Prevalence and effect on prognosis of sarcopenia in patients with primary biliary cholangitis.

Background: Sarcopenia adversely affects the treatment outcomes in Cirrhosis and NAFLD. However, such research is limited in primary biliary cholangitis (PBC) patients. This study was performed to examine the prevalence of sarcopenia and its impact on PBC patients' prognoses. Methods: This study enrolled confirmed PBC patients who had an abdominal CT scan. Sarcopenia was determined by the L3-skeletal muscle index with a Chinese population-based cut-off value. Laboratory test values and liver stiffness measurements values were obtained from the electronic medical records. Results: In total, 174 PBC patients with a median age of 54 (IQR, 48, 62) years old, were enrolled. 45 (25.9%) patients among them were diagnosed with sarcopenia. Univariate and multivariate logistic regression results illustrated that male gender (OR = 9.152, 95%CI = 3.131-26.751, p < 0.001) and LSM ≥ 12.8 kPa (OR = 4.539, 95%CI = 1.651, 12.478, p = 0.003) were the independent risk factors of sarcopenia in PBC patients. In the prognosis analysis, sarcopenia was determined as a risk factor for indicating adverse events in PBC patients (HR = 4.058, 95%CI = 1.955-8.424, p < 0.001) by Cox proportional hazards regression. Conclusion: The current findings illustrate that comprehensive evaluation and management of sarcopenia may contribute to the improvement of treatment outcomes and life quality of PBC patients.

Prognostic Value of Non-alcoholic Fatty Liver Disease and RCA Pericoronary Adipose Tissue CT Attenuation in Patients with Acute Chest Pain.

RATIONALE AND OBJECTIVES: Pericoronary adipose tissue (PCAT) CT attenuation of right coronary artery (RCA) and non-alcoholic fatty liver disease (NAFLD) have prognostic value for major adverse cardiovascular events (MACE) in patients with coronary artery disease. However, the superior prognostic value between RCA PCAT CT attenuation and NAFLD remains unclear in patients with acute chest pain. This study is to evaluate the prognostic value of NAFLD for MACE, and further assess the incremental prognostic value of NAFLD over PCAT CT attenuation. MATERIALS AND METHODS: Between January 2011 and December 2021, all consecutive emergency patients with acute chest pain referred for coronary CT angiography (CCTA) were retrospectively enrolled. MACE included unstable angina requiring hospitalization, coronary revascularization, non-fatal myocardial infarction, and all-cause death. Patients' baseline and CCTA characteristics, RCA PCAT CT attenuation, and the presence of NAFLD were used to evaluate risk factors of MACE using multivariable Cox regression analysis. The prognostic value of NAFLD compared to RCA PCAT CT attenuation was analyzed. RESULTS: A total of 514 patients were enrolled (mean age, 58.36 ± 13.05 years; 310 men). During a median follow-up of 31 months, 60 patients (11.67%) experienced MACE. NAFLD (HR = 2.599, 95% CI: 1.207, 5.598, P = 0.015) and RCA PCAT CT attenuation (HR = 1.026, 95% CI: 1.001, 1.051, P = 0.038) were independent predictors of MACE. The global Chi-square analysis showed that NAFLD improved the risk of MACE more than that using clinical risk factors and CCTA metrics (59.51 vs 54.44, P = 0.024) or combined with RCA PCAT CT attenuation (63.75 vs 59.51, P = 0.040). CONCLUSION: NAFLD and RCA PCAT CT attenuation were predictors of MACE. NAFLD had an incremental prognostic value beyond RCA PCAT CT attenuation for MACE in patients with acute chest pain. Adding CT-FFR into the risk prediction of patients with acute chest pain is worth considering.

Association of Objective Sleep Characteristics and Incident Angina Pectoris: A Longitudinal Analysis from the Sleep Heart Health Study.

Purpose: While prior research has highlighted a significant association between sleep characteristics and angina pectoris (AP) incidence, the link between sleep efficiency (SE) and angina remains unexplored. This study seeks to elucidate the relationship between AP and objectively quantified SE. Patients and Methods: We examined a cohort of 2990 participants (1320 males and 1670 females; mean age 63.69 ± 13.2 years) from the Sleep Heart Health Study. The main exposure variable was SE, as determined by baseline home polysomnography, while the primary outcome was the first incidence of angina pectoris (AP) during the period between the baseline polysomnography and the end of follow-up. A multivariate Cox regression model was utilized, controlling for factors such as age, gender, BMI, smoking and alcohol consumption habits, diabetes, hypertension, sleep duration, triglycerides, cholesterol, high-density lipoprotein, apnea-hypopnea index, nocturnal oxygen saturation, to analyze the relationship between SE and AP. Results: During an average follow-up of 11 years, 284 patients developed AP. The unadjusted Kaplan-Meier analysis identified the 2nd quartile of SE as having the lowest AP risk. The multivariate Cox proportional hazards model demonstrated a higher risk of AP in quartile 1 (HR, 1.679; 95% CI, 1.109-2.542; P <0.014) and quartile 3 (HR, 1.503; 95% CI, 1.037-2.179; P <0.031), compared to quartile 2 of SE. Upon stratified analysis, this relationship was particularly pronounced in hypertensive individuals. Conclusion: Our results highlight the critical role of optimal sleep efficiency in mitigating the risk of angina pectoris, especially among hypertensive individuals.

Prognostic value of coronary CT angiography and CT myocardial perfusion imaging among patients with and without Diabetes.

OBJECTIVES: Whether stress CT myocardial perfusion imaging (CT-MPI) improves risk assessment in patients with diabetes mellitus (DM) remains unexplored. We aimed to evaluate the prognostic value of coronary CT angiography (CCTA) and stress CT-MPI in suspected coronary artery disease (CAD) patients with and without DM. METHODS: A total of 334 patients with suspected CAD who underwent CCTA and stress CT-MPI from May 2020 to July 2021 were retrospectively analyzed. The endpoint was major adverse cardiovascular events (MACEs). Multivariable Cox regression analysis was used to evaluate the risk factors for MACEs, including clinical risk factors, CCTA characteristics and CT-MPI characteristics. RESULTS: After a median follow-up of 21 months,15 patients of the DM group and 16 patients of the non-DM group experienced MACEs. Multivariate Cox stepwise regression analysis showed that abnormal perfusion myocardial segments ratio was associated with MACEs after adjusting for clinical risk factors and CCTA characteristics in all patients (HR:1.023, p < 0.001), DM group (HR:1.024, p = 0.008) and non-DM group (HR:1.028, p = 0.003). By adding CT-MPI characteristics to CCTA characteristics and clinical risk factors, the global chi-square for predicting MACEs increased from 62.24 to 78.84 in all patients (p < 0.001), from 19.18 to 27.30 in DM group (p = 0.004) and from 39.51 to 48.65 in non-DM group (p = 0.003); the increment of C-index in all patients, DM group and non-DM group were 0.018, 0.054 and 0.019, respectively. CONCLUSION: In all patients and those with and without DM, CT-MPI has incremental prognostic value over clinical risk factors alone or combined with CCTA characteristics in predicting MACEs.

Lack of Incremental Prognostic Value of Pericoronary Adipose Tissue Computed Tomography Attenuation Beyond Coronary Artery Disease Reporting and Data System for Major Adverse Cardiovascular Events in Patients With Acute Chest Pain.

BACKGROUND: Pericoronary adipose tissue (PCAT) and Coronary Artery Disease Reporting and Data System (CAD-RADS) category had prognostic values for major adverse cardiovascular events (MACEs). However, little is known about the difference between CAD-RADS and PCAT computed tomography (CT) attenuation for predicting MACEs. This study was to compare the prognostic value of PCAT and CAD-RADS for MACEs in patients with acute chest pain. METHODS: Between January 2010 and December 2021, all consecutive emergency patients with acute chest pain referred for coronary computed tomography angiography were enrolled in this retrospective study. MACEs included unstable angina requiring hospitalization, coronary revascularization, nonfatal myocardial infarction, and all-cause death. Patients' clinical characteristics, CAD-RADS, and PCAT CT attenuation were used to evaluate risk factors of MACEs using multivariable Cox regression analysis. RESULTS: A total of 1313 patients were evaluated (mean age, 57.13±12.57 years; 782 men). During a median follow-up of 38 months, 142 of the 1313 patients (10.81%) experienced MACEs. Multivariable Cox regression analysis showed that CAD-RADS categories 2, 3, 4, 5 (hazard ratio range, 2.286-8.325; all P<0.005) and right coronary artery PCAT CT attenuation (hazard ratio, 1.033; P=0.006) were independent predictors of MACEs after adjusting for clinical risk factors. The C statistics revealed that CAD-RADS improved risk stratification compared with PCAT CT alone (C-index, 0.760 versus 0.712; P=0.036). However, the benefit of right coronary artery PCAT CT attenuation combined with CAD-RADS was not significant compared with CAD-RADS alone (0.777 versus 0.760; P=0.129). CONCLUSIONS: Right coronary artery PCAT CT attenuation and CAD-RADS were independent predictors of MACEs. However, no incremental prognostic value of right coronary artery PCAT CT attenuation beyond CAD-RADS was detected for MACEs in patients with acute chest pain.

Nonalcoholic fatty liver disease is associated with myocardial ischemia by CT myocardial perfusion imaging, independent of clinical and coronary CT angiography characteristics.

OBJECTIVE: The aim of this study was to evaluate whether patients with nonalcoholic fatty liver disease (NAFLD) have more myocardial malperfusion on CT myocardial perfusion imaging (CT-MPI), as well as to further assess if NAFLD is a predictor of myocardial ischemia independently. METHODS: A total of 310 consecutive patients were included for analysis. All patients were divided into two groups according to the presence or absence of NAFLD, which was diagnosed by noncontrast cardiac CT partially covered liver and spleen. Clinical characteristics as well as imaging features including coronary artery calcium score, CCTA, and CT-MPI findings were analyzed. Univariable and multivariable logistic regression analyses were used to find out the relationship between NAFLD and myocardial ischemia. RESULTS: NAFLD (unadjusted hazard ratio [HR]: 2.4, 95% confidence interval [CI]: 1.2 to 4.4, p = 0.008), male (HR: 2.6, 95% CI: 1.5 to 4.5, p = 0.001), obstructive CAD (HR: 2.3, 95% CI: 1.3 to 4.2, p = 0.004), and FAI ≥ -70.1 HU (HR: 3.1, 95% CI: 1.8 to 5.5, p < 0.001) were associated with myocardial ischemia in univariable analysis. After adjusting for traditional CAD risk factors and CT characteristics in the multivariable regression analysis, NAFLD (HR: 2.3, 95% CI: 1.2 to 4.4, p = 0.016) was an independent predictor of myocardial ischemia. CONCLUSION: Our data suggest that myocardial ischemia was more prevalent in patients with NAFLD, and NAFLD is a predictor of myocardial ischemia independent of traditional cardiovascular risk factors and CCTA characteristics. KEY POINTS: • NAFLD patients had higher calcium score, incidence of obstructive coronary artery disease, grade of CAD-RADS, quantitative plaque characteristics, and incidence of fat attenuation index ≥ -70.1 HU. • NAFLD patients had a higher incidence of myocardial ischemia, myocardial hypoperfusion, and hypoperfusion myocardial segments ratio. • NAFLD was a predictor of myocardial ischemia, independent of traditional cardiovascular risk factors, and CCTA characteristics.

Predictive Value of CT-Based Radiomics in Distinguishing Renal Angiomyolipomas with Minimal Fat from Other Renal Tumors.

Objectives: This study is aimed at determining whether CT-based radiomics models can help differentiate renal angiomyolipomas with minimal fat (AMLmf) from other solid renal tumors. Methods: This retrospective study included 58 patients with a postoperative pathologically confirmed AMLmf (observation group) and 140 patients with other common renal tumors (control group). Non-contrast-enhanced CT and contrast-enhanced CT data were evaluated. Radiomics features were extracted from manually delineated volume of interest (VOIs). The least absolute shrinkage and selection operator (LASSO) regression was used for feature screening. Five classifiers, including logistic regression, multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbor (KNN), and logistic regression (LR), were used, with leave-out validation (128 training, 60 testing). The diagnostic performance of the classifier was evaluated and compared by receiver operating characteristic curve (ROC) analysis. Results: Among the 1029 extracted features, prediction models of AMLmf were composed, by 2, 10, 4, and 9 selected features for precontrast phase (PCP), corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP), respectively. Models of CMP and NP achieved adequate performance after using MLP classifier, with prediction accuracy of 0.767 (AUC 0.85, sensitivity 0.76, and specificity 0.78) and 0.783 (AUC 0.83, sensitivity 0.79, and specificity 0.78), respectively. MLP model of features selected from the combination of the all features had the best diagnostic performance (accuracy 0.8500, sensitivity 0.8095, specificity 0.9444, and AUC 0.9193). Conclusions: Radiomics features may help to distinguish benign AMLmf from common malignant kidney masses, which may contribute to the selection of interventions for renal tumors.

Improved interobserver agreement on nodule type and Lung-RADS classification of subsolid nodules using computer-aided solid component measurement.

PURPOSE: The Lung CT Screening Reporting and Data System (Lung-RADS) classification of subsolid nodules (SSNs) can be challenging due to limited interobserver agreement in determining the type and size of the nodule. Our study aimed to assess the effect of a computer-aided method on the interobserver agreement of Lung-RADS classification for SSNs. MATERIALS AND METHODS: This study consisted of 156 SSNs in 121 patients who underwent initial CT screening for lung cancer. Three independent readers determined the nodule type and measured the size of the entire nodule as well as the solid component, first without and then assisted by a semi-automated computer-aided tool. They assigned to each nodule the corresponding Lung-RADS 1.1 category. Agreement in size measurements was assessed by intraclass correlation coefficient (ICC) and Bland-Altman indexes, while agreement in nodule type and Lung-RADS was determined using Fleiss kappa statistics. The relationship between final diagnosis of the nodules and Lung-RADS classifications was also evaluated. RESULTS: Among the 156 nodules, manual size measurement reached an ICC of 0.994, and 48 nodules contained solid component measured by all the three readers both manually and semi-automatically. ICCs for the solid component measurement were 0.952, 0.997 and 0.996 for manual diameter, semi- automated diameter and volume measurement, respectively. Bias and 95% limits of agreement for average diameter of solid component were smaller with semi-automated measurements than with manual measurements. Kappa values of semi-automated assessment for nodule type (0.974) and Lung-RADS classification (0.958 for diameter and 0.952 for volume) were higher than with the manual measurements (0.783 for nodule type and 0.652 for Lung-RADS classification). Compared to manual work, the semi-automated assessment identified more 4B nodules among the 26 pathologically confirmed invasive adenocarcinomas (IACs). CONCLUSION: Semi-automated assessment could improve the interobserver agreement of nodule type and Lung-RADS classification for SSNs, and be inclined to classify SSNs corresponding to pathologically confirmed IACs into higher risk categories.

Influence of Different Segmentations on the Diagnostic Performance of Pericoronary Adipose Tissue.

Objective: To investigate the influence of different segmentations on the diagnostic performance of pericoronary adipose tissue (PCAT) CT attenuation and radiomics features for the prediction of ischemic coronary artery stenosis. Methods: From June 2016 to December 2018, 108 patients with 135 vessels were retrospectively analyzed in the present study. Vessel-based PCAT was segmented along the 40 mm-long proximal segments of three major epicardial coronary arteries, while lesion-based PCAT was defined around coronary lesions. CT attenuation and radiomics features derived from two segmentations were calculated and extracted. The diagnostic performance of PCAT CT attenuation or radiomics models in predicting ischemic coronary stenosis were also compared between vessel-based and lesion-based segmentations. Results: The mean PCAT CT attenuation was -75.7 ± 9.1 HU and -76.1 ± 8.1 HU (p = 0.395) for lesion-based and vessel-based segmentations, respectively. A strong correlation was found between vessel-based and lesion-based PCAT CT attenuation for all cohort and subgroup analyses (all p < 0.01). A good agreement for all cohort and subgroup analyses was also detected between two segmentations. The diagnostic performance was comparable between vessel-based and lesion based PCAT CT attenuation in predicting ischemic stenosis. The radiomics features of PCAT based on vessel or lesion segmentation can both adequately identify the ischemic stenosis. However, no significant difference was detected between the two segmentations. Conclusions: The quantitative evaluation of PCAT can be reliably measured both from vessel-based and lesion-based segmentation. Furthermore, the radiomics analysis of PCAT may potentially help predict hemodynamically significant coronary artery stenosis.

Diagnostic performance of corrected FFRCT metrics to predict hemodynamically significant coronary artery stenosis.

OBJECTIVES: To determine the diagnostic performance of the fractional flow reserve (FFR) derived from coronary computed tomography angiography (CCTA) (FFRCT) difference across the lesion (ΔFFRCT lesion) or the vessel (ΔFFRCT vessel) and the gradient of FFRCT for the identification of hemodynamically significant coronary stenosis. METHODS: From June 2016 to December 2018, 73 patients suspected of having coronary artery disease who underwent CCTA followed invasive coronary angiography (ICA) within 1 month were retrospectively included. ΔFFRCT lesion, ΔFFRCT vessel, and FFRCT gradient were calculated. Performance characteristics of different corrected FFRCT metrics in detecting ischemic stenosis were analyzed. Impacts of coronary calcification and lesion length on the corrected FFRCT metrics were also analyzed. RESULTS: The diagnostic sensitivities, specificities, and accuracies of 94.4%, 88.7%, and 91.0% with ΔFFRCT lesion, 57.1%, 72.3%, and 65.2% with ΔFFRCT vessel, and 50.0%, 85.1%, and 68.5% with FFRCT gradient, respectively, were detected. There was higher specificity, accuracy, and area under the curve (AUC) for ΔFFRCT lesion compared with CCTA (p < 0.05 for all). The specificity and AUC of FFRCT gradient and ΔFFRCT vessel were significantly higher than CCTA (p < 0.05 for all). Coronary calcification showed no impact on corrected FFRCT metrics. ΔFFRCT lesion for lesion length ratio (LLR) < 1/10 was significantly lower than that for LLR 1/10 to 3/10 and LLR > 3/10. CONCLUSIONS: ΔFFRCT lesion was significantly correlated with the hemodynamically significant coronary artery stenosis. ΔFFRCT lesion had the potential to be immediately used in real-world practice to discriminate ischemic coronary artery stenosis. KEY POINTS: • The difference of FFRCT across the lesion or the vessel and the gradient of FFRCT was related to the hemodynamically significant coronary artery stenosis. • The difference of FFRCT across the lesion showed the best diagnostic performance in detecting the hemodynamically significant coronary artery stenosis. • Coronary calcification showed no impact on corrected FFRCT metrics, while lesion length related to the difference of FFRCT across the lesion.

Pericoronary adipose tissue CT attenuation and volume: Diagnostic performance for hemodynamically significant stenosis in patients with suspected coronary artery disease.

OBJECTIVES: The aim of this study was to investigate the diagnostic abilities of both pericoronary adipose tissue (PCAT) CT attenuation and volume for the predication hemodynamic significance of coronary artery stenosis as evaluated by fractional flow reserve (FFR). METHODS: Patients with ≥ 30 % in at least 1 major epicardial coronary artery were retrospectively included. Furthermore, all eligible patients underwent coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) as well as FFR within 1 month. PCAT CT attenuation and volume around ischemic and non-ischemic coronary stenosis were measured and compared. The diagnostic accuracy of PCAT CT attenuation and volume for the identification of hemodynamically significant stenosis was determined against the reference standard of FFR ≤ 0.80. RESULTS: A total of 61 patients (mean age, 57.8 years ± 11.8) with 77 vessels were included. Average PCAT CT attenuation of all vessels was -70.3 ± 7.4 HU. PCAT CT attenuation in coronary arteries with hemodynamically significant stenosis (FFR ≤ 0.80) (-65.6 ± 5.9 HU) was significantly higher than those with FFR > 0.80 (-75.3 ± 5.4 HU; p = 0.000). There was a strong correlation between FFR and PCAT CT attenuation (r = 0.64, p < 0.001). However, no significant difference in PCAT volume was observed between FFR ≤ 0.8 (5.0 ± 3.5 cm3) and FFR > 0.80 (5.5 ± 3.7 cm3, p = 0.511). The diagnostic accuracy was significantly higher in the combination of CCTA and PCAT CT attenuation compared with CCTA alone (area under the curve: 0.869 vs. 0.569, p < 0.001). CONCLUSIONS: PCAT CT attenuation but not volume was related to the hemodynamic significance of coronary artery stenosis. For the patients with suspected coronary artery disease, after adding of PCAT CT attenuation to CCTA, the diagnostic ability for the identification of ischemic coronary stenosis was significantly improved.

Predicting Disease-Free Survival With Multiparametric MRI-Derived Radiomic Signature in Cervical Cancer Patients Underwent CCRT.

Prognostic biomarkers that can reliably predict the disease-free survival (DFS) of locally advanced cervical cancer (LACC) are needed for identifying those patients at high risk for progression, who may benefit from a more aggressive treatment. In the present study, we aimed to construct a multiparametric MRI-derived radiomic signature for predicting DFS of LACC patients who underwent concurrent chemoradiotherapy (CCRT). METHODS: This multicenter retrospective study recruited 263 patients with International Federation of Gynecology and Obetrics (FIGO) stage IB-IVA treated with CCRT for whom pretreatment MRI scans were performed. They were randomly divided into two groups: primary cohort (n = 178) and validation cohort (n = 85). The LASSO regression and Cox proportional hazard regression were conducted to construct the radiomic signature (RS). According to the cutoff of the RS value, patients were dichotomized into low- and high-risk groups. Pearson's correlation and Kaplan-Meier analysis were conducted to evaluate the association between the RS and DFS. The RS, the clinical model incorporating FIGO stage and lymph node metastasis by the multivariate Cox proportional hazard model, and a combined model incorporating RS and clinical model were constructed to estimate DFS individually. RESULTS: The final radiomic signature consisted of four radiomic features: T2W_wavelet-LH_ glszm_Size Zone NonUniformity, ADC_wavelet-HL-first order_ Median, ADC_wavelet-HH-glrlm_Long Run Low Gray Level Emphasis, and ADC_wavelet _LL_gldm_Large Dependence High Gray Emphasis. Higher RS was significantly associated with worse DFS in the primary and validation cohorts (both p<0.001). The RS demonstrated better prognostic performance in predicting DFS than the clinical model in both cohorts (C-index, 0.736-0.758 for RS, and 0.603-0.649 for clinical model). However, the combined model showed no significant improvement (C-index, 0.648, 95% CI, 0.571-0.685). CONCLUSIONS: The present study indicated that the multiparametric MRI-derived radiomic signature could be used as a non-invasive prognostic tool for predicting DFS in LACC patients.

Preoperative Imaging Risk Findings for Postoperative New Stroke in Patients With Acute Type A Aortic Dissection.

Background: Stroke is a common postoperative complication in patients with acute type A aortic dissection (ATAAD). We aimed to explore the preoperative imaging risk findings for postoperative new stroke in patients with ATAAD. Methods: From January 2015 to December 2018, 174 patients with ATAAD who underwent preoperative aortic computed tomography angiography (CTA) and cerebral diffusion-weighted imaging (DWI) as well as postoperative brain CT were included, and divided into DWI (+) and DWI (-) groups. Pre- and intraoperative variables were collected, and logistic regression analysis was used to determine the independent risk predictors of postoperative new stroke. Results: The incidence of postoperative new stroke was 18.4% (32/174) in patients with ATAAD. Postoperative stroke was detected in 13 (31.0%) patients in the DWI (+) group and in 19 (14.4%) patients in the DWI (-) group with significant difference (P = 0.016). In the DWI (+) group, the lesions of the cerebral infarction located in the unilateral cerebral hemisphere and distributed more than three lobes (P = 0.007) were an independent risk factor for postoperative new stroke. Hypotension (P = 0.002), retrograde ascending aorta dissection with thrombosis of the false lumen (P = 0.010), aortic arch entry (P = 0.035), and coronary artery involvement (P = 0.001) were independent risk factors for postoperative stroke in the DWI (-) cohort. Conclusions: Patients with ATAAD with cerebral infarction are more likely to develop postoperative new stroke; thus, a preoperative DWI examination may be necessary. DWI lesions distributed more than 3 lobes in the unilateral hemisphere suggest a high possibility of postoperative stroke. For patients with ATAAD with normal brain, particular attention should be given to the CTA findings of false lumen thrombosis, aortic arch entry, and coronary artery involvement to avoid postoperative stroke.

Pressure-flow curve derived from coronary CT angiography for detection of significant hemodynamic stenosis.

OBJECTIVES: Coronary CT angiography (cCTA) has been used to non-invasively assess both the anatomical and hemodynamic significance of coronary stenosis. The current study investigated a new CFD-based method of evaluating pressure-flow curves across a stenosis to further enhance the diagnostic value of cCTA imaging. METHODS: Fifty-eight patients who underwent both cCTA imaging and invasive coronary angiography (ICA) with fractional flow reserve (FFR) within 2 weeks were enrolled. The pressure-flow curve-derived parameters, viscous friction (VF) and expansion loss (EL), were compared with conventional cCTA parameters including percent area stenosis (AS) and minimum lumen area (MLA) by receiver operating characteristic (ROC) curve analysis. FFR ≤ 0.80 was used to indicate ischemia-causing stenosis. Correlations between FFR and other measurements were calculated by Spearman's rank correlation coefficient (rho). RESULTS: Sixty-eight stenoses from 58 patients were analyzed. VF, EL, and AS were significantly larger in the group of FFR ≤ 0.8 while smaller MLA values were observed. The ROC-AUC of VF (0.91, 95% CI 0.81-0.96) was better than that of AS (change in AUC (ΔAUC) 0.27, p < 0.05) and MLA (ΔAUC 0.17, p < 0.05), and ROC-AUC of EL (0.90, 95%CI 0.80-0.96) was also better than that of AS (ΔAUC 0.26, p < 0.05) and MLA (ΔAUC 0.16, p < 0.05). FFR values correlated well with VF (rho = - 0.74 (95% CI - 0.83 to - 0.61, p < 0.0001) and EL (rho = - 0.74 (95% CI - 0.83 to - 0.61, p < 0.0001). CONCLUSION: Pressure-flow curve-derived parameters enhance the diagnostic value of cCTA examination. KEY POINTS: • Pressure-flow curve derived from cCTA can assess coronary lesion severity. • VF and EL are superior to cCTA alone for indicating ischemic lesions. • Pressure-flow curve derived from cCTA may assist in clinical decision-making.

Computed tomography angiography findings predict the risk factors for preoperative acute ischaemic stroke in patients with acute type A aortic dissection.

OBJECTIVES: Acute ischaemic stroke (AIS) is a highly dreaded complication of acute type A aortic dissection (ATAAD). Knowledge about independent predictors of preoperative AIS in patients with ATAAD remains unclear. The aim of this study was to identify the risk factors for preoperative AIS in patients with ATAAD by computed tomography angiography (CTA) findings. METHODS: Between October 2014 and March 2017, 281 (217 male; mean age, 50 years) patients with ATAAD underwent aortic CTA and diffusion-weighted magnetic resonance imaging of the brain was used to confirm the results. The clinical data and CTA findings were evaluated retrospectively. Patients were divided into 2 groups depending on the presence or absence of preoperative AIS. RESULTS: Preoperative AIS was detected in 103 (36.7%) of the patients with ATAAD. Univariable analysis of the clinical characteristics and CTA findings revealed that age, aortic valve insufficiency (moderate or severe), the ratio of the diameter of the true lumen of the ascending aorta to the diameter of the involved ascending aorta, intimal flap plaque, dissection of the common carotid artery (CCA), the lower density of the unilateral internal carotid artery, the CCA originating from the false lumen and dissection of the subclavian artery were implicated in patients with ATAAD with AIS. Multivariable analysis further showed that aortic valve insufficiency (moderate or severe) [odds ratio (OR) 2.033, 95% confidence interval (CI) 1.052-3.931; P = 0.035], 2 CTA findings including the ratio of the diameters (OR 0.074, 95% CI 0.011-0.516; P = 0.009) and dissection of the CCA (OR 2.422, 95% CI 1.389-4.224; P = 0.002) were independent risk predictors for preoperative AIS in patients with ATAAD. The lower density in the false lumen, the same enhancement in the true and false lumen with re-entry and the stenosis of the true lumen without re-entry significantly increased the risk of preoperative AIS in CCA dissection. CONCLUSIONS: Aortic valve insufficiency (moderate or severe), the ratio of the diameters of the true and false lumens and CCA dissection are independent predictors of preoperative AIS in patients with ATAAD. The specific carotid and aortic CTA findings may help to predict the risk factors for preoperative AIS in patients with ATAAD. CLINICAL REGISTRATION NUMBER: 20120216-4.

Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade.

PURPOSE: To evaluate the performance of machine learning (ML)-based computed tomography (CT) radiomics analysis for discriminating between low grade (WHO/ISUP I-II) and high grade (WHO/ISUP III-IV) clear cell renal cell carcinomas (ccRCCs). METHODS: A total of 164 low grade and 107 high grade ccRCCs were retrospectively analyzed in this study. Radiomic features were extracted from corticomedullary phase (CMP) and nephrographic phase (NP) CT images. Intraclass correlation coefficient (ICC) was calculated to quantify the feature's reproducibility. The training and validation cohort consisted of 163 and 108 cases. Least absolute shrinkage and selection operator (LASSO) regression method was used for feature selection. The machine learning (ML) classifiers were k-NearestNeighbor (KNN), Logistic Regression (LR), multilayer perceptron (MLP), Random Forest (RF), and support vector machine (SVM). The performance of classifiers was mainly evaluated and compared by certain metrics. RESULTS: Seven CMP features (ICC range, 0.990-0.999) and seven NP features (ICC range, 0.931-0.999) were selected. The accuracy of CMP, NP and the combination of CMP and NP ranged from 82.2%-85.9 %, 82.8%-94.5 % and 86.5%-90.8 % in the training cohort, and 90.7%-95.4%, 77.8%-79.6 % and 91.7%-93.5 % in the validation cohort. The AUC of CMP, NP and the combination of CMP and NP ranged from 0.901 to 0.938, 0.912 to 0.976, 0.948 to 0.968 in the training cohort, and 0.957 to 0.974, 0.856 to 0.875, 0.960 to 0.978 in the validation cohort. CONCLUSIONS: ML-based CT radiomics analysis can be used to predict the WHO/ISUP grade of ccRCCs preoperatively.

A DNA aptamer recognizing MMP14 for in vivo and in vitro imaging identified by cell-SELEX.

A key challenge for the management of various types of cancer, including pancreatic cancer and hepatocellular carcinoma, is accurate diagnosis at an early stage. Matrix metalloproteinase 14 (MMP14) is overexpressed in numerous types of cancer and is associated with poor prognosis. Therefore, MMP14-specific imaging probes have potential use in the diagnosis of MMP14-positive cancer. Aptamers are short oligonucleotide sequences that can bind to molecular targets with a high specificity and affinity. Aptamers are typically obtained from an in vitro library; this process is usually termed systematic evolution of ligands by exponential enrichment (SELEX). In the present study, a DNA aptamer targeting MMP14 was obtained by cell-SELEX and termed M17, which specifically recognizes MMP14-positive cells. Aptamer M17 selectively binds to membrane proteins of MMP14-transfected 293T cells (Kd, 4.98±1.26 nM). Pancreatic cancer cell imaging suggested that aptamer M17 can bind to the cell membranes of two pancreatic cancer cell lines (MIA PaCa-2 and PANC-1). In vivo tumor imaging demonstrated that the targeting recognition of MIA PaCa-2 tumor cells in mice could be visualized using Cy5-labeled aptamer M17. Aptamer M17-conjugated polyethylene glycol-Fe3O4 can specifically bind to MIA PaCa-2 and PANC-1 cells, and reduce MRI T2-weighted imaging signal intensity. The DNA aptamer M17 has the advantages of simplicity of synthesis, small size, low immunogenicity, high penetrability and high affinity. Therefore, aptamer M17 is a potential molecular probe for the diagnosis and treatment of MMP14-positive cancer.

Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade.

OBJECTIVES: To discriminate low grade (Fuhrman I/II) and high grade (Fuhrman III/IV) clear cell renal cell carcinoma (CCRCC) by using CT-based radiomic features. METHODS: 161 and 99 patients diagnosed with low and high grade CCRCCs from January 2011 to May 2018 were enrolled in this study. 1029 radiomic features were extracted from corticomedullary (CMP), and nephrographic phase (NP) CT images of all patients. We used interclass correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO) regression method to select features, then the selected features were constructed three classification models (CMP, NP and with their combination) to discriminate high and low grades CCRCC. These three models were built by logistic regression method using 5-fold cross validation strategy, evaluated with receiver operating characteristics curve (ROC) and compared using DeLong test. RESULTS: We found 11 and 24 CMP and NP features were independently significantly associated with the Fuhrman grades. The model of CMP, NP and Combined model using radiomic feature set showed diagnostic accuracy of 0.719 (AUC [area under the curve], 0.766; 95% CI [confidence interval]: 0.709-0.816; sensitivity, 0.602; specificity, 0.838), 0.738 (AUC, 0.818; 95% CI:0.765-0.838; sensitivity, 0.693; specificity, 0.838), 0.777(AUC, 0.822; 95% CI: 0.769-0.866; sensitivity, 0.677; specificity, 0.839). There were significant differences in AUC between CMP model and Combined model (P = 0.0208), meanwhile, the differences between CMP model and NP model, NP model and Combined model reached no significant (P = 0.0844, 0.7915). CONCLUSIONS: Radiomic features could be used as biomarker for the preoperative evaluation of the CCRCC Fuhrman grades.

Identification of CTA-Based Predictive Findings for Temporary and Permanent Neurological Dysfunction after Repair in Acute Type A Aortic Dissection.

The aim of this study was to determine CT risk findings predictive of temporary neurological dysfunction (TND) and permanent neurological dysfunction (PND) after surgical repair for acute type A aortic dissection (ATAAD). A total of 255 patients (41 ± 16 years, 79% male) with ATAAD underwent aortic CT angiography (CTA) and surgical repair consecutively from January 2013 to June 2016. The CTA findings of the 255 patients for the thoracic aorta and carotid artery were analysed to identify risk factors predictive of TND and PND. Thirty-eight patients (15%) suffered TND, and 18 (7%) exhibited PND. Common carotid artery (CCA) dissection (OR = 4.63), lower enhancement of unilateral ICA (OR = 3.02) and aortic arch tears (OR = 2.83) were predictors of postoperative TND, while PND was best predicted by retrograde ascending aorta (aAO) dissection (OR = 5.62) and aortic arch tears (OR = 6.74). In CCA dissection, the extent of the entire CCA and proximal ICA (P = 0.014), a low-enhancement false lumen with re-entry (P = 0.000) and a severely narrowed true lumen without re-entry (P = 0.005) significantly increased the risk of postoperative TND. In patients with ATAAD, specific CT findings allow the individual risk of postoperative TND and PND to be identified and may guide subsequent surgical management.

Quantitative parameters of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI): potential application in predicting pathological grades of pancreatic ductal adenocarcinoma.

BACKGROUND: The aim of this study was to compare intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) parameters such as standard apparent diffusion coefficient (ADCstandard), pure diffusion coefficient (Dslow), pseudodiffusion coefficient (Dfast) and perfusion fraction (ƒ) for differentiating pancreatic ductal adenocarcinoma (PDAC) with different pathological grades. METHODS: Institutional Review Board of our hospital approved this study protocol. Subjects comprised 38 PDACs confirmed by pathology. Pancreatic multiple b values DWI with 15 b values of 0, 10, 20, 40, 60, 80, 100, 150, 200, 400, 800, 1,000, 1200, 1,500, and 2,000 s/mm2 was performed using GE Discovery MR750 3.0T scanner. ADCstandard, Dslow, Dfast and ƒ values of all PDACs were calculated using mono- and bi-exponential models. Parameters of well/moderately differentiated and poorly differentiated PDAC were compared using Independent Sample t-test. P values <0.05 were considered significant. RESULTS: Mean Dslow value of well/moderately differentiated PDAC was significantly lower than that of poorly differentiated PDAC (0.540×10-3vs. 0.676×10-3 mm2/s, P<0.001). Mean ƒ value of well/moderately differentiated PDAC was significantly higher than that of poorly differentiated PDAC (60.3% vs. 38.4%, P<0.001). The area under curve value of ƒ in differentiating well/moderately differentiated PDAC from poorly differentiated PDAC was slightly higher than that of Dslow (0.894>0.865). When the Dslow value was less than or equal to 0.599×10-3 mm2/s, the sensitivity and specificity were 100% and 84.6% respectively. When ƒ value was greater than 49.6%, the sensitivity and specificity were 92.0% and 84.6% respectively. CONCLUSIONS: Dslow and ƒ derived from IVIM-DWI model can be used to distinguish well/moderately differentiated PDAC from poorly differentiated PDAC. And to serve this purpose, Dslow and ƒ have high diagnostic performance. IVIM-DWI is a promising and non-invasive tool for predicting pathological grade of PDAC.