A coronary CT angiography-derived myocardial radiomics model for predicting adverse outcomes in chronic myocardial infarction.

BACKGROUND: The prognostic efficacy of a coronary computed tomography angiography (CCTA)-derived myocardial radiomics model in patients with chronic myocardial infarction (MI) is unclear. METHODS: In this retrospective study, a cohort of 236 patients with chronic MI who underwent both CCTA and cardiac magnetic resonance (CMR) examinations within 30 days were enrolled and randomly divided into training and testing datasets at a ratio of 7:3. The clinical endpoints were major adverse cardiovascular events (MACE), defined as all-cause death, myocardial reinfarction and heart failure hospitalization. The entire three-dimensional left ventricular myocardium on CCTA images was segmented as the volume of interest for the extraction of radiomics features. Five models, namely the clinical model, CMR model, clinical+CMR model, CCTA-radiomics model, and clinical+CCTA-radiomics model, were constructed using multivariate Cox regression. The prognostic performances of these models were evaluated through receiver operating characteristic curve analysis and the index of concordance (C-index). RESULTS: Fifty-one (20.16%) patients experienced MACE during a median follow-up of 1439.5 days. The predictive performance of the CCTA-radiomics model surpassed that of the clinical model, CMR model, and clinical+CMR model in both the training (area under the curve (AUC) of 0.904 vs. 0.691, 0.764, 0.785; C-index of 0.88 vs. 0.71, 0.75, 0.76, all p values <0.001) and testing (AUC of 0.893 vs. 0.704, 0.851, 0.888; C-index of 0.86 vs. 0.73, 0.85, 0.85, all p values <0.05) datasets. CONCLUSIONS: The CCTA-based myocardial radiomics model is a valuable tool for predicting adverse outcomes in chronic MI, providing incremental value to conventional clinical and CMR parameters.

Antigravity Autonomous Superwettable Pumps for Spontaneous Separation of Oil-Water Emulsions.

Oil-water separation based on superwettable materials offers a promising way for the treatment of oil-water mixtures and emulsions. Nevertheless, such separation techniques often require complex devices and external energy input. Therefore, it remains a great challenge to separate oil-water mixtures and emulsions through an energy-efficient, economical, and sustainable way. Here, a novel approach demonstrating the successful separation of oil-water emulsions using antigravity-driven autonomous superwettable pumps is presented. By transitioning from traditional gravity-driven to antigravity-driven separation, the study showcases the unprecedented success in purifying oil/water from emulsions by capillary/siphon-driven superwettable autonomous pumps. These pumps, composed of self-organized interconnected channels formed by the packing of superhydrophobic and superhydrophilic sand particles, exhibit outstanding separation flux, efficiency, and recyclability. The findings of this study not only open up a new avenue for oil-water emulsion separation but also hold promise for profound impacts in the field.

Association of stress hyperglycemia ratio with left ventricular function and microvascular obstruction in patients with ST-segment elevation myocardial infarction: a 3.0 T cardiac magnetic resonance study.

BACKGROUND: Stress hyperglycemia, which is associated with poor prognosis in patients with acute myocardial infarction (AMI), can be determined using the stress hyperglycemia ratio (SHR). Impaired left ventricular function and microvascular obstruction (MVO) diagnosed using cardiac magnetic resonance (CMR) have also been proven to be linked to poor prognosis in patients with AMI and aid in risk stratification. However, there have been no studies on the correlation between fasting SHR and left ventricular function and MVO in patients with acute ST-segment elevation myocardial infarction (ASTEMI). Therefore, this study aimed to investigate the additive effect of fasting SHR on left ventricular function and global deformation in patients with ASTEMI and to explore the association between fasting SHR and MVO. METHODS: Consecutive patients who underwent CMR at index admission (3-7 days) after primary percutaneous coronary intervention (PPCI) were enrolled in this study. Basic clinical, biochemical, and CMR data were obtained and compared among all patients grouped by fasting SHR tertiles: SHR1: SHR < 0.85; SHR2: 0.85 ≤ SHR < 1.01; and SHR3: SHR ≥ 1.01. Spearman's rho (r) was used to assess the relationship between fasting SHR and left ventricular function, myocardial strain, and the extent of MVO. Multivariable linear regression analysis was performed to evaluate the determinants of left ventricular function and myocardial strain impairment in all patients with AMI. Univariable and multivariable regression analyses were performed to investigate the correlation between fasting SHR and the presence and extent of MVO in patients with AMI and those with AMI and diabetes mellitus (DM). RESULTS: A total of 357 patients with ASTEMI were enrolled in this study. Left ventricular ejection fraction (LVEF) and left ventricular global function index (LVGFI) were significantly lower in SHR2 and SHR3 than in SHR1. Compared with SHR1 and SHR2 groups, left ventricular strain was lower in SHR3, as evidenced by global radial (GRS), global circumferential (GCS), and global longitudinal (GLS) strains. Fasting SHR were negatively correlated with LVEF, LVGFI, and GRS (r = - 0.252; r = - 0.261; and r = - 0.245; all P<0.001) and positively correlated with GCS (r = 0.221) and GLS (r = 0.249; all P <0.001). Multivariable linear regression analysis showed that fasting SHR was an independent determinant of impaired LVEF, LVGFI, GRS, and GLS. Furthermore, multivariable regression analysis after adjusting for covariates signified that fasting SHR was associated with the presence and extent of MVO in patients with AMI and those with AMI and DM. CONCLUSION: Fasting SHR in patients with ASTEMI successfully treated using PPCI is independently associated with impaired cardiac function and MVO. In patients with AMI and DM, fasting SHR is an independent determinant of the presence and extent of MVO.

Prognostic Value of Left Atrial Volume and Late Gadolinium Enhancement on Cardiac Magnetic Resonance Imaging in Patients with Coronary Artery Disease and Severe Left Ventricular Dysfunction Underwent CABG.

RATIONALE AND OBJECTIVES: This study aims to investigate whether the combination of Left atrial volume (LAV) and late gadolinium enhancement (LGE) is helpful in stratifying the risk in CABG patients with CAD with EF≤ 35%. MATERIALS AND METHODS: We conducted a retrospective analysis involving 205 CAD patients with EF≤ 35% who underwent CABG. All patients underwent gadolinium-enhanced CMR before surgery. The CMR images were analyzed for LAV, biventricular function, LGE, and left ventricular myocardial strain. Primary endpoint events included all-cause mortality, revascularization, re-hospitalization due to myocardial infarction or heart failure, and stroke after CABG. Multivariable Cox analysis was performed to identify independent risk factors for adverse outcomes. Kaplan-Meier curve analysis with the log-rank test was employed to evaluate survival estimates. RESULTS: A total of 55 patients reached the primary endpoints. Univariate Cox proportional hazard regression analysis showed that LAV index (LAVi), left ventricular EF (LVEF), right ventricular EF, LGE percent, and global longitudinal strain were significantly associated with the primary outcome (all P < 0.05). Multivariable analysis showed that LAVi (hazard ratio [HR] 1.05, [95% confidence interval (CI) 1.02-1.07], P < 0.001) and LGE percent (HR 1.10, [95% CI 1.06-1.15], P < 0.001) were independently associated with the primary outcome. Kaplan-Meier analysis indicated a significant increase in the risk of endpoint occurrence when patients exhibited LAVi≥ 51.0 mL/m2 and LGE≥ 11.6% (both P < 0.05). CONCLUSION: For CAD patients with LVEF≤ 35%, the combination of LAVi and LGE percent demonstrated good predictive value for adverse events after CABG. CMR is a helpful tool to risk-stratify patients with severe left ventricular dysfunction undergoing CABG.

Cardiac magnetic resonance feature tracking derived left atrial strain in the diagnosis of patients with constrictive pericarditis and restrictive cardiomyopathy.

Objective: To explore the diagnostic value of cardiac magnetic resonance feature tracking (CMR-FT) divided left atrial (LA) strain in differentiating constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Methods: Patients with CP (n = 40) and RCM (n = 40), and another 40 normal control group were retrospectively enrolled over a period of 8 years at a tertiary cardiac centre. Left ventricular (LV) and biatrial strain and strain rate (SR) were measured. Atrial strain was used to differentiate between patients with CP and RCM. Then, patients were grouped according to their left ventricular ejection fraction (LVEF), either ≥50% or < 50%. A deeper analysis was done to evaluate the diagnostic value of atrial strain in these subgroups. Receiver operating characteristic curves (ROC) were used to assess the accuracy of myocardial strain based on CMR FT for the differential diagnosis of CP and RCM. Results: LV and LA strain and SR were significantly lower in patients with CP and RCM than those in the normal controls (P < 0.05). LA strain and SR were significantly lower in the RCM group than in the CP group (P < 0.05). In patients with either LVEF≥50% or＜50%, LA strain were lower in the RCM group than in the CP group (P < 0.05). ROC analysis showed that LA stored strain (LA-εs) had a good differential diagnostic value for CP and RCM, with an area under the curve (AUC) of 0.811 and an optimal cutoff value of 6.98%, above this value it tends to develop CP. Further, an excellent differential diagnostic value was found in patients with LVEF＜50%, with an AUC of 0.955. Conclusion: LA strain analysis obtained by CMR-FT provides good differential diagnostic value for distinguishing CP from RCM, especially in patients with LVEF＜50%.

Cardiac magnetic resonance analysis of left atrium function in patients with pre-apical hypertrophic cardiomyopathy.

Background: Patients presenting with unexplained T wave inversion on electrocardiogram combined with thickened left ventricular apex but less than 15 mm had been proposed as a preclinical scope of apical hypertrophy cardiomyopathy (pre-ApHCM). However, analysis of left atrial (LA) function in these patients has not been studied. This study aims to evaluate the LA function in pre-ApHCM patients and compare it with patients with ApHCM using cardiac magnetic resonance (CMR) imaging. Methods: In this retrospective case-control study, a total of 3,593 CMR reports from Beijing Anzhen Hospital, Capital Medical University, China were reviewed. Finally, 31 pre-ApHCM patients were identified and 40 ApHCM and 31 normal controls were included for comparison. LA volumetric and strain were analyzed by CMR. Two-tailed one-way ANOVA was used to analyze the difference of three groups. Pearson correlation test was used for correlation analysis. Results: All of the volumetric parameters in pre-ApHCM group were higher than those in control group. LA reservoir (LA total EF, εs) and conduit function (LA passive EF, εe) parameters, were significantly different among the three groups, which were the lowest in the ApHCM group, intermediate in the pre-ApHCM group, and the highest in the control group ((all P<0.001). Compared with the control group, the LA booster pump function, both the booster EF and booster pump strain (εa) in ApHCM were impaired (P=0.003 and P=0.002 respectively). Meanwhile, only the εa was impaired (P=0.016) while LA booster EF was not (P=0.064) in the pre-ApHCM group, neither εa nor the booster EF show difference between the ApHCM and pre-ApHCM (P=0.272 and P=0.518 respectively). Conclusions: LA function features in pre-ApHCM patients were similar to ApHCM but different from the normal controls. In pre-ApHCM and ApHCM patients, LA reservoir and conduit function impaired earlier before left atrium enlarged and decreased progressively as apex thickens. These findings may help to understand the LA functional change from pre-ApHCM to ApHCM, and to detect subclinical changes in patients with pre-ApHCM before overt hypertrophy or clinical symptoms develop.

Ultra-Tough and Highly Stretchable Dual-Crosslinked Eutectogel Based on Coordinated and Non-Coordinated Two Types Deep Eutectic Solvent Mixture.

Eutectogels are gaining attention in flexible device applications for their superior ionic conductivity, stability, biocompatibility, and cost-effectiveness. However, most existing eutectogels suffer from low strength and toughness. Herein, ultra-tough and highly stretchable polyacrylamide (PAM) eutectogels featuring a dual-crosslinked network comprising chemical cross-linking and physical cross-linking facilitated by metal coordination bonds and hydrogen bonds are developed. This is achieved through a controlled strategy involving polymerization of acrylamide in a coordinated metal salt-type deep eutectic solvent (DES) combined with a non-coordinated choline chloride (ChCl)-type DES mixture. By varying the molar ratio of these two types of DES, exceptional and adjustable mechanical properties of the resulting eutectogel are achieved, including a high tensile strength ranging from 2.9 to 8.2 MPa and elongation at break ranging from 1725 to 747%, at a 70 wt% DES content. Furthermore, the reversible non-covalent crosslinking in these eutectogels enables self-recovery and self-healing capabilities of eutectogels. The prepared eutectogels also exhibit outstanding ionic conductivity (3.56 mS cm-1 ), making them well-suited for use as strain sensors in human motion detection. The toughening strategy is universally effective for creating tough eutectogels using coordinated metal salt-type DES with various metal ions, as well as a diverse range of coordinatable polymers.

Deep Learning-based Prediction of Percutaneous Recanalization in Chronic Total Occlusion Using Coronary CT Angiography.

Background CT is helpful in guiding the revascularization of chronic total occlusion (CTO), but manual prediction scores of percutaneous coronary intervention (PCI) success have challenges. Deep learning (DL) is expected to predict success of PCI for CTO lesions more efficiently. Purpose To develop a DL model to predict guidewire crossing and PCI outcomes for CTO using coronary CT angiography (CCTA) and evaluate its performance compared with manual prediction scores. MATERIALS AND METHODS: Participants with CTO lesions were prospectively identified from one tertiary hospital between January 2018 and December 2021 as the training set to develop the DL prediction model for PCI of CTO, with fivefold cross validation. The algorithm was tested using an external test set prospectively enrolled from three tertiary hospitals between January 2021 and June 2022 with the same eligibility criteria. All participants underwent preprocedural CCTA within 1 month before PCI. The end points were guidewire crossing within 30 minutes and PCI success of CTO.Results A total of 534 participants (mean age, 57.7 years ± 10.8 [SD]; 417 [78.1%] men) with 565 CTO lesions were included. In the external test set (186 participants with 189 CTOs), the DL model saved 85.0% of the reconstruction and analysis time of manual scores (mean, 73.7 seconds vs 418.2-466.9 seconds) and had higher accuracy than manual scores in predicting guidewire crossing within 30 minutes (DL, 91.0%; CT Registry of Chronic Total Occlusion Revascularization, 61.9%; Korean Multicenter CTO CT Registry [KCCT], 68.3%; CCTA-derived Multicenter CTO Registry of Japan (J-CTO), 68.8%; P < .05) and PCI success (DL, 93.7%; KCCT, 74.6%; J-CTO, 75.1%; P < .05). For DL, the area under the receiver operating characteristic curve was 0.97 (95% CI: 0.89, 0.99) for the training test set and 0.96 (95% CI: 0.90, 0.98) for the external test set. Conclusion The DL prediction model accurately predicted the percutaneous recanalization outcomes of CTO lesions and increased the efficiency of noninvasively grading the difficulty of PCI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Pundziute-do Prado in this issue.

A20 ameliorates advanced glycation end products-induced melanogenesis by inhibiting NLRP3 inflammasome activation in human dermal fibroblasts.

BACKGROUND: Advanced glycation end products (AGEs) promote melanogenesis through activating NLRP3 inflammasome in fibroblasts. Although A20 has been highlighted to inhibit NLRP3 inflammasome activation, its roles and mechanisms remain elusive in photoaging-associated pigmentation. OBJECTIVES: To determine the significance of fibroblast A20 in AGEs-induced NLRP3 inflammasome activation and pigmentation. METHODS: The correlation between A20 and AGEs or melanin was studied in sun-exposed skin and lesions of melasma and solar lentigo. We then investigated A20 level in AGEs-treated fibroblast and the effect of fibroblast A20 overexpression or knockdown on AGEs-BSA-induced NLRP3 inflammasome activation and pigmentation, respectively. Finally, the severity of NLRP3 inflammasome activation and pigmentation was evaluated after mice were injected intradermally with A20-overexpression adeno-associated virus and AGEs-BSA. RESULTS: Dermal A20 expression was decreased and exhibited negative correlation with either dermal AGEs deposition or epidermal melanin level in sun-exposed skin and pigmentary lesions. Moreover, both AGEs-BSA and AGEs-collagen robustly decreased A20 expression via binding to RAGE in fibroblasts. Further, A20 overexpression or depletion significantly decreased or augmented AGEs-BSA-induced activation of NF-κB pathway and NLRP3 inflammasome and IL-18 production and secretion in fibroblasts, respectively. Importantly, fibroblast A20 potently repressed AGEs-BSA-stimulated melanin content，tyrosinase activity，and expression of microphthalmia-associated transcription factor and tyrosinase in melanocytes. Particularly, fibroblast A20 significantly abrogated AGEs-BSA-promoted melanogenesis in ex vivo skin and mouse models. Additionally, fibroblast A20 inhibited AGEs-BSA-activated MAPKs in melanocytes and the epidermis of ex vivo skin. CONCLUSIONS: Fibroblast A20 suppresses AGEs-stimulate melanogenesis in photoaging-associated hyperpigmentation disorders by inhibiting NLRP3 inflammasome activation.

Generation of a FLNA knockout hESC line (WAe009-A-P) to model cardiac valvular dysplasia using CRISPR/Cas9.

The FLNA gene encodes the cytoskeletal protein filamin A which plays a key role in the structure and function of the cardiac valves. Truncating FLNA mutations are associated with cardiac valvular dysplasia. To further understand the exact role of FLNA in this disease, we have generated a human FLNA knockout cell line from H9 using CRISPR/Cas9 technology in this study. This cell line WAe009-A-P has a 2 bp deletion in the exon 2 of FLNA gene which resulted in a frameshift in the translation of FLNA and no FLNA protein was detected in this cell line. Moreover, WAe009-A-P also expressed pluripotency markers, had a normal female karyotype (46XX) and maintained the ability to differentiate into the three germ layers in vitro.

Impaired cardiac pump function assessment with normalized cardiac power using cardiac magnetic resonance in patients with hypertrophic cardiomyopathy.

Background: Cardiac power (CP; CP = 0.222 × cardiac output × mean blood pressure) output in patients with heart failure has been studied previously, but its importance in patients with hypertrophic cardiomyopathy (HCM) remains unclear. The present study aimed to explore the role of normalized CP (normalized CP = CP/ventricle mass) in assessing cardiac function in patients with HCM with normal ejection fraction using cardiac magnetic resonance (CMR). Methods: This cross-sectional study enrolled 99 patients with HCM who underwent CMR from December 2020 to January 2022 at Beijing Anzhen Hospital, and these patients were classified into heart failure or non-heart failure subgroups. Meanwhile, a control group comprising 65 gender- and age-matched healthy volunteers was also enrolled. The baseline clinical characteristics and cardiac functional parameters were compared between the patients with HCM and the controls, and multivariable linear regression analysis was performed to analyze the relationship between normalized CP and the relevant factors. Results: Significantly higher CP (1.19 vs. 1.01 W; P=0.03) but lower normalized CP (0.73 vs. 1.12 W/100 g; P<0.001) were found in patients with HCM as compared with the controls. Multivariable analysis showed that HCM correlated well with normalized CP [ß=-0.235; 95% confidence interval (CI): -0.341 to -0.129; P<0.001]. In the HCM group, there were 34 cases with heart failure and 65 with non-heart failure, and the patients with HCM with heart failure showed similar CP (1.14 vs. 1.24 W; P=0.06) but significantly lower normalized CP (0.54 vs. 0.78 W/100 g; P<0.001). The correlation analysis of normalized CP and functional parameters revealed that normalized CP was inversely correlated with left ventricle mass/body surface area (R=-0.509; 95% CI: -0.646 to -0.341; P<0.001) in patients with HCM. Conclusions: Normalized CP decreased significantly and was negatively correlated with ventricle mass, indicating impaired cardiac pump function in patients with HCM. Normalized CP might play a critical role in detecting and evaluating impaired cardiac pump function in patients with HCM with preserved ejection fraction.

Deep learning-based prognostic model using non-enhanced cardiac cine MRI for outcome prediction in patients with heart failure.

OBJECTIVES: To evaluate the performance of a deep learning-based multi-source model for survival prediction and risk stratification in patients with heart failure. METHODS: Patients with heart failure with reduced ejection fraction (HFrEF) who underwent cardiac magnetic resonance between January 2015 and April 2020 were retrospectively included in this study. Baseline electronic health record data, including clinical demographic information, laboratory data, and electrocardiographic information, were collected. Short-axis non-contrast cine images of the whole heart were acquired to estimate the cardiac function parameters and the motion features of the left ventricle. Model accuracy was evaluated using the Harrell's concordance index. All patients were followed up for major adverse cardiac events (MACEs), and survival prediction was assessed using Kaplan-Meier curves. RESULTS: A total of 329 patients were evaluated (age 54 ± 14 years; men, 254) in this study. During a median follow-up period of 1041 days, 62 patients experienced MACEs and their median survival time was 495 days. When compared with conventional Cox hazard prediction models, deep learning models showed better survival prediction performance. Multi-data denoising autoencoder (DAE) model reached the concordance index of 0.8546 (95% CI: 0.7902-0.8883). Furthermore, when divided into phenogroups, the multi-data DAE model could significantly discriminate between the survival outcomes of the high-risk and low-risk groups compared with other models (p < 0.001). CONCLUSIONS: The proposed deep learning (DL) model based on non-contrast cardiac cine magnetic resonance imaging could independently predict the outcome of patients with HFrEF and showed better prediction efficiency than conventional methods. CLINICAL RELEVANCE STATEMENT: The proposed multi-source deep learning model based on cardiac magnetic resonance enables survival prediction in patients with heart failure. KEY POINTS: • A multi-source deep learning model based on non-contrast cardiovascular magnetic resonance (CMR) cine images was built to make robust survival prediction in patients with heart failure. • The ground truth definition contains electronic health record data as well as DL-based motion data, and cardiac motion information is extracted by optical flow method from non-contrast CMR cine images. • The DL-based model exhibits better prognostic value and stratification performance when compared with conventional prediction models and could aid in the risk stratification in patients with HF.

Machine learning-based radiomics analysis for predicting local recurrence of primary dermatofibrosarcoma protuberans after surgical treatment.

BACKGROUND AND PURPOSE: Dermatofibrosarcoma protuberans (DFSP) is characterized by locally invasive growth patterns and high local recurrence rates. Accurately identifying patients with high local recurrence risk may benefit patients during follow-up and has potential value for making treatment decisions. This study aimed to investigate whether machine learning-based radiomics models could accurately predict the local recurrence of primary DFSP after surgical treatment. MATERIALS AND METHODS: This retrospective study included a total of 146 patients with DFSP who underwent MRI scans between 2010 and 2016 from two different institutions: institution 1 (n = 104) for the training set and institution 2 (n = 42) for the external test set. Three radiomics random survival forest (RSF) models were developed using MRI images. Additionally, the performance of the Ki67 index was compared with the three RSF models in the external validation set. RESULTS: The average concordance index (C-index) scores of the RSF models based on fat-saturation T2W (FS-T2W) images, fat-saturation T1W with gadolinium contrast (FS-T1W + C) images, and both FS-T2W and FS-T1W + C images from 10-fold cross-validation in the training set were 0.855 (95% CI: 0.629, 1.00), 0.873 (95% CI: 0.711, 1.00), and 0.875 (95% CI: 0.688, 1.00), respectively. In the external validation set, the C-indexes of the three trained RSF models were higher than that of the Ki67 index (0.838, 0.754, and 0.866 vs. 0.601, respectively). CONCLUSION: Random survival forest models developed using radiomics features derived from MRI images were proven helpful for accurate prediction of local recurrence of primary DFSP after surgical treatment and showed better predicting performance than the Ki67 index.

Survival prediction of heart failure patients using motion-based analysis method.

BACKGROUND AND OBJECTIVE: Survival prediction of heart failure patients is critical to improve the prognostic management of the cardiovascular disease. The existing survival prediction methods focus on the clinical information while lacking the cardiac motion information. we propose a motion-based analysis method to predict the survival risk of heart failure patients for aiding clinical diagnosis and treatment. METHODS: We propose a motion-based analysis method for survival prediction of heart failure patients. First, our method proposes the hierarchical spatial-temporal structure to capture the myocardial border. It promotes the model discrimination on border features. Second, our method explores the dense optical flow structure to capture motion fields. It improves the tracking capability on cardiac images. The cardiac motion information is obtained by fusing boundary information and motion fields of cardiac images. Finally, our method proposes the multi-modality deep-cox structure to predict the survival risk of heart failure patients. It improves the survival probability of heart failure patients. RESULTS: The motion-based analysis method is confirmed to be able to improve the survival prediction of heart failure patients. The precision, recall, F1-score, and C-index are 0.8519, 0.8333, 0.8425, and 0.8478, respectively, which is superior to other state-of-the-art methods. CONCLUSIONS: The experimental results show that the proposed model can effectively predict survival risk of heart failure patients. It facilitates the application of robust clinical treatment strategies.

Longitudinal changes of health-related quality of life over 10 years in breast cancer patients treated with radiotherapy following breast-conserving surgery.

PURPOSES: The study intended to (1) assess changes of health-related quality of life (HRQoL) between early treatment-related time points and 10 years post-treatment in a cohort of breast cancer (BC) patients who received radiotherapy (RT), (2) to evaluate differences in HRQoL between long-term BC survivors and unaffected women from the same geographical region and (3) to identify determinants of long-term HRQoL in the survivor cohort. METHODS: 292 BC patients were recruited prior to RT after breast-conserving surgery between 1998 and 2001 in Germany and prospectively followed up for a median of 11.4 years (range 10.3-12.8 years). HRQoL was assessed using EORTC QLQ-C30 at pre-RT (baseline), during RT, 6 weeks after RT, and at the 10-year follow-up. Changes in mean HRQoL scores over time were assessed using linear-mixed models. HRQoL in long-term survivors and controls was compared using Wilcoxon rank-sum test, stratified by age groups. Multivariable linear regression models were used to identify determinants for HRQoL in long-term BC survivors. RESULTS: Compared to baseline level (mean summary score of 64.9), global health status/quality of life (GHS/QoL) declined during RT (62.4) and improved 6 weeks after RT (69.9) before decreasing to baseline level at the 10-year follow-up (66.7). Most functional domains deteriorated or remained stable at 10 years post-diagnosis compared to post-RT scores, except for role functioning which improved, while dyspnea and diarrhea significantly deteriorated between those two time points. There were no significant differences in long-term GHS/QoL between BC survivors 10 years post-RT and controls for all age groups (p > 0.05). However, deficits in specific HRQoL domains such as emotional burden, sleep problems or fatigue were found to more strongly affect survivors, in particular those younger than 65 years, compared to controls. In the determinant analysis, being overweight was associated with lower GHS/QoL and physical functioning, while living with others was found to be associated with better physical functioning, and decreased dyspnea and pain levels. Certain comorbidities such as depression had a strong association with multiple HRQoL domains, including lower GHS/QoL and functioning as well as a higher level of fatigue, pain, sleep/intestinal problems, and financial difficulties. Side effects such as lymphedema/pain and fibrosis were associated with worse physical and social functioning, respectively. CONCLUSION: The long-term GHS/QoL remained comparable when compared with the control population while restrictions in certain functional and symptoms domains in long-term BC survivors persisted over 10 years, in particular among younger survivors. Targeted screening to identify cancer survivors at risk for psychosocial/other impairment accounting also for comorbidities and treatment side effects may be warranted in long-term aftercare to address unmet health needs.

Clinical implications and molecular features of tertiary lymphoid structures in stage I lung adenocarcinoma.

AIMS: We investigated the clinical implications and molecular features of TLS in stage I lung adenocarcinoma (LUAD). METHODS: We retrospectively reviewed the clinicopathological characteristics of 540 patients with p-stage I LUAD. Logistic regression analysis was applied to determining the relationships between clinicopathological features and the presence of TLS. TLS-associated immune infiltration pattern and signature genes were characterized using the transcriptomic profiles of 511 LUADs from The Cancer Genome Atlas (TCGA) database. RESULTS: The presence of TLS was associated with a higher pT stage, low- and middle-grade patterns, and the absence of tumor spreading through air spaces (STAS) and subsolid nodules. Multivariate Cox regression analysis identified that the presence of TLS was associated with favorable overall survival (OS) (p < 0.001) and recurrence-free survival (RFS) (p < 0.001). Subgroup analysis showed that the most favorable OS (p < 0.001) and RFS (p < 0.001) favored the TLS + PD-1- subgroup. The presence of TLS was characterized by abundance in antitumor immunocytes including activated CD8+ T and B cells as well as dentritic cells in TCGA cohort. CONCLUSION: The presence of TLS was an independent favorable factor for patients with stage I LUAD. The presence TLS was featured by special immune profiles which might aid oncologists in determining personalized adjuvant treatment.

Prognosis evaluation of chronic inflammatory cardiomyopathy with ring-like late gadolinium enhancement.

AIMS: Ring-like late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) imaging is a special LGE pattern in patients with chronic inflammatory cardiomyopathy (infl-CMP), which is associated with cardiac dysfunction and ventricular remodelling and attributed to viral infection followed by bacterial and parasitic infections. Data on the combination of CMR imaging and clinical parameters that can add long-term prognostic value in patients with infl-CMP are still rare. We aimed to evaluate the prognostic value of CMR in risk stratification in this kind of patients. METHODS AND RESULTS: A total of 319 consecutive patients with clinically suspected myocarditis were retrospectively identified. Forty-seven patients with ring-like LGE on CMR who diagnosed as infl-CMP, and 72 patients with other LGE pattern were eligible for standardized follow-up. The left ventricle (LV) and right ventricle function and mass were analyses by CMR. Myocardial strain of the ventricles was evaluated by feature tracking. Major (cardiac death, resuscitated cardiac arrest, ventricular assist device, transplantation, and appropriate implantable cardioverter-defibrillator shock) and minor (rehospitalization due to heart failure and sustained atrial fibrillation) adverse cardiovascular events were assessed during follow-up since the date of nCMR examination. Cox proportional hazards model was used to investigate which of the prognostic factors identified by univariable analysis were significantly associated with cardiac events. In the ring-like LGE group, adverse cardiac events occurred in 14 (31.11%) patients, including 7 deaths (15.56%), 6 (13.33%) heart-failure hospitalizations, and 1 (2.22%) case of sustained atrial fibrillation during the mean follow-up period of 70.15 ± 45.68 months (interquartile range: 32.83-103.71 months). No major or minor adverse cardiac event occurred in the other LGE pattern group, except rehospitalization in one patient due to arrhythmia. Further analyses of ring-like LGE group by univariable and Multivariable Cox proportional hazard regression analysis showed that body mass index (BMI) (hazard ratio [HR]: 0.54, 95% confidence interval [CI]: 0.40-0.73, P = 0.000), right ventricle cardiac index (RVCI) (HR: 0.31, 95% CI: 0.14-0.71, P = 0.002), LV basal peak circumferential strain (LV-PCSbasal ) (HR: 1.26, 95% CI: 1.11-1.43, P = 0.000) were independently associated with the long-term outcome. Receiver operator characteristic curve indicated that the cut-off of LV-PCSBasal was -7.95%, and it has added prognostic value to BMI and RVCI. CONCLUSIONS: For infl-CMP patients with ring-like LGE on CMR, low BMI and RVCI were associated with a poor prognosis. LV-PCSBasal with a cut-off of -7.95% can add prognostic value for patients with infl-CMP who have ring-like LGE.

Quantification of Early Diffuse Myocardial Fibrosis Through 7.0 T Cardiac Magnetic Resonance T1 Mapping in a Type 1 Diabetic Mellitus Mouse Model.

BACKGROUND: Diffuse myocardial interstitial fibrosis (DMIF) is a key factor for heart failure (HF) in diabetic cardiomyopathy. MRI T1-mapping technique can quantitatively evaluate DMIF. PURPOSE: To evaluate of early DMIF in a type 1 diabetes mellitus (T1DM) mouse model through 7.0 T MRI T1 mapping. STUDY TYPE: Prospective. ANIMAL MODEL: A total of 50 8-week-old C57Bl/6J male mice were divided into control (n = 20) and T1DM (n = 30) groups. FIELD STRENGTH/SEQUENCE: A 7.0 T small animal MRI; gradient echo Look-Locker inversion recovery T1-mapping sequence; cine MRI. Scans were acquired in control and T1DM mice every 4 weeks until 24 weeks. ASSESSMENT: End-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), left ventricle (LV) mass, fractional shortening (FS), and E/A ratio. They were evaluated through echocardiography and cine MRI. The extracellular volume fraction (ECV) was calculated. Sirius Red staining was performed and calculated collagen volume fraction (CVF). STATISTICAL TESTS: Differences in ECV and CVF between two groups were analyzed using one-way analysis of variance. The correlation between ECV and CVF was assessed using Pearson's correlations. RESULTS: Compared with the control group, a progressive decrease in FS, EF, and E/A ratio was observed in the T1DM group. Both ECV and CVF values gradually increased during diabetes progression. A significant increase in ECV and CVF values was observed at 12 weeks (ECV: 32.5% ± 1.6% vs. 28.1% ± 1.8%; CVF: 6.9% ± 1.8% vs. 3.3% ± 1.1%). ECV showed a strong correlation with CVF (r = 0.856). DATA CONCLUSION: ECV is an accurate and feasible imaging marker that can be used to quantitatively assess DMIF changes over time in T1DM mice. ECV has potential to accurately detect DMIF in the early stage and may be a useful imaging tool to assess the need for early intervention in T1DM mice. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 3.

Artificial intelligence-based full aortic CT angiography imaging with ultra-low-dose contrast medium: a preliminary study.

OBJECTIVES: To further reduce the contrast medium (CM) dose of full aortic CT angiography (ACTA) imaging using the augmented cycle-consistent adversarial framework (Au-CycleGAN) algorithm. METHODS: We prospectively enrolled 150 consecutive patients with suspected aortic disease. All received ACTA scans of ultra-low-dose CM (ULDCM) protocol and low-dose CM (LDCM) protocol. These data were randomly assigned to the training datasets (n = 100) and the validation datasets (n = 50). The ULDCM images were reconstructed by the Au-CycleGAN algorithm. Then, the AI-based ULDCM images were compared with LDCM images in terms of image quality and diagnostic accuracy. RESULTS: The mean image quality score of each location in the AI-based ULDCM group was higher than that in the ULDCM group but a little lower than that in the LDCM group (all p < 0.05). All AI-based ULDCM images met the diagnostic requirements (score ≥ 3). Except for the image noise, the AI-based ULDCM images had higher attenuation value than the ULDCM and LDCM images as well as higher SNR and CNR in all locations of the aorta analyzed (all p < 0.05). Similar results were also seen in obese patients (BMI > 25, all p < 0.05). Using the findings of LDCM images as the reference, the AI-based ULDCM images showed good diagnostic parameters and no significant differences in any of the analyzed aortic disease diagnoses (all K-values > 0.80, p < 0.05). CONCLUSIONS: The required dose of CM for full ACTA imaging can be reduced to one-third of the CM dose of the LDCM protocol while maintaining image quality and diagnostic accuracy using the Au-CycleGAN algorithm. KEY POINTS: • The required dose of contrast medium (CM) for full ACTA imaging can be reduced to one-third of the CM dose of the low-dose contrast medium (LDCM) protocol using the Au-CycleGAN algorithm. • Except for the image noise, the AI-based ultra-low-dose contrast medium (ULDCM) images had better quantitative image quality parameters than the ULDCM and LDCM images. • No significant diagnostic differences were noted between the AI-based ULDCM and LDCM images regarding all the analyzed aortic disease diagnoses.