived fractional flow reserve (FFRCT) ≤ 0.80 and plaque progression improved the prediction of events over current risk factors. • Major adverse cardiovascular events (MACE) significantly increased with the presence of plaque progression at follow-up stratified by the FFRCT change group.

The effect of coronary calcification on diagnostic performance of machine learning-based CT-FFR: a Chinese multicenter study.

OBJECTIVE: To investigate the effect of coronary calcification morphology and severity on the diagnostic performance of machine learning (ML)-based coronary CT angiography (CCTA)-derived fractional flow reserve (CT-FFR) with FFR as a reference standard. METHODS: A total of 442 patients (61.2 ± 9.1 years, 70% men) with 544 vessels who underwent CCTA, ML-based CT-FFR, and invasive FFR from China multicenter CT-FFR study were enrolled. The effect of calcification arc, calcification remodeling index (CRI), and Agatston score (AS) on the diagnostic performance of CT-FFR was investigated. CT-FFR ≤ 0.80 and lumen reduction ≥ 50% determined by CCTA were identified as vessel-specific ischemia with invasive FFR as a reference standard. RESULTS: Compared with invasive FFR, ML-based CT-FFR yielded an overall sensitivity of 0.84, specificity of 0.94, and accuracy of 0.90 in a total of 344 calcification lesions. There was no statistical difference in diagnostic accuracy, sensitivity, or specificity of CT-FFR across different calcification arc, CRI, or AS levels. CT-FFR exhibited improved discrimination of ischemia compared with CCTA alone in lesions with mild-to-moderate calcification (AUC, 0.89 vs. 0.69, p < 0.001) and lesions with CRI ≥ 1 (AUC, 0.89 vs. 0.71, p < 0.001). The diagnostic accuracy and specificity of CT-FFR were higher than CCTA alone in patients and vessels with mid (100 to 299) or high (≥ 300) AS. CONCLUSION: Coronary calcification morphology and severity did not influence diagnostic performance of CT-FFR in ischemia detection, and CT-FFR showed marked improved discrimination of ischemia compared with CCTA alone in the setting of calcification. KEY POINTS: • CT-FFR provides superior diagnostic performance than CCTA alone regardless of coronary calcification. • No significant differences in the diagnostic performance of CT-FFR were observed in coronary arteries with different coronary calcification arcs and calcified remodeling indexes. • No significant differences in the diagnostic accuracy of CT-FFR were observed in coronary arteries with different coronary calcification score levels.

Coronavirus Disease 2019 (COVID-19): A Perspective from China.

In December 2019, an outbreak of severe acute respiratory syndrome coronavirus 2 infection occurred in Wuhan, Hubei Province, China, and spread across China and beyond. On February 12, 2020, the World Health Organization officially named the disease caused by the novel coronavirus as coronavirus disease 2019 (COVID-19). Because most patients infected with COVID-19 had pneumonia and characteristic CT imaging patterns, radiologic examinations have become vital in early diagnosis and the assessment of disease course. To date, CT findings have been recommended as major evidence for clinical diagnosis of COVID-19 in Hubei, China. This review focuses on the etiology, epidemiology, and clinical symptoms of COVID-19 while highlighting the role of chest CT in prevention and disease control.

A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.

OBJECTIVES: To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents. METHODS: A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score. RESULTS: Of 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm3. An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance. CONCLUSIONS: The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists. KEY POINTS: • The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung.