Search Results (1,608)

Background/Objectives: Historically, echocardiographic imaging of the right heart has been challenging because its abnormal geometry is not conducive to reproducible anatomical and functional assessment. With the development of advanced echocardiographic techniques, it is now possible to complete an integrated assessment of the right heart that has fewer assumptions, resulting in increased accuracy and precision. Echocardiography continues to be the first-line imaging modality for diagnostic analysis and the management of acute and chronic right heart failure because of its portability, versatility, and affordability compared to cardiac computed tomography, magnetic resonance imaging, nuclear scintigraphy, and positron emission tomography. Virtually all echocardiographic parameters have been well-validated and have demonstrated prognostic significance. The goal of this narrative review of the echocardiographic parameters of the right heart chambers and hemodynamic alterations associated with right ventricular dysfunction is to present information that must be acquired during each examination to deliver a comprehensive assessment of the right heart and to discuss their clinical significance in right heart failure. Methods: Using a literature search in the PubMed database from 1985 to 2025 and the Cochrane database, which included but was not limited to terminology that are descriptive of right heart anatomy and function, disease states involving acute and chronic right heart failure and pulmonary hypertension, and the application of conventional and advanced echocardiographic modalities that strive to elucidate the pathophysiology of right heart failure, we reviewed randomized control trials, observational retrospective and prospective cohort studies, societal guidelines, and systematic review articles. Conclusions: In addition to the conventional 2-dimensional echocardiography and color, spectral, and tissue Doppler measurements, a contemporary echocardiographic assessment of a patient with suspected or proven right heart failure must include 3-dimensional echocardiographic-derived measurements, speckle-tracking echocardiography strain analysis, and hemodynamics parameters to not only characterize the right heart anatomy but to also determine the underlying pathophysiology of right heart failure. Complete and point-of-care echocardiography is available in virtually all clinical settings for routine care, but this imaging tool is particularly indispensable in the emergency department, intensive care units, and operating room, where it can provide an immediate assessment of right ventricular function and associated hemodynamic changes to assist with real-time management decisions. Full article

Background: Traumatic brain injury (TBI) remains a significant contributor to global mortality and long-term neurological disability. Accurate prognostic biomarkers are crucial for enhancing prognostic accuracy and guiding personalized clinical management. Objective: This review assesses the prognostic value of arterial spin labeling (ASL), a non-invasive MRI technique, in adult and pediatric TBI, with a focus on quantitative cerebral blood flow (CBF) and arterial transit time (ATT) measures. A comprehensive literature search was conducted across PubMed, Embase, Scopus, and IEEE databases, including observational studies and clinical trials that applied ASL techniques (pCASL, PASL, VSASL, multi-PLD) in TBI patients with functional or cognitive outcomes, with outcome assessments conducted at least 3 months post-injury. Results: ASL-derived CBF and ATT parameters demonstrate potential as prognostic indicators across both acute and chronic stages of TBI. Hypoperfusion patterns correlate with worse neurocognitive outcomes, while region-specific perfusion alterations are associated with affective symptoms. Multi-delay and velocity-selective ASL sequences enhance diagnostic sensitivity in TBI with heterogeneous perfusion dynamics. Compared to conventional perfusion imaging, ASL provides absolute quantification without contrast agents, making it suitable for repeated monitoring in vulnerable populations. ASL emerges as a promising prognostic biomarker for clinical use in TBI. Conclusion: Integrating ASL into multiparametric models may improve risk stratification and guide individualized therapeutic strategies. Full article

Background: Pancreatic cystic lesions (PCLs), including intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs), pose a diagnostic challenge due to their variable malignant potential. Current guidelines, such as Fukuoka and American Gastroenterological Association (AGA), have moderate predictive accuracy and may lead to overtreatment or missed malignancies. Artificial intelligence (AI), incorporating machine learning (ML) and deep learning (DL), offers the potential to improve risk stratification, diagnosis, and management of PCLs by integrating clinical, radiological, and molecular data. This is the first systematic review to evaluate the application, performance, and clinical utility of AI models in the diagnosis, classification, prognosis, and management of pancreatic cysts. Methods: A systematic review was conducted in accordance with PRISMA guidelines and registered on PROSPERO (CRD420251008593). Databases searched included PubMed, EMBASE, Scopus, and Cochrane Library up to March 2025. The inclusion criteria encompassed original studies employing AI, ML, or DL in human subjects with pancreatic cysts, evaluating diagnostic, classification, or prognostic outcomes. Data were extracted on the study design, imaging modality, model type, sample size, performance metrics (accuracy, sensitivity, specificity, and area under the curve (AUC)), and validation methods. Study quality and bias were assessed using the PROBAST and adherence to TRIPOD reporting guidelines. Results: From 847 records, 31 studies met the inclusion criteria. Most were retrospective observational (n = 27, 87%) and focused on preoperative diagnostic applications (n = 30, 97%), with only one addressing prognosis. Imaging modalities included Computed Tomography (CT) (48%), endoscopic ultrasound (EUS) (26%), and Magnetic Resonance Imaging (MRI) (9.7%). Neural networks, particularly convolutional neural networks (CNNs), were the most common AI models (n = 16), followed by logistic regression (n = 4) and support vector machines (n = 3). The median reported AUC across studies was 0.912, with 55% of models achieving AUC ≥ 0.80. The models outperformed clinicians or existing guidelines in 11 studies. IPMN stratification and subtype classification were common focuses, with CNN-based EUS models achieving accuracies of up to 99.6%. Only 10 studies (32%) performed external validation. The risk of bias was high in 93.5% of studies, and TRIPOD adherence averaged 48%. Conclusions: AI demonstrates strong potential in improving the diagnosis and risk stratification of pancreatic cysts, with several models outperforming current clinical guidelines and human readers. However, widespread clinical adoption is hindered by high risk of bias, lack of external validation, and limited interpretability of complex models. Future work should prioritise multicentre prospective studies, standardised model reporting, and development of interpretable, externally validated tools to support clinical integration. Full article

Varicocele is a common, potentially correctable condition associated with impaired male fertility. Despite being frequently encountered in clinical andrology, its pathophysiological mechanisms, diagnostic criteria, and therapeutic approaches remain areas of active investigation and debate. The authors conducted a comprehensive literature search, using the PubMed database, covering clinical studies, systematic reviews, meta-analyses, and current international guidelines from the past ten years. Emphasis was placed on studies investigating novel diagnostic modalities, therapeutic innovations, and prognostic markers. Emerging evidence supports the multifactorial pathophysiology of varicocele, involving oxidative stress, hypoxia, inflammatory pathways, and potential genetic predisposition. Biomarkers, including microRNAs, antisperm antibodies, and sperm DNA fragmentation, offer diagnostic and prognostic utility, though their routine clinical implementation requires further validation. Advances in imaging, such as shear wave elastography, may improve diagnostic accuracy. While microsurgical subinguinal varicocelectomy remains the gold standard, technological refinements and non-surgical alternatives are being explored. Indications for treatment have expanded to include selected cases of non-obstructive azoospermia, hypogonadism, and optimization for assisted reproduction, though high-level evidence is limited. Full article

Arrhythmogenic cardiomyopathies (ACMs) are a phenotypically and etiologically heterogeneous group of myocardial disorders characterized by fibrotic or fibro-fatty replacement of ventricular myocardium, electrical instability, and an elevated risk of sudden cardiac death. Initially identified as a right ventricular disease, ACMs are now recognized to include biventricular and left-dominant forms. Genetic causes account for a substantial proportion of cases and include desmosomal variants, non-desmosomal variants, and familial gene-elusive forms with no identifiable pathogenic mutation. Nongenetic etiologies, including post-inflammatory, autoimmune, and infiltrative mechanisms, may mimic the phenotype. In many patients, the disease remains idiopathic despite comprehensive evaluation. Cardiac magnetic resonance imaging has emerged as a key tool for identifying non-ischemic scar patterns and for distinguishing arrhythmogenic phenotypes from other cardiomyopathies. Emerging classifications propose the unifying concept of scarring cardiomyopathies based on shared structural substrates, although global consensus is evolving. Risk stratification remains challenging, particularly in patients without overt systolic dysfunction or identifiable genetic markers. Advances in tissue phenotyping, multi-omics, and artificial intelligence hold promise for improved prognostic assessment and individualized therapy. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

Background: The development of brain metastases (BM) is a relatively uncommon but significantly adverse event in the spread of colorectal cancer (CRC). Although management of CRC BM has improved with advances in imaging and systemic therapies, clinical outcomes remain poor. Methods: This retrospective cohort study used the U.S. National Cancer Database to evaluate survival outcomes, treatment patterns, and prognostic factors in CRC patients diagnosed with BM between 2010 and 2020. Patients with isolated brain-only metastases formed the primary analytic cohort, while those with additional extracranial metastases were included for descriptive comparison. Multivariable Cox proportional hazards and logistic regression models were used to assess factors associated with of survival. Proportional hazards assumptions were tested using Schoenfeld residuals. Accelerated failure time models were also employed. Results: From a cohort of 1,040,877 individuals with CRC, 795 had metastatic disease present along with relevant data, of which 296 had isolated BM. Median overall survival (mOS) in BM-only metastatic disease group was 7.82 months (95% CI: 5.82–9.66). The longest survival was observed among patients treated with stereotactic radiosurgery combined with systemic therapy (SRS+Sys), with a median OS of 23.26 months (95% CI: 17.51–41.95) and a 3-year survival rate of 35.8%. In adjusted Cox models, SRS, systemic therapy, and definitive surgery of the primary site were each independently associated with reduced hazard of death. Rectal cancer patients had longer survival than those with colon primaries (mOS: 10.35 vs. 6.08 months). Age, comorbidity burden, and insurance status were not associated with survival in adjusted analyses. Conclusions: SRS+Sys was associated with longer survival compared to other treatment strategies. However, treatment selection is highly dependent on individual clinical factors such as performance status, comorbidities, and disease extent; therefore, these findings must be interpreted with caution Future prospective studies incorporating molecular and biomarker data are warranted to better guide care in this rare and high-risk group. Full article

Purpose: Predicting colorectal cancer liver metastasis (CRLM) is essential for prognostic assessment. This study aims to develop and validate an interpretable multimodal machine learning framework based on multiparametric MRI for predicting CRLM, and to enhance the clinical interpretability of the model through SHapley Additive exPlanations (SHAP) analysis and deep learning visualization. Methods: This multicenter retrospective study included 463 patients with pathologically confirmed colorectal cancer from two institutions, divided into training (n = 256), internal testing (n = 111), and external validation (n = 96) sets. Radiomics features were extracted from manually segmented regions on axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). Deep learning features were obtained from a pretrained ResNet101 network using the same MRI inputs. A least absolute shrinkage and selection operator (LASSO) logistic regression classifier was developed for clinical, radiomics, deep learning, and combined models. Model performance was evaluated by AUC, sensitivity, specificity, and F1-score. SHAP was used to assess feature contributions, and Grad-CAM was applied to visualize deep feature attention. Results: The combined model integrating features across the three modalities achieved the highest performance across all datasets, with AUCs of 0.889 (training), 0.838 (internal test), and 0.822 (external validation), outperforming single-modality models. Decision curve analysis (DCA) revealed enhanced clinical net benefit from the integrated model, while calibration curves confirmed its good predictive consistency. SHAP analysis revealed that radiomic features related to T2WI texture (e.g., LargeDependenceLowGrayLevelEmphasis) and clinical biomarkers (e.g., CA19-9) were among the most predictive for CRLM. Grad-CAM visualizations confirmed that the deep learning model focused on tumor regions consistent with radiological interpretation. Conclusions: This study presents a robust and interpretable multiparametric MRI-based model for noninvasively predicting liver metastasis in colorectal cancer patients. By integrating handcrafted radiomics and deep learning features, and enhancing transparency through SHAP and Grad-CAM, the model provides both high predictive performance and clinically meaningful explanations. These findings highlight its potential value as a decision-support tool for individualized risk assessment and treatment planning in the management of colorectal cancer. Full article

Objectives: This multicenter study aimed to clarify the recurrence patterns; treatment outcomes; and prognostic factors of thymic carcinoma, a rare cancer. Methods: We analyzed 101 patients with thymic carcinoma who underwent multidisciplinary treatment, including radiotherapy. The median age was 62 years, with 27 patients in stage I–II; 44 in stage III; and 30 in stage IV by the TNM classification. Seventy-two patients underwent surgery with radiotherapy; and 29 patients underwent definitive radiotherapy. Image-guided radiotherapy (IGRT) and elective nodal irradiation (ENI) were used for 35 and 23 patients, respectively. Local recurrence-free survival (LRFS); progression-free survival (PFS); and overall survival (OS) were calculated, and univariate and multivariate analyses were performed. Results: With a median follow-up of 68 months, we observed 17 local recurrences; 27 regional recurrences; and 35 distant metastases. The 5-year LRFS; PFS; and OS were 82%, 41%, and 76%, respectively. Multivariate analysis revealed that stage was the only factor associated with LRFS; PFS; and OS (p = 0.040; p < 0.0001; and p = 0.048, respectively), while treatment modality was associated with only LRFS (p = 0.015). IGRT and ENI were also associated with LRFS (p = 0.002 and 0.013, respectively). PFS and OS of stage IV patients were comparable between the surgery with radiotherapy and definitive radiotherapy groups (p = 0.99 and 0.98, respectively). Conclusions: Our results suggest the importance of stage-specific treatment strategies rather than resectability, especially for stage IV patients. These results should be validated in a prospective study. Our results also suggest that radiotherapy methods influence recurrence Full article

In this review, we explore the complexities of objectively assessing the response to immunotherapy in mycosis fungoides (MF), a prevalent form of cutaneous T-cell lymphoma. The core challenge lies in distinguishing between reactive and malignant lymphocytes amidst treatment, particularly given the absence of uniform pathological biomarkers for MF. We highlight the vital role of emerging histological technologies, such as multispectral imaging and spatial transcriptomics, in offering a more profound insight into the tumor microenvironment (TME) and its dynamic response to immunomodulatory therapies. Drawing on parallels with melanoma—another immunogenic skin cancer—our review suggests that methodologies and insights from melanoma could be instrumental in refining the approach to MF. We specifically focus on the prognostic implications of various TME cell types, including CD8+ tumor-infiltrating lymphocytes, natural killer (NK) cells, and histiocytes, in predicting therapy responses. The review culminates in a discussion about adapting and evolving treatment response quantification strategies from melanoma research to the distinct context of MF, advocating for the implementation of novel techniques like high-throughput T-cell receptor gene rearrangement analysis. This exploration underscores the urgent need for continued innovation and standardization in evaluating responses to immunotherapies in MF, a field rapidly evolving with new therapeutic strategies. Full article

Primary aldosteronism (PA), the most common cause of secondary hypertension, is increasingly recognized as an independent driver of adverse cardiac remodeling, mediated through mechanisms beyond elevated blood pressure alone. Chronic aldosterone excess leads to myocardial fibrosis, left ventricular hypertrophy, and diastolic dysfunction via mineralocorticoid receptor activation, oxidative stress, inflammation, and extracellular matrix dysregulation. These changes culminate in a distinct cardiomyopathy phenotype, often underrecognized in early stages. Multimodality cardiac imaging, led primarily by conventional and speckle-tracking echocardiography, and complemented by exploratory cardiac magnetic resonance (CMR) techniques such as T1 mapping and late gadolinium enhancement, enables non-invasive assessment of structural, functional, and tissue-level changes in aldosterone-mediated myocardial damage. While numerous studies have established the diagnostic and prognostic relevance of imaging in PA, several gaps remain. Specifically, the relative sensitivity of different modalities in detecting subclinical myocardial changes, the long-term prognostic significance of imaging biomarkers, and the differential impact of adrenalectomy versus medical therapy on cardiac reverse remodeling require further clarification. Moreover, the lack of standardized imaging-based criteria for defining and monitoring PA-related cardiomyopathy hinders widespread clinical implementation. This narrative review aims to synthesize current knowledge on the pathophysiological mechanisms of aldosterone-induced cardiac remodeling, delineate the strengths and limitations of existing imaging modalities, and critically evaluate the comparative effects of surgical and pharmacologic interventions. Emphasis is placed on early detection strategies, identification of imaging biomarkers with prognostic utility, and integration of multimodal imaging into clinical decision-making pathways. By outlining current evidence and highlighting key unmet needs, this review provides a framework for future research aimed at advancing personalized care and improving cardiovascular outcomes in patients with PA. Full article

Background/Objectives: Pediatric allogeneic hematopoietic stem cell transplantation (allo-HSCT) is complicated by iron overload and hypomagnesemia, both contributing to immune dysfunction and post-transplant morbidity. The combined impact of these metabolic disturbances on pediatric allo-HSCT outcomes remains unexplored. This study aims to determine whether hypomagnesemia can serve as a prognostic biomarker for delayed immune reconstitution and explores its interplay with iron overload in predicting post-transplant complications and survival outcomes. Methods: A retrospective analysis was conducted on 163 pediatric allo-HSCT recipients. Serum magnesium levels were measured at defined intervals post-transplant, and outcomes were correlated with CD4+ T cell recovery, time to engraftment, incidence of graft-versus-host disease (GVHD), and survival within 12 months. Iron status, including siderosis severity, was evaluated using imaging and laboratory parameters obtained from clinical records. Results: Patients who died within 12 months post-transplant exhibited significantly lower magnesium levels. Hypomagnesemia was associated with delayed CD4+ T cell recovery, prolonged engraftment, and an increased risk of acute GVHD. A strong inverse correlation was observed between magnesium levels and the severity of siderosis. Iron overload appeared to exacerbate magnesium deficiency. Additionally, the coexistence of hypomagnesemia and siderosis significantly increased the risk of immune dysfunction and early mortality. No significant association was found with chronic GVHD. Conclusions: Hypomagnesemia is a significant, early predictor of poor outcomes in pediatric allo-HSCT, particularly in the context of iron overload, underscoring the need for early intervention, including iron chelation and MRI, to improve outcomes. Full article

Background: Hypometabolism of the thalamus has been reported in epilepsy patients. This study aimed to investigate the prognostic value of thalamic metabolic activity in children with epilepsy. Methods: A total of 200 children with epilepsy and 237 children without epilepsy (sex- and age-matched control group) underwent 18F-FDG PET/CT in this study. Localization of the interictal hypometabolic epileptic focus was performed visually. Bilateral thalamic metabolic activity was evaluated qualitatively (thalamic FDG uptake in relation to the cerebral cortex) and semi-quantitatively (SUV max, normalized SUV (ratio to ipsilateral cerebellum), and absolute asymmetric index (AAI). Results: A total of 133 patients (66.5%) with epilepsy showed cerebral cortical hypometabolism in the interictal 18F-FDG PET study; there were 76 patients on the right side, 55 patients on the left side, and two patients on both sides. Of these 133 patients, 45 also had visually observed asymmetric hypometabolism in the thalamus. Semi-quantitatively, asymmetry was more prominent in epileptic patients. AAI was a more sensitive variable than other variables. Average AAIs were 3.89% and 7.36% in the control and epilepsy patients, respectively. Metabolic activity in the thalami was significantly reduced in epileptic patients compared to the control group. Associated hypometabolism of the ipsilateral thalamus was observed in 66.5% of epileptic patients with a focal cortical defect semi-quantitatively. Overall, 61 out of 200 patients showed thalamus hypometabolism. Some 51 out of 61 patients (83.6%) with thalamus hypometabolism showed refractory disease; however, the refractory disease was noted in 90 out of 139 (64.7%) patients without thalamus hypometabolism. Brain surgery was performed in 86 epileptic patients (43%). Some 35 out of 86 patients had thalamus hypometabolism. Recurrence of epilepsy was observed more in patients with thalamus hypometabolism (48% vs. 25%), with p ≤ 0.01. Conclusion: This study suggests that patients with thalamus metabolic abnormalities may be more medically resistant to therapy and less responsive to surgical treatments. Therefore, the thalamus metabolic abnormality could be used as a prognostic sign in pediatric epilepsy. Recent studies have also suggested that incorporating thalamic metabolic data into clinical workflows may improve the stratification of treatment-resistant epilepsy in children. Full article

Due to their clinical heterogeneity, nonspecific symptoms, and the limitations of existing biomarkers and imaging modalities, metabolic brain diseases (MBDs), such as mitochondrial encephalopathies, lysosomal storage disorders, and glucose metabolism syndromes, pose significant diagnostic challenges. This review examines the growing potential of cell-free DNA (cfDNA) derived from cerebrospinal fluid (CSF) epigenetic profiling as a dynamic, cell-type-specific, minimally invasive biomarker approach for MBD diagnosis and monitoring. We review important technological platforms and their use in identifying CNS-specific DNA methylation patterns indicative of neuronal injury, neuroinflammation, and metabolic reprogramming, including cfMeDIP-seq, enzymatic methyl sequencing (EM-seq), and targeted bisulfite sequencing. By synthesizing current findings across disorders such as MELAS, Niemann–Pick disease, Gaucher disease, GLUT1 deficiency syndrome, and diabetes-associated cognitive decline, we highlight the superior diagnostic and prognostic resolution offered by CSF cfDNA methylation signatures relative to conventional CSF markers or neuroimaging. We also address technical limitations, interpretive challenges, and translational barriers to clinical implementation. Ultimately, this review explores CSF cfDNA epigenetic analysis as a liquid biopsy modality. The central objective is to assess whether epigenetic profiling of CSF-derived cfDNA can serve as a reliable and clinically actionable biomarker for improving the diagnosis and longitudinal monitoring of metabolic brain diseases. Full article

The Vesical Imaging Reporting and Data System (VI-RADS) is used to detect muscle-invasive bladder cancer, with emerging prognostic implications. Integrating imaging parameters with molecular biomarkers may improve risk stratification in bladder cancer. This study evaluated whether combining VI-RADS scores with serum cytokeratin fragment 19 (CYFRA 21-1) levels—a clinically relevant biomarker for bladder cancer—could improve overall survival (OS) prediction. We retrospectively analyzed 134 patients who underwent transurethral resection of bladder tumors, magnetic resonance imaging, and postoperative serum CYFRA 21-1 measurements. In total, 15 cancer-specific deaths were observed during follow-up. Receiver operating characteristic curve analysis identified optimal prognostic cut-off values: VI-RADS score ≥ 4 and CYFRA 21-1 level ≥ 1.8 ng/mL. The 1-, 2-, and 3-year OS in patients with both high VI-RADS scores and CYFRA 21-1 levels were 42.9%, 16.7%, and 8.3%, respectively, significantly lower than those in other groups (p < 0.001, 0.002, and 0.003, respectively). Multivariate Cox proportional hazards analysis demonstrated that such patients had the poorest OS (hazard ratio: 7.51; p = 0.002). This suggests that combining VI-RADS and CYFRA 21-1 improves prognostic accuracy in bladder cancer, demonstrating potential clinical utility by informing individualized treatment strategies; however, limitations include the retrospective study design and absence of external validation. Full article