Rationale and ObjectivesTo develop a radiomics model based on cardiac computed tomography (CT) for predicting left ventricular adverse remodeling (LVAR) in patients with severe aortic stenosis (AS) who underwent transcatheter aortic valve replacement (TAVR).Materials and MethodsPatients with severe AS who underwent TAVR from January 2019 to December 2022 were recruited. The cohort was divided into adverse remodeling group and non-adverse remodeling group based on LVAR occurrence, and further randomly divided into a training set and a validation set at an 8:2 ratio. Left ventricular radiomics features were extracted from cardiac CT. The least absolute shrinkage and selection operator regression was utilized to select the most relevant radiomics features and clinical features. The radiomics features were used to construct the Radscore, which was then combined with the selected clinical features to build a nomogram. The predictive performance of the models was evaluated using the area under the curve (AUC), while the clinical value of the models was assessed using calibration curves and decision curve analysis.ResultsA total of 273 patients were finally enrolled, including 71 with adverse remodeling and 202 with non-adverse remodeling. 12 radiomics features and five clinical features were extracted to construct the radiomics model, clinical model, and nomogram, respectively. The radiomics model outperformed the clinical model (training AUC: 0.799 vs. 0.760; validation AUC: 0.766 vs. 0.755). The nomogram showed highest accuracy (training AUC: 0.859, validation AUC: 0.837) and was deemed most clinically valuable by decision curve analysis.ConclusionThe cardiac CT-based radiomics features could predict LVAR after TAVR in patients with severe AS.

Abstract Objectives To assess the effectiveness of HRCT-based radiomics in predicting rapidly progressive interstitial lung disease (RP-ILD) and mortality in anti-MDA5 positive dermatomyositis-related interstitial lung disease (anti-MDA5 + DM-ILD). Methods From August 2014 to March 2022, 160 patients from Institution 1 were retrospectively and consecutively enrolled and were randomly divided into the training dataset (n = 119) and internal validation dataset (n = 41), while 29 patients from Institution 2 were retrospectively and consecutively enrolled as external validation dataset. We generated four Risk-scores based on radiomics features extracted from four areas of HRCT. A nomogram was established by integrating the selected clinico-radiologic variables and the Risk-score of the most discriminative radiomics model. The RP-ILD prediction performance of the models was evaluated by using the area under the receiver operating characteristic curves, calibration curves, and decision curves. Survival analysis was conducted with Kaplan-Meier curves, Mantel-Haenszel test, and Cox regression. Results Over a median follow-up time of 31.6 months (interquartile range: 12.9–49.1 months), 24 patients lost to follow-up and 46 patients lost their lives (27.9%, 46/165). The Risk-score based on bilateral lungs performed best, attaining AUCs of 0.869 and 0.905 in the internal and external validation datasets. The nomogram outperformed clinico-radiologic model and Risk-score with AUCs of 0.882 and 0.916 in the internal and external validation datasets. Patients were classified into low- and high-risk groups with 50:50 based on nomogram. High-risk group patients demonstrated a significantly higher risk of mortality than low-risk group patients in institution 1 (HR = 4.117) and institution 2 cohorts (HR = 7.515). Conclusion For anti-MDA5 + DM-ILD, the nomogram, mainly based on radiomics, can predict RP-ILD and is an independent predictor of mortality.

Background Early precise identification of high-risk dilated cardiomyopathy (DCM) phenotype is essential for clinical decision-making and patient surveillance. The aim of the study was to assess the prognostic value of enhanced cine cardiac magnetic resonance (CMR)-based radiomics in DCM. Methods We prospectively enrolled 401 (training set: 281; test set: 120) DCM patients. Radiomic features were extracted from enhanced cine images of entire left ventricular wall and selected by the least absolute shrinkage and selection operator. Different predictive models were built using logistic regression classifier to predict all-cause mortality and heart transplantation. Model performances were compared with the area under the receiver operating characteristic curves (AUCs). Kaplan-Meier curves, log-rank test, and Cox regression were used for survival analysis. Results Endpoint events occurred in 65 patients over a median follow-up period of 25.4 months. 13 radiomic features were finally selected. The Rad_Combined model integrating clinical characteristics, CMR parameters and radiomics features achieved the best performance with an AUC of 0.836 and 0.835 in the training and test sets, respectively. High-risk groups with endpoint events defined by the Rad_Combined model had significantly shorter survival time than low-risk group in both the training [Hazard Ratio (HR) = 7.74, P < 0.001] and test sets (HR = 4.84, P < 0.001). Conclusion The Rad_Combined model might serve as an effective tool to help risk stratification and clinical decision-making for patients with DCM. Trial registration Chinese Clinical Trial Registry, ChiCTR1800017058 by the ethics committee of West China hospital,Sichuan University.

To evaluate the left ventricular (LV) myocardial tissue characteristics in early adult obesity and its association with regional adipose tissue and ectopic fat deposition. Forty-nine obese adults (mean body mass index: 29.9 ± 2.0 kg/m2) and 44 healthy controls were prospectively studied. LV native and post-contrast T1 values, extracellular volume fraction (ECV), regional adipose tissue (epicardial, visceral, and subcutaneous adipose tissue (EAT, VAT, and SAT)), and ectopic fat deposition (hepatic and pancreatic proton density fat fractions (H-PDFF and P-PDFF)) based on magnetic resonance imaging were compared. The association was assessed by multivariable linear regression. The obese participants showed reduced global ECV compared to the healthy controls (p < 0.05), but there was no significant difference in global native or post-contrast T1 values between the two groups. Additionally, the obese individuals exhibited higher EAT, VAT, SAT, H-PDFF, and P-PDFF than the controls (p < 0.05). ECV was associated with insulin resistance, dyslipidemia, and systolic blood pressure (SBP) (p < 0.05). Multiple linear regression demonstrated that H-PDFF and SAT were independently associated with ECV in entire population (β = − 0.123 and − 0.012; p < 0.05). Reduced myocardial ECV in patients with mild-to-moderate obesity and its relationship to SBP may indicate that cardiomyocyte hypertrophy, rather than extracellular matrix expansion, is primarily responsible for myocardial tissue remodeling in early adult obesity. Our findings further imply that H-PDFF and SAT are linked with LV myocardial tissue remodeling in this cohort beyond the growth difference and cardiovascular risk factors. Effect of lifestyle intervention on metabolism of obese patients based on smart phone software (ChiCTR1900026476). Myocardial fibrosis in severe obesity predicts poor prognosis. We showed that cardiomyocyte hypertrophy, not myocardial fibrosis, is the main myocardial tissue characteristic of early obesity. This finding raises the possibility that medical interventions, like weight loss, may prevent cardiac fibrosis. • Myocardial tissue characteristics in early adult obesity are unclear. • Myocardial extracellular volume fraction (ECV) can be quantitatively evaluated using T1 mapping based on cardiac magnetic resonance imaging (MRI). • Cardiac MRI-derived ECV may noninvasively evaluate myocardial tissue remodeling in early adult obesity.

Background Abdominal ectopic fat deposition and excess visceral fat depots in obesity may be related to cardiovascular disease (CVD) as both are involved in the metabolic syndrome (MetS). The awareness of the link between abdominal adiposity and subclinical cardiac remodeling would help improve treatment and outcome. Besides, liver fibrosis has also shown a potential relationship with cardiac dysfunction. Thus, we aimed to investigate the associations of magnetic resonance (MR)-based abdominal adiposity and hepatic shear stiffness with subclinical left ventricular (LV) remodeling while taking account of MetS-related confounders in adults free of overt CVD. Methods This was an exploratory, prospective study of 88 adults (46 subjects with obesity, 42 healthy controls) who underwent 3 T cardiac and body MR exams. Measures of abdominal MR included hepatic and pancreatic proton density fat fraction (H-PDFF and P-PDFF), hepatic shear stiffness by MR elastography, and subcutaneous and visceral adipose tissue (SAT and VAT). Cardiac measures included epicardial adipose tissue (EAT) and parameters of LV geometry and function. Associations were assessed using Pearson correlation and multivariable linear regression analyses, in which age, sex, and MetS-related confounders were adjusted for. Results The LV ejection fractions of all participants were within the normal range. Higher H-PDFF, P-PDFF, SAT and VAT were independently associated with lower LV global myocardial strain parameters (radial, circumferential and longitudinal peak strain [PS], longitudinal peak systolic strain rate and diastolic strain rate) (β = − 0.001 to − 0.41, p < 0.05), and P-PDFF, SAT and VAT were independently and positively associated with LV end-diastolic volume and stroke volume (β = 0.09 to 3.08, p ≤ 0.02) in the over-all cohort. In the obesity subgroup, higher P-PDFF and VAT were independently associated with lower circumferential and longitudinal PS, respectively (β = − 0.29 to − 0.05, p ≤ 0.01). No independent correlation between hepatic shear stiffness and EAT or LV remodeling was found (all p ≥ 0.05). Conclusions Ectopic fat depositions in the liver and pancreas, and excess abdominal adipose tissue pose a risk of subclinical LV remodeling beyond MetS-related CVD risk factors in adults without overt CVD. VAT may play a more considerable role as a risk factor for subclinical LV dysfunction than does SAT in individuals with obesity. The underlying mechanisms of these associations and their longitudinal clinical implications need further investigation.

Differentiation of left atrial appendage thrombus (LAAT) and left atrial appendage (LAA) circulatory stasis is difficult when based only on single-phase computed tomography angiography (CTA) in routine clinical practice. Radiomics provides a promising tool for their identification. We retrospectively enrolled 204 (training set: 144; test set: 60) atrial fibrillation patients before ablation, including 102 LAAT and 102 circulatory stasis patients. Radiomics software was used to segment whole LAA on single-phase CTA images and extract features. Models were built and compared via a multivariable logistic regression algorithm and area under of the receiver operating characteristic curves (AUCs), respectively. For the radiomics model, radiomics clinical model, radiomics radiological model, and combined model, the AUCs were 0.82, 0.86, 0.90, 0.93 and 0.82, 0.82, 0.84, 0.85 in the training set and the test set, respectively (p < 0.05). One clinical feature (rheumatic heart disease) and four radiological features (transverse diameter of left atrium, volume of left atrium, location of LAA, shape of LAA) were added to the combined model. The combined model exhibited excellent differential diagnostic performances between LAAT and circulatory stasis without increasing extra radiation exposure. The single-phase, CTA-based radiomics analysis shows potential as an effective tool for accurately detecting LAAT in patients with atrial fibrillation before ablation.

Introduction: Diabetes markedly affects the formation and development of intracranial atherosclerosis. The study was aimed at evaluating whether radiomics features can help distinguish plaques primarily associated with diabetes. Materials and Methods: We retrospectively analyzed patients who were admitted to our center because of acute ischemic stroke due to intracranial atherosclerosis between 2016 and 2022. Clinical data, blood biomarkers, conventional plaque features and plaque radiomics features were collected for all patients. Odds ratios (ORs) with 95% confidence intervals (CIs) were determined from logistic regression models. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to describe diagnostic performance. The DeLong test was used to compare differences between models. Results: Overall, 157 patients (115 men; mean age, 58.7 ± 10.7 years) were enrolled. Multivariate logistic regression analysis showed that plaque length (OR: 1.17; 95% CI: 1.07–1.28) and area (OR: 1.13; 95% CI: 1.02–1.24) were independently associated with diabetes. On combining plaque length and area as a conventional model, the AUCs of the training and validation cohorts for identifying diabetes patients were 0.789 and 0.720, respectively. On combining radiomics features on T1WI and contrast-enhanced T1WI sequences, a better diagnostic value was obtained in the training and validation cohorts (AUC: 0.889 and 0.861). The DeLong test showed the model combining radiomics and conventional plaque features performed better than the conventional model in both cohorts (p < 0.05). Conclusions: The use of radiomics features of intracranial plaques on hrMRI can effectively distinguish culprit plaques with diabetes as the primary pathological cause, which will provide new avenues of research into plaque formation and precise treatment.

Background and objective: The development of acute aortic dissection (AD) remains unpredictable due to the intricate nature of the AD mechanism and the varied patient-specific aortic anatomy. The aim of this study was to simulate the hemodynamic parameters in the aortas before the onset of TBAD with healthy controls.Methods: This study numerically assessed the effectiveness of hemodynamic indicators in predicting the risk of type B AD (TBAD) by investigating the differences in hemodynamic parameters between healthy and repaired aortas (aortas before TBAD development). Four wall shear stress (WSS)-based indicators and three helicity-based indicators were adopted and analyzed.Results: The results showed that more pathological anatomical feathers can be observed in the repaired aortas. For WSS-based indicators, only averaged cross flow index (CFI) and oscillatory shear index OSI (CFI, 1.03 +/- 0.07 vs. 0.83 +/- 0.10 and OSI, 0.12 +/- 0.03 vs. 0.04 +/- 0.02) (all p < 0.001) were significantly higher in the repaired aortas than those in the healthy aortas. On the other hand, average helicity in the repaired aortas also showed a significant difference compared with that in healthy aortas (h1, 3.88 +/- 5.55 vs.-8.03 +/- 14.16) ( p < 0.05). Furthermore, the skewed helical structure and flow disturbance was found in the repaired aortas.Conclusion: 1) There are marked differences in pathological anatomical features, such as aortic dilation, elongation and tortuosity between the healthy aortas and repaired aortas, and the corresponding hemo-dynamic indicators also have also been significantly changed. 2) Compared with anatomical characteris-tics, hemodynamic indicators may be more accurate for predicting the risk and location of TBAD, such as the OSI and CFI index were significantly enhanced in the region where the entry tears have occurred. 3) In clinical practice, anatomical features remain important factors for assessing the risk for development of TBAD; however, hemodynamic analyses with quantitative data and more visualizing characteristics have showed promising potential in this aspect.(c) 2022 Published by Elsevier B.V.

Background and ObjectiveThe underlying mechanism of aortic dissection (AD) remains unclear and the onset of AD is still unpredictable. Although clinical study with statistical analysis has reported that type III aortic arch may have strong correlation with type B AD (TBD), the effects of different arch types on the wall shear stress (WSS) have not been clarified.MethodsAs a complementary work, this study numerically investigated the distribution of five WSS-based indicators in thirty aortic arches without AD, which were classified into three groups based on the arch types.ResultsThe distribution of most WSS indicators, such as time averaged WSS (TAWSS), oscillatory shear index (OSI) and relative residence time (RRT) had no significant difference among different types of aortic arches (P>0.05). However, a multidirectional WSS index, namely CFI, was found its maximum value was positively correlated with type III aortic arch in proximal descending aorta (p<0.001, r = 0.65).ConclusionsIt can be concluded that the enhancement or oscillation of WSS may not be the main reason of TBD is prevalence in type III arches, while the multidirectional WSS distribution may be an important factor. It can be further referred that the CFI may have a potential to predict the onset of TBD.

Objective This study aimed to develop enhanced cine image-based radiomic models for non-invasive prediction of left ventricular adverse remodeling following transcatheter aortic valve replacement (TAVR) in symptomatic severe aortic stenosis. Methods A total of 69 patients (male:female = 37:32, median age: 66 years, range: 47–83 years) were retrospectively recruited, and severe aortic stenosis was confirmed via transthoracic echocardiography detection. The enhanced cine images and clinical variables were collected, and three types of regions of interest (ROIs) containing the left ventricular (LV) myocardium from the short-axis view at the basal, middle, and apical LV levels were manually labeled, respectively. The radiomic features were extracted and further selected by using the least absolute shrinkage and selection operator (LASSO) regression analysis. Clinical variables were also selected through univariate regression analysis. The predictive models using logistic regression classifier were developed and validated through leave-one-out cross-validation. The model performance was evaluated with respect to discrimination, calibration, and clinical usefulness. Results Five basal levels, seven middle levels, eight apical level radiomic features, and three clinical factors were finally selected for model development. The radiomic models using features from basal level (Rad I), middle level (Rad II), and apical level (Rad III) had achieved areas under the curve (AUCs) of 0.761, 0.909, and 0.913 in the training dataset and 0.718, 0.836, and 0.845 in the validation dataset, respectively. The performance of these radiomic models was improved after integrating clinical factors, with AUCs of the Combined I, Combined II, and Combined III models increasing to 0.906, 0.956, and 0.959 in the training dataset and 0.784, 0.873, and 0.891 in the validation dataset, respectively. All models showed good calibration, and the decision curve analysis indicated that the Combined III model had a higher net benefit than other models across the majority of threshold probabilities. Conclusion Radiomic models and combined models at the mid and apical slices showed outstanding and comparable predictive effectiveness of adverse remodeling for patients with symptomatic severe aortic stenosis after TAVR, and both models were significantly better than the models of basal slice. The cardiac magnetic resonance radiomic analysis might serve as an effective tool for accurately predicting left ventricular adverse remodeling following TAVR in patients with symptomatic severe aortic stenosis.

Background Pulmonary cryptococcosis (PC) is an invasive pulmonary fungal disease, and nodule/mass-type PC may mimic lung cancer (LC) in imaging appearance. Thus, an accurate diagnosis of nodule/mass-type PC is beneficial for appropriate management. However, the differentiation of nodule/mass-type PC from LC through computed tomography (CT) is still challenging. Purpose To develop and externally test a CT-based radiomics model for differentiating nodule/mass-type PC from LC. Methods In this retrospective study, patients with nodule/mass-type PC or LC who underwent non-enhanced chest CT were included: Institution 1 was for the training set, and institutions 2 and 3 were for the external test set. Large quantities of radiomics features were extracted. The radiomics score (Rad-score) was calculated using the linear discriminant analysis, and a subsequent fivefold cross-validation was performed. A combined model was developed by incorporating Rad-score and clinical factors. Finally, the models were tested with an external test set and compared using the area under the receiver operating characteristic curve (AUC). Results A total of 168 patients (45 with PC and 123 with LC) were in the training set, and 72 (36 with PC and 36 with LC) were in the external test set. Of the 81 patients with PC, 30 were immunocompromised (37%). Rad-score, comprising 18 features, had an AUC of 0.844 after fivefold cross-validation, which was lower than that (AUC = 0.943, p = 0.003) of the combined model integrating Rad-score, age, lobulation, pleural retraction, and patches. In the external test set, Rad-score and the combined model obtained good predictive performance (AUC = 0.824 for Rad-score, and 0.869 for the combined model). Moreover, the combined model outperformed the clinical model in the cross-validation and external test (0.943 vs. 0.810, p <0.001; 0.869 vs. 0.769, p = 0.011). Conclusions The proposed combined model exhibits a good differential diagnostic performance between nodule/mass-type PC and LC. The CT-based radiomics analysis has the potential to serve as an effective tool for the differentiation of nodule/mass-type PC from LC in clinical practice.

Objective:To investigate the differential diagnostic performance of computed tomography (CT)-based radiomics in thymic epithelial tumors (TETs) and lymphomas in anterior mediastinum.Methods:There were 149 patients with TETs and 93 patients with lymphomas enrolled. These patients were assigned to a training set (n = 171) and an external validation set (n = 71). Dedicated radiomics prototype software was used to segment lesions on preoperative chest enhanced CT images and extract features. The multivariable logistic regression algorithm was used to construct three models according to clinico-radiologic features, radiomics features, and combined features, respectively. Performance of the three models was compared by using the area under the receiver operating characteristic curves (AUCs). Decision curve analysis was used to evaluate clinical utility of the three models.Results:For clinico-radiologic model, radiomics signature model, and combined model, the AUCs were 0.860, 0.965, 0.975 and 0.843, 0.961, 0.955 in the training cohort and the test cohort, respectively (all P<0.05). The accuracies of each model were 0.836, 0.895, 0.918 and 0.845, 0.901, 0.859 in the two cohorts, respectively (all P<0.05). Compared with the clinico-radiologic model, better diagnostic performances were found in the radiomics signature model and the combined model.Conclusions:Radiomics signature model and combined model exhibit outstanding and comparable differential diagnostic performances between TETs and lymphomas. The CT-based radiomics analysis might serve as an effective tool for accurately differentiating TETs from lymphomas before treatment.

目的 探讨卵圆孔未闭(PFO)在心动周期中的形态学、动态特征及其邻近异常结构.方法 收集59例行心电门控双源计算机断层成像发现PFO的成年患者影像学资料,评价PFO及邻近解剖结构特征、心动周期对PFO显示的影响.解剖评价内容:PFO大小、卵圆窝大小、继发房间隔厚度及是否合并房间隔瘤.结果 PFO平均长度、宽度和高度分别为(9.5±4.6)mm、(6.4±4.0)mm和(2.1±0.6)mm,卵圆窝大小为(17.1±5.3)mm×(15.4±4.2)mm,继发房间隔宽度为(6.5±2.4)mm.6例患者(10.1％)合并房间隔瘤.在心动周期中,多数患者PFO最佳图像质量见于70％R-R间期.结论 PFO显示最佳时相为70％R-R间期,心电门控双源计算机断层成像可评价PFO的精确解剖及周围结构特征,为PFO封堵提供详细的术前解剖数据.