Imaging Parameters Research Articles

Novel MRI Signs in Atlantodental Space Predict Reduction Degree of Atlantoaxial Dislocation.

The type of atlantodental space tissue in patients with atlantoaxial dislocation (AAD) can help doctors understand the possibility of reduction before surgery. However, relevant research on this topic is lacking. This study aimed to summarize cases of AAD, classified based on the atlantodental space using magnetic resonance imaging (MRI), and preliminarily explore its impact on the degree of reduction. Preoperative T2-weighted MRIs and dynamic digital radiographs of patients who underwent posterior reduction and fixation surgery for congenital AAD between September 2012 and February 2023 were collected. The patients were classified into flexible and inflexible tissue sign groups based on T2-weighted imaging. Patients with an atlantodental interval < 3 mm on extension digital radiography were considered radiographically reducible. Three radiologists read and recorded the MRI results using standard protocols. Kappa and Fleiss kappa values were used to evaluate intra- and inter-observer agreements for MRI signs and dynamic digital radiography findings. Multivariate logistic regression and receiver operating characteristic curves were used to analyze the relationships between imaging parameters and the reduction degree. In total, 118 patients with AAD were included in the analysis. Inter-observer agreement among the three readers was higher for MRI than for dynamic digital radiography (0.816 vs. 0.668). The intra-observer consistency for MRI signs was also better than that of dynamic digital radiography. Both the flexible tissue sign and radiographically reducible groups showed a higher rate of satisfactory reduction. However, only the flexible tissue sign showed positive results in the multivariate regression. The receiver operating characteristic curve for MRI signs as a predictor of satisfactory reduction yielded an area under the curve of 0.776 (95% confidence interval, 0.667-0.875, p < 0.0001). Novel MRI signs of the atlantodental space exhibited high inter- and intra-observer agreement. Patients with flexible tissue signs were more likely to achieve satisfactory reduction after direct posterior surgery.

Bone Mineral Density and First Line Imaging with [18F]fluorocholine PET/CT in Normocalcemic and Hypercalcemic Primary Hyperparathyroidism: Results from a Single Center

Objectives: Primary hyperparathyroidism (PHPT) is associated with normal or elevated calcium levels and affects bone mineral density. The proportion of cases predisposed to metabolic bone disease is unknown in patients with PHPT. The aim of this study was to assess bone mineral density and bone quality in patients with normo- or hypercalcemic primary hyperparathyroidism undergoing baseline parathyroid gland assessment with [18F]fluorocholine PET/CT imaging. Methods: A total of 140 consecutive patients were enrolled in this observational study. All patients with normo- or hypercalcemic primary hyperparathyroidism underwent dual-energy X-ray absorptiometry (DXA) for assessment of bone mineral density (BMD) and trabecular bone score (TBS). [18F]fluorocholine PET/CT was performed in all patients for the detection and localization of parathyroid adenoma. Hyper- and normocalcemic patients were compared with regard to the proportion of osteoporosis and osteopenia, T-Score, TBS, serum calcium, phosphorus and parathyroid hormone levels, the maximum standardized uptake value (SUVmax) in PET/CT imaging, and laboratory results. Results: The majority of patients was female (88.57%) and had a pathologic bone mineral density (52.86%). Overall, 33 patients had osteoporosis and 41 osteopenia. The mean lumbar T-Score was −1.48 (SD 1.37) and the T-Score of the femoral neck was −1.21 (SD 0.92). Mean TBS was also decreased (−2.13). No difference was found between normo- or hypercalcemic patients regarding bone metabolism and imaging parameters. Conclusions: More than half of patients with normo- or hypercalcemic PHPT showed abnormal BMD. First-line [18F]fluorocholine PET/CT identified parathyroid adenoma in a high proportion of patients, even in patients with normocalcemic PHPT. The early evaluation of metabolic bone disease seems desirable in clinical management of females with PHPT.

Dynamic Contrast-Enhanced and Diffusion-Weighted Imaging in Magnetic Resonance in the Assessment of Peritoneal Recurrence of Ovarian Cancer in Patients with or Without BRCA Mutation

Background: The aim of this study was to determine the differences in diffusion-weighted imaging (DWI) and dynamic contrast enhancement (DCE) parameters between patients with peritoneal high-grade serous ovarian cancer (HGSOC) recurrence with BRCA mutations (BRCAmut) or BRCA wild type (BRCAwt). Materials and Methods: We retrospectively analyzed the abdominal and pelvic magnetic resonance (MR) images of 43 patients suspected of having recurrent HGSOC, of whom 18 had BRCA1/2 gene mutations. Patients underwent MRI examination via a 1.5 T MRI scanner, and the analyzed parameters were as follows: apparent diffusion coefficient (ADC), time to peak (TTP) and perfusion maximum enhancement (Perf. Max. En.). Results: The mean ADC in patients with BRCAwt was lower than that in patients with BRCAmut: 788.7 (SD: 139.5) vs. 977.3 (SD: 103), p-value = 0.00002. The average TTP value for patients with BRCAwt was greater than that for patients with mutations: 256.3 (SD: 50) vs. 160.6 (SD: 35.5), p-value &lt; 0.01. The Perf. Max. En. value was lower in the BRCAwt group: 148.6 (SD: 12.3) vs. 233.6 (SD: 29.2), p-value &lt; 0.01. Conclusion: Our study revealed a statistically significant correlation between DWI and DCE parameters in examinations of peritoneal metastasis in patients with BRCA1/2 mutations. Adding DCE perfusion to the MRI protocol for ovarian cancer recurrence in patients with BRCAmut may be a valuable tool.

Diffusion weighted MRI and neutrophil lymphocyte ratio non-invasively predict infection in pancreatic necrosis: a pilot study.

Infected pancreatic necrosis (IPN) is a major determinant of mortality in acute pancreatitis (AP). Non-invasive diagnosis of IPN could guide the intervention in AP. We aimed to investigate the role of non-invasive methods like diffusion weighted magnetic resonance imaging (DW-MRI) and clinico-laboratory parameters as predictors of IPN. Prospective evaluation for predictors of IPN by diffusion restriction (DR) on DW-MRI and clinico-laboratory parameters was performed. Out of 39 patients included, 31 were analysed after exclusion. Twenty-six (83.8%) patients had moderately severe AP, and the rest had severe disease. They were categorized into Group A: patients with documented infection after intervention (n = 17) and Group B: successfully managed without intervention or negative culture after intervention (n = 14). On univariate analysis, Group A had significantly more incidence of fever (P = 0.020), persistent unwellness (P = 0.003), elevated neutrophil count (P = 0.007), lymphocyte count (P = 0.007), neutrophil lymphocyte ratio (NLR) (P = 0.028), DR on DW-MRI (P = 0.001) and low apparent diffusion coefficient (ADC) (P = 0.086). Multivariate analysis revealed DR on DW-MRI (P = 0.004) and NLR (P = 0.035) as significant predictors of IPN, among other factors. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of DW-MRI were 94.1%, 78.6%, 91.66%, and 84.21%, respectively. The area under curve of NLR on the ROC plot was 0.85 and the best cutoff was >3.5, with sensitivity, specificity, PPV, and NPV of 70.6%, 78.6%, 80%, and 68.7% respectively. DW-MRI and NLR are promising non-invasive tools for accurate prediction of IPN and hence can guide the need for intervention in acute pancreatitis.

Changes in resting-state functional connectivity of large-scale brain networks in bulimia nervosa: evidence from causal analysis.

Bulimia nervosa (BN) has been observationally linked to the functional connectivity (FC) of large-scale brain networks, but the biological mechanisms remain unclear. This study used two-sample Mendelian randomization (MR) with genetic variations as instrumental variables (IVs) to explore potential causal relationships between FC and BN. Summary data from genome-wide association studies (GWAS) involving 2,564 individuals were analyzed to identify genetically predicted BN. Functional magnetic resonance imaging parameters and materials were sourced from the UK Biobank. The variables underwent independent component analysis processing by the database to generate the final GWAS dataset. Various methods, including MR Pleiotropy RESidual Sum and Outlier, MR Egger, and weighted median, were employed to detect heterogeneity and pleiotropy, with inverse variance weighting serving as the principal estimation method (P < 0.05). The FC imaging-derived phenotypes revealed that BN exerted a causal influence on the FC between large-scale networks, including the visual network, default mode network (DMN), frontoparietal network, somatosensory network (SSN), and ventral attention network. Additionally, BN had a causal impact on the within-network FC of both the DMN and SSN. The study provides evidence that BN leads to further changes in FC patterns within and between large-scale brain networks.

The Clinical Application of Dynamic Contrast-Enhanced MRI in Canine Masses of Mesenchymal and Epithelial Origin: A Preliminary Case Series

Evaluating masses of mesenchymal and epithelial origin accurately using computed tomography (CT) has several limitations in dogs. This study aimed to present dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters to aid in improving the diagnostic accuracy for masses of mesenchymal and epithelial origin in veterinary medicine. Four dogs diagnosed with benign and malignant soft tissue sarcoma (STS), cholesteatoma, or squamous cell carcinoma underwent CT, conventional MRI, and DCE-MRI. Ktrans is a quantitative DCE-MRI parameter representing vascular permeability and tissue perfusion and is related to the potential for malignancy. Hemangiopericytomas (Grade II, STS) showed a higher Ktrans than normal muscle tissue and myxosarcoma (Grade I, STS). Squamous cell carcinoma (a malignant epithelial tumor) also showed a higher Ktrans than normal muscle tissue and cholesteatoma (a mass originating from keratinized squamous epithelium). These results suggest that higher Ktrans values may indicate a greater likelihood that a lesion is more malignant. In conclusion, Ktrans might be useful as a biomarker for evaluating the malignancy of a mass and as an indicator of lesion characteristics in dogs.

Application of tongue image characteristics and oral-gut microbiota in predicting pre-diabetes and type 2 diabetes with machine learning

BackgroundThis study aimed to characterize the oral and gut microbiota in prediabetes mellitus (Pre-DM) and type 2 diabetes mellitus (T2DM) patients while exploring the association between tongue manifestations and the oral-gut microbiota axis in diabetes progression.MethodsParticipants included 30 Pre-DM patients, 37 individuals with T2DM, and 28 healthy controls. Tongue images and oral/fecal samples were analyzed using image processing and 16S rRNA sequencing. Machine learning techniques, including support vector machine (SVM), random forest, gradient boosting, adaptive boosting, and K-nearest neighbors, were applied to integrate tongue image data with microbiota profiles to construct predictive models for Pre-DM and T2DM classification.ResultsSignificant shifts in tongue characteristics were identified during the progression from Pre-DM to T2DM. Elevated Firmicutes levels along the oral-gut axis were associated with white greasy fur, indicative of underlying metabolic changes. An SVM-based predictive model demonstrated an accuracy of 78.9%, with an AUC of 86.9%. Notably, tongue image parameters (TB-a, perALL) and specific microbiota (Escherichia, Porphyromonas-A) emerged as prominent diagnostic markers for Pre-DM and T2DM.ConclusionThe integration of tongue diagnosis with microbiome analysis reveals distinct tongue features and microbial markers. This approach significantly improves the diagnostic capability for Pre-DM and T2DM.

Enhancing preoperative diagnosis of pancreatic ductal adenocarcinoma and mass-forming chronic pancreatitis: a study on normalized conventional MR imaging parameters.

To assess the utility of signal intensity ratio (SIR) in distinguishing between mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma (PDAC), thereby reducing unnecessary pancreatectomies or delayed diagnosis brought by misdiagnosis. This retrospective study included 170 participants (34 with MFCP and 136 with PDAC) who underwent radical pancreatic surgery and were diagnosed via specimen pathology. The study group was carefully selected with a 1:4 ratio matching for sex, age, and operation time between two entities. T1 SIR, T2 SIR, arterial phase (AP) SIR, portal venous phase (VP) SIR, delay phase (DP) SIR, DWI0-50 SIR, and DWI500-1000 SIR, were calculated by dividing the signal intensity of lesions by that of the paraspinal muscle, serving as a reference organ. Intraclass Correlation Coefficient (ICC) was estimated to evaluate the intraobserver and interobserver reliability. Wilcoxon tests were employed for univariate analysis, and receiver operating characteristic (ROC) curves were generated to determine optimal cutoff points and AUC values for selected predictors. A tenfold cross-validation method was applied to validate the robustness of the results. The ICC demonstrated excellent correlation for both intraobserver and interobserver(ICCs > 0.8). T1 SIR, AP SIR, VP SIR, and DP SIR were significantly lower in the PDAC group compared to the MFCP group, and exhibited good independent predictive properties with the sensitivities of 61.8, 61.8, 70.6, and 73.5%, specificities of 66.2, 68.4, 59.6, and 55.9%, and AUCs of 0.620, 0.659, 0.670, and 0.668, respectively, hovering around 0.7. The tenfold cross-validation confirmed the reliability and robustness of our findings, with consistent AUC, sensitivity, specificity, and 95% confidence intervals over 1000 iterations. T1 SIR, AP SIR, VP SIR, and DP SIR show promise as potential imaging biomarkers for distinguishing between MFCP and PDAC.

Neutron tomography and image registration methods to study local physical deformations and attenuation variations in treated archaeological iron nail samples

This study presents a preliminary examination of the effects of environment changes post-excavation on heavily corroded archaeological Roman iron nails using neutron tomography and image registration techniques. Roman nails were exposed to either a high relative humidity environment, or fast thermal drying as primary experiments to show the power of this imaging technique to monitor and quantify the structural changes of corroded metal artifacts. This research employed a series of pre- and post-treatment tomography acquisitions (time-series) complemented by advanced image registration methods. Based on mutual information (MI) metrics, we performed rigid body and affine image registrations to meticulously account for sample repositioning challenges and variations in imaging parameters. Using non-affine local registration results, in a second step, we detected localized expansion and shrinkage in the samples attributable to imposed environmental changes. Specifically, we observed local shrinkage on the nail that was dried, mostly in their Transformed Medium (TM), the outer layer where corrosion products are cementing soil and sand particles. Conversely, the sample subjected to high relative humidity environment exhibited localized expansion, with varying degrees of change across different regions. This work highlights the efficacy of our registration techniques in accommodating manual removal or loss of extraneous material (loosely adhering soil and TM layers around the nails) post-initial tomography, successfully capturing local structural changes with high precision. Using differential analysis on the accurately registered samples we could also detect and volumetrically quantify the variation in moisture and detect changes in active corrosion sites (ACS) in the sample. These preliminary experiments allowed us to advance and optimize the application of a neutron tomography and image registration workflow for future, more advanced experiments such as humidity fluctuations, corrosion removal through micro-blasting, dechlorination and other stabilization treatments.

Multivariable models of outcomes with [177Lu]Lu-PSMA-617: analysis of the phase 3 VISION trial

[177Lu]Lu-PSMA-617 (177Lu-PSMA-617) prolonged life in patients with metastatic castration-resistant prostate cancer (mCRPC) in VISION (NCT03511664). However, distinguishing between patients likely and unlikely to respond remains a clinical challenge. We present the first multivariable models of outcomes with 177Lu-PSMA-617 built using data from VISION, a large prospective phase 3 clinical trial powered for overall survival. Adults with progressive post androgen receptor pathway inhibitor and taxane prostate-specific membrane antigen (PSMA)-positive mCRPC received 177Lu-PSMA-617 plus protocol-permitted standard of care (SoC) or SoC alone. In this post hoc analysis, multivariable Cox proportional hazards models of overall survival (OS) and radiographic progression-free survival (rPFS), and a logistic regression model of prostate-specific antigen response (≥50% decline; PSA50) were constructed and evaluated using C-index or receiver operating characteristic (ROC) analyses with bootstrapping validation. Nomograms were constructed for visualisation. Patients were randomised between June 2018 and October 2019. Data from all 551 patients in the 177Lu-PSMA-617 arm were analysed in multivariable modelling. The OS nomogram (C-index, 0.73; 95% confidence interval [CI], 0.70-0.76) included whole-body maximum standardised uptake value (SUVmax), time since diagnosis, opioid analgesic use, aspartate aminotransferase, haemoglobin, lymphocyte count, presence of PSMA-positive lesions in lymph nodes, lactate dehydrogenase (LDH), alkaline phosphatase (ALP), and neutrophil count. The rPFS nomogram (C-index, 0.68; 0.65-0.72) included SUVmax, time since diagnosis, opioid analgesic use, lymphocyte count, presence of liver metastases by computed tomography, LDH, and ALP. The PSA50 nomogram (area under ROC curve, 0.72; 95% CI, 0.68-0.77) included SUVmax, lymphocyte count and ALP. Performances of the OS and rPFS models were maintained when they were reconstructed excluding SUVmax. These models of outcomes with 177Lu-PSMA-617 are the first built using prospective phase 3 data. They show that a combination of pretreatment laboratory, clinical, and imaging parameters, reflecting both patient and tumour status, influences outcomes. These models are important for aiding treatment selection, patient management, and clinical trial design. Novartis.

Enhancing IVF Outcomes with Artificial Intelligence: Current Advances and Future Possibilities

The integration of Artificial Intelligence (AI) into In Vitro Fertilization (IVF) practices has marked a revolutionary shift in reproductive medicine, offering enhanced precision, efficiency, and personalized treatment plans. The rapid advancement of Artificial Intelligence (AI) has led to significant innovations in the field of reproductive medicine, particularly in In Vitro Fertilization (IVF). Traditional IVF procedures, while effective, often face challenges such as variable success rates, high costs, and the emotional burden on patients due to multiple treatment cycles. AI offers a promising solution to these issues by enhancing accuracy, personalization, and efficiency throughout the IVF process. AI algorithms have shown remarkable capabilities in diagnosing infertility by analyzing complex datasets from hormone profiles, genetic testing, and medical imaging, enabling early identification of conditions like polycystic ovary syndrome (PCOS) and endometriosis. Moreover, one of the most promising applications of AI in IVF is embryo grading. However, AI systems have been developed to objectively evaluate embryos based on time-lapse imaging, morphology, and other parameters, improving the selection process. Additionally, AI has been instrumental in optimizing ovarian stimulation protocols by analyzing patient data to determine the appropriate medication dosage, minimizing the risk of ovarian hyperstimulation syndrome (OHSS). This review discusses the current state of AI integration in fertility treatments, successful case studies, and ongoing research to develop more sophisticated AI models. Overall, AI holds immense promise in making IVF more accessible, affordable, and successful for patients worldwide, ushering in a new era of precision medicine in reproductive health.

Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features

BackgroundAccurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5–10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules.MethodsBlood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules.ResultsOur results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5–10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones.ConclusionsOur study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making.Trial registration: This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic.clinicaltrials.gov/ct2/show/NCT05432128.

Imaging predictors of adverse prognosis in fabry disease cardiomyopathy: a systematic review and meta-analysis

Abstract Background Cardiac involvement represents the main cause of death in patients with Fabry disease (FD). Echocardiography and Cardiac Magnetic Resonance (CMR) have an established diagnostic role, but their prognostic value remains unresolved. This systematic review and meta-analysis sought to assess the prognostic implications of imaging parameters in FD. Methods PubMed, ClinicalTrials.gov, Embase and Cochrane Library databases were searched for studies published from inception through to October 1, 2023. Full-length original papers including FD patients undergoing baseline imaging assessment and clinical follow-up were selected. Pre-defined study outcomes were a cardiovascular endpoint and a composite clinical endpoint. A meta-analysis of the studies investigating the effect of single imaging parameters on a pre-specified cardiovascular endpoint and a separate analysis on a pre-specified composite clinical endpoint were performed to obtain the pooled estimates for the association between the imaging parameters and the study outcome. The study protocol was registered in PROSPERO. Results Thirteen studies including 1,399 FD patients (44.8% males) were selected. At pooled analysis, late gadolinium enhancement (HR 4.39; 95% CI 2.5-7.71), left atrium volume indexed (HR 1.02 per ml/m2; 95% CI 1.01-1.04), E/e’ (HR 1.14 per unit increase; 95% CI 1.08-1.21), left ventricular (LV) mass indexed (HR 1.014 per mg/m2; 95% CI 1.006-1.023), maximum LV wall thickness (HR 1.19 per mm, 95% CI 1.04-1.36), LV-global longitudinal strain (HR 1.2 per unit increase; 95% CI 1.2-1.27), were significantly associated with the cardiovascular endpoint, whereas T1-mapping (p=0.115) and LV-ejection fraction (p=0.305) were not. T1-mapping was associated with the composite endpoint (HR 0.986 per msec increase; 95% CI 0.976-0.997). Meta-regression analysis did not show any significant interaction between each of the potential effect modifiers. Conclusion This meta-analysis demonstrates a robust prognostic value regarding several imaging parameters in FD, specifically indexes of LV mass and wall thickness, diastolic dysfunction, LV-GLS, and LGE presence. These findings support multimodality cardiovascular imaging, including CMR, in FD disease patients to predict long-term prognosis and call for longitudinal multicentre studies to develop multi-modality-imaging derived risk-score algorithms.

Long-term outcomes after transcatheter aortic valve implantation: derivation and validation of the long-TAVI prediction model

Abstract Background Identification of high-risk individuals after transcatheter aortic valve implantation (TAVI) is essential to personalized care. Established risk models require a plethora of variables, fail to account for biomarker dynamics and imaging parameters, mostly focus on the short term, and offer limited predictive performance. Purpose We aimed to develop an easily applicable prediction model for long-term mortality or heart failure (HF) admission following TAVI. Methods Patients undergoing TAVI at a high-volume university centre in the United Kingdom (n = 990) were recruited into a prospective cohort study. Enrolled patients were divided into a development cohort (07/2007 – 12/2016) and a validation cohort (01/2017 – 11/2018) and followed for up to 15.1 (median 5.3, interquartile range [IQR] 2.8 – 7.4) years. Patient characteristics along with pre- and post-procedural imaging results and brain natriuretic peptide (BNP) levels before the procedure and at 3 months were recorded. A clinical risk score for long-term mortality and HF admission (i.e., Long-TAVI risk score) was derived using Cox regression and evaluated using time-dependent area under the receiver operating characteristics curve (AUC). Results Age at presentation was 79.8 (standard deviation [SD] 8.9) years, 961 (99.0%) patients had signs of heart failure, left ventricular ejection fraction was 52 (SD 11.2), and pre-TAVI BNP levels were 298.0 (interquartile range [IQR] 159.0 – 571.5) pg/mL. High BNP levels at presentation (higher vs. lower than 400 pg/mL: HR 1.50, 95% CI 1.17 – 1.92, P &amp;lt; 0.001) and dynamic changes in BNP with the first 3 months (low/high vs. low/low: 2.61, 95% CI 1.17 – 5.86, P = 0.020; high/high vs. low/low: 2.41, 95% CI 1.26 – 4.64, P = 0.008) were independent predictors mortality or HF admission. The simple 8-item Long-TAVI score accounts for periprocedural BNP dynamics and shows good discrimination (AUC 0.76, 95% CI 0.72 – 0.79) and calibration in the development cohort performing similarly well in the external validation cohort (AUC 0.70, 95% CI 0.63 – 0.77). Long-TAVI significantly outperformed the logistic EURO score (AUC 0.56, 95% CI 0.52 – 0.59, P &amp;lt; 0.001). Conclusion Both pre-TAVI BNP levels and periprocedural BNP trajectories are independently associated with long-term outcomes after TAVI. Accounting for clinical characteristics, imaging parameters, and circulating BNP levels, the newly developed Long-TAVI risk score achieves high performance and provides the first prediction model for long-term mortality or HF admission. Long-TAVI represents a novel tool for personalized risk assessment in patients undergoing TAVI.Correlation at baseline A) and FUP B)Score performance A) and risk groups B)

Effects of cardiac resynchronization therapy and physiologicaL pacing on left atrioventricular coupling index: the REPHYL-LACI study

Abstract Introduction Cardiac dyssynchrony and right ventricular pacing are associated with left atrial (LA) remodeling and atrioventricular uncoupling. Cardiac resynchronization therapy -CRT and conduction system pacing –CSP are important therapeutic options. Left atrioventricular coupling index (LACI) is a novel imaging parameter useful to describe the relationship between LA and left ventricle (LV). It could be measured as the ratio between LA end-diastolic volume (EDV) and the LV EDV or, alternatively, as the LA volume indexed for body surface area (LAVi) divided for a’ measured by tissue Doppler imaging (TDI) at medial mitral annulus level. Higher values indicate greater impairment of atrioventricular coupling. However, LACI has not been investigated in patients treated with CRT or CSP, and there is no comparison data about the two different measurement methodologies with echocardiography. Purpose To study the effects of CRT/CSP on LACI. Methods We retrospectively collected clinical, ECG and echocardiographic data of patients treated with standard CRT or CSP. Changes between baseline and follow-up (i.e. before and after CRT/CSP) were tested for significance. Baseline features were compared with these of a control group of patients without established major cardiovascular diseases. Results We enrolled fifty-six patients (median age at first echo 78[72.5-82]years; males 70%) and 56 controls (median age 77[72.5-83]years, p=0.974; males 57%, p= 0.170). Main clinical/instrumental data are shown in Table 1 and 2. In patients treated with CRT/CSP baseline LACIv (LAVid/LV EDV) was significantly lower than in controls (31[21-44]%vs34[29-48]%, p=0.040), despite higher LAVi and LV volumes in the first group (p&amp;lt;0.0001 for both). Conversely, LACId (LAVi/TDI-a’) was significantly higher in the CRT/CSP (4.48[2.81-8.28]vs2.89[2.36-3.67]mlxsecond/cm-1, p&amp;lt;0.0001). After a median follow-up of 515[282-1326]days, patients treated with CRT/CSP showed a significant increase of both LACIv (40[29-50]%, p=0.004) and LACId (4.95[3.77-9.89], p&amp;lt;0.0001). This worsening of atrioventricular coupling was accompanied by a trending LA dilatation with significant reduction of its reservoir function compared to baseline (expansion index 0.58[0.38-1.30]vs0.43[0.29-0.58], p=0.0002), despite a tendency toward stability or improvement of LV size and systolic/diastolic parameters. Conclusions In patients treated with CRT/CSP there is a worsening of LACI over time. We can speculate that the detrimental effects of artificial pacing might not be adequately counterbalanced by CRT/CSP, or that LV and LA pathology follow alternative trajectories with different responses to treatments. Moreover, the measurement of LACId should be probably avoided in patients with conduction disorders or paced. Further larger case-control studies are needed to clarify the usefulness of LACI as a valid parameter to assess atrioventricular coupling and its changes over time in patients treated with CRT/CSP.

Detection and risk stratification of cardiac amyloidosis patients by integration of imaging and non-imaging data using a machine learning approach

Abstract Background With the advent of amyloid-targeting therapies, early and reliable diagnosis as well as precise risk estimation of cardiac amyloidosis (CA) have become of substantial importance. While the current diagnostic approach relies on difficult-to-standardize, visual interpretation of 99mTc-scintigraphy, risk assessment is largely based on a combination of blood and imaging parameters. Purpose Aim of this study is to assess the possibility of machine learning-based integration of scintigraphy, echocardiography, and non-imaging parameters such as blood parameters and comorbidities for the detection and risk stratification of patients with CA. Methods This study included all patients who underwent 99mTc-scintigraphy at a university affiliated tertiary care centre between 2010 and 2023. Two machine learning models were developed, and the relative predictive importance of their parameters were assessed (Figure 1): First, a model to detect patients with CA-suggestive uptake (Perugini grade 2/3) on 99mTc-scintigraphy scans; Second, a model to assess the risk of patients with CA-suggestive uptake for future heart failure hospitalization (HFH). A total of 58 features were extracted from electronic health records including blood, echocardiographic, demographic parameters and comorbidities. Scintigraphy imaging features were extracted from the raw imaging data using a deep learning approach. For the time to HFH prediction, a random survival forest machine learning model was employed. Results Overall, 12 380 consecutively enrolled patients were included, 279 (2.3%) of which were affected by CA-suggestive uptake. The machine learning model showed higher accuracy for the detection of CA (AUC 0.96 [95% CI 0.95-0.97], sensitivity 0.78 [95% CI 0.77-0.80] and specificity 0.99 [95% CI 0.99-0.99]. In the outcome analysis, the machine model showed good accuracy in predicting future HFH (C-index 0.71 [95% CI 0.68-0.75]). Parameter importance for the time to event analysis revealed right ventricular diameter, previous diagnosis of chronic heart failure and creatine kinase as the three most important predictors (Figure 2). Conclusions Detection of patients with cardiac amyloidosis and their risk estimation for heart failure can be aided using machine learning-based integration of imaging and non-imaging data. Our findings may guide novel approaches for assessing disease progression and treatment response.

Prognostic value of multiparametric cardiac magnetic resonance after revascularized STEMI

Abstract Despite early revascularization of STEMI through angioplasty, the prognosis following STEMI remains poor for a non-negligible percentage of patients (1). In this setting, multiple imaging parameters have demonstrated their prognostic value. The aim of the present study is to evaluate which of these parameters are the most potent predictors of events following revascularized STEMI. Methods A prospective observational study was conducted, including a cohort of revascularized STEMI patients and who underwent cardiac MRI during the first week. The protocol included cardiac functional analysis and with b-SSFP sequences for evaluation of volumes, mass, ejection fraction and also circumferential and radial strain with feature tracking. STIR sequences were used to assess edema and salvaged myocardium, IR-FGE sequences were employed for evaluation of necrosis size and microvascular obstruction. Intra-infarct hemorrhage assessment was performed using T2* mapping sequences as previosly described (2,3,4) . A semi-automatic analysis was conducted, considering as microvascular obstruction segments with hypointensity in the infarct core and as segments with hemorrhage those with a T2* less than 20 ms. The association of imaging parameters with cardiovascular events in the first year following STEMI was studied using ROC curves. Variables with significant association were analyzed using a multivariate logistic regression model. Results Ninety-four patients with a mean age of 63 (SD 13) years were included. Nine of them had cardiovascular events in the first year and two of them died. The relative extent of necrosis and microvascular obstruction, minimal radial strain, left ventricular ejection fraction (LVEF) and indexed left ventricular end- systolic volume (ILVESV) showed significant association with cardiovascular events after STEMI. Variables with significant association were entered into a multivariate predictive model, revealing that the extent of microvascular obstruction (OR: 1.9, p=0.02) and the left ventricular end-systolic volume (OR: 1.04, p&amp;lt;0.01) are the only independent predictors of cardiovascular events following STEMI. Conclusion Although multiple imaging parameters have shown prognostic value after STEMI, the ILVESV and the extent of microvascular obstruction are the only independent predictors of events following STEMI.Multiparametric cardiac MR in STEMIAREA UNDER ROC CURVE OF CMR PARAMETERS

Predicting Portal Pressure Gradient in Patients with Decompensated Cirrhosis: A Non-invasive Deep Learning Model.

A high portal pressure gradient (PPG) is associated with an increased risk of failure to control esophagogastric variceal hemorrhage and refractory ascites in patients with decompensated cirrhosis. However, direct measurement of PPG is invasive, limiting its routine use in clinical practice. Consequently, there is an urgent need for non-invasive techniques to assess PPG. To develop and validate a deep learning model that predicts PPG values for patients with decompensated cirrhosis and identifies those with high-risk portal hypertension (HRPH), who may benefit from early transjugular intrahepatic portosystemic shunt (TIPS) intervention. Data of 520 decompensated cirrhosis patients who underwent TIPS between June 2014 and December 2022 were retrospectively analyzed. Laboratory and imaging parameters were used to develop an artificial neural network model for predicting PPG, with feature selection via recursive feature elimination for comparison experiments. The best performing model was tested by external validation. After excluding 92 patients, 428 were included in the final analysis. A series of comparison experiments demonstrated that a three-parameter (3P) model, which includes the international normalized ratio, portal vein diameter, and white blood cell count, achieved the highest accuracy of 87.5%. In two distinct external datasets, the model attained accuracy rates of 85.40% and 90.80%, respectively. It also showed notable ability to distinguish HRPH with an AUROC of 0.842 in external validation. The developed 3P model could predict PPG values for decompensated cirrhosis patients and could effectively distinguish HRPH.

Changes in peak oxygen consumption on excercise and cardiomyopathy disease stage in Fabry disease

Abstract Background Fabry disease (FD) is an X-linked lysosomal storage disorder manifesting as deficiency in the enzyme α-Galactosidase A (1) resulting in multi-organ accumulation of globotriaosylceramide (Gb3) (2) This causes predominantly cardiac, renal and cerebrovascular manifestations. Shortness of breath and exercise intolerance are common and can be related to multiple underlying mechanisms. Cardiopulmonary exercise testing (CPEX) quantifies aerobic fitness, functional capacity, chronotropic and haemodynamic response to exercise, and attributes effort intolerance to different aetiologies, including impaired cardiac and respiratory function. Purpose We hypothesise that there is progressive effort intolerance that reflects continual cardiac Gb3 accumulation, becoming common during myocardial hypertrophy and disabling in the fibrosis and inflammation stage. The aims of this study were to ascertain frequency and severity of exercise intolerance in FD by investigating the relationship between peak maximal oxygen uptake (VO2 max) and cardiac disease stage, quantified based on a four-point scale, as proposed in published literature (3). Methods This was a retrospective review of adults with FD and exercise intolerance attending a specialist clinic who had undergone either CPEX or six-minute walk testing (6MWT) in those less mobile. Biochemical, cardiac magnetic resonance (CMR) imaging and transthoracic echocardiographic (TTE) data were also collected. Results N=54 patients underwent CPEX and N=35 patient underwent 6MWT. Estimated peak VO2 max was calculated using 6MWD in those undergoing 6MWT. Median age was 54 (IQR 40-62) years and 53% were male. Patients were equally distributed across disease stage (1-4). There were significant trends in age (increasing) and gender (male predominance) from stage 1-4. Age/sex-normalised peak VO2 max values were calculated as per published literature due to the confounding effect of age and male gender. Mean normalised VO2 max was 35.5ml/kg/min for the cohort, an underperformance of 12.3 ± 8.0 ml/kg/min. The magnitude of underperformance began from stage 1 (no cardiomyopathy) and increased with each stage (Figure 1). Impairments in renal (creatinine and albumin: creatinine ratio) and cardiac biomarkers (troponin and N-terminal pro brain natriuretic peptide) was associated with reduced VO2 max (Table 1). Increased indexed left ventricular mass on CMR and impairments in atrial volume and function on TTE were also associated with a reduction in VO2 max (Table 1). Conclusion This study is the first of its kind to demonstrate impairments in peak aerobic capacity in adults with FD and no cardiomyopathy (stage 1) which impairs further with cardiac disease stage. Future work should focus on how disease-modifying therapy can target this using VO2 as an endpoint and marker of response.Normalised peak VO2 max + disease stageVO2 max and blood / imaging parameters

The role of semi-quantitative parameters of thallium-201 myocardial perfusion imaging in predicting mortality rate among CKD population

Abstract Background Chronic kidney disease (CKD) is a major public health concern worldwide, with high morbidity and mortality rates. Myocardial perfusion imaging (MPI) is a non-invasive diagnostic tool that can detect early-stage cardiovascular disease in CKD patients. Purpose This study aimed to investigate the role of semi-quantitative parameters of Thallium-201 MPI in predicting mortality rates among CKD patients. Methods This retrospective study included 579 CKD patients who underwent Thallium-201 MPI between October 2005 and December 2019. Semi-quantitative parameters were analyzed using automation software, including transient ischemic dilation (TID), lung-to-heart ratio (LHR), total perfusion deficit (TPD), summed stress score (SSS), summed rest score (SRS), summed difference score (SDS), summed thickening percent (ST%), summed motion percent (SM%), stress end-diastolic volume (EDV), stress end-systolic volume (ESV), and stress left ventricular ejection fraction (LVEF). The primary endpoint was overall mortality. Results During a median follow-up of 52 months, 148 patients died. The older age group, lower hemoglobin levels, and lower estimated glomerular filtration rate (eGFR) were associated with higher mortality rates. The semi-quantitative parameters that predicted overall mortality included LHR above 0.4, elevated ST% , increased stress EDV and ESV, and reduced stress LVEF in a multivariable Cox regression model. Conclusion Thallium-201 MPI with semi-quantitative parameters can predict mortality rates in CKD patients without a definite diagnosis of coronary artery disease. The identified parameters, including LHR above 0.4, elevated ST levels, increased stress EDV and ESV, and reduced stress LVEF can assist in early detection the mortality risk in CKD patients.Characteristics of CKD PatientsMultivariable Analysis