Non-invasive Imaging Technique Research Articles

Identification of novel targets for ankylosing spondylitis through integrated transcriptome-wide association analysis and Mendelian randomization: Medical thermography analysis

Ankylosing spondylitis (AS) is a chronic inflammatory disease that mainly affects the spine and joints and seriously affects the quality of life of patients. Medical thermal imaging, as a non-invasive and real-time imaging technique, can be used to evaluate inflammation and blood flow changes. This study aims to identify new targets for ankylosing spondylitis by integrating transcriptome broad association analysis and Mendelian randomization, combined with medical thermal imaging technology, and provide basis for its early diagnosis and treatment. The study performed transcriptome extensive association analysis to identify genes associated with ankylosing spondylitis. The causal relationship between these genes and disease risk was then analyzed using Mendelian randomization. The researchers then conducted a detailed assessment of all participants using advanced medical thermal imaging techniques designed to detect and identify signs of local and systemic inflammation, revealing differences in thermal imaging characteristics between patients with ankylosing spondylitis and healthy people. Transcriptome analysis identified multiple candidate genes that showed significant associations in the onset of AS. After Mendelian randomization, it was confirmed that several of these genes have causal relationship.

Clinical predictors and noninvasive imaging in Fontan-associated liver disease: A systematic review and meta-analysis

Background: Despite the development of several imaging modalities for diagnosing Fontan-associated liver disease (FALD), there is no optimal protocol for the follow-up of FALD. We conducted a systematic review and meta-analysis to identify factors related to liver fibrosis using biopsy reports and to identify alternative noninvasive modalities that could better reflect liver histological changes in FALD. Methods: A systematic review and meta-analysis were conducted following the PRISMA guidelines Table S2. We searched Embase, PubMed, and Cochrane databases for studies on FALD, focusing on those assessing clinical factors associated with liver fibrosis severity through liver biopsy and noninvasive imaging techniques. Results: A total of 42 studies were identified, of which 12 conducted meta-analyses and subgroup analyses of the severity of liver fibrosis using liver biopsies. Liver biopsy results showed a weak positive correlation between Fontan duration and fibrosis severity (R = 0.36). Subgroup analyses revealed significant differences in hemodynamic parameters, such as Fontan pressure, between patients with mild and severe fibrosis. Platelet count, aspartate aminotransferase to platelet ratio index, and fibrosis-4 index were significantly associated with fibrosis severity, with severe fibrosis showing lower platelet counts and higher aspartate aminotransferase to platelet ratio index and fibrosis-4 index levels. Noninvasive imaging modalities, particularly magnetic resonance elastography and shear wave elastography, demonstrated strong correlations with biopsy-confirmed fibrosis severity. Conclusions: This study identifies key clinical factors, and noninvasive modalities accurately reflect liver fibrosis severity in patients with FALD. Clinical factors such as platelet count, aspartate aminotransferase to platelet ratio index, and fibrosis-4 index may aid in identifying patients at risk for severe fibrosis. In addition, magnetic resonance elastography and shear wave elastography are promising tools for noninvasive assessment in our study. Further research is needed to refine these diagnostic approaches and improve patient management.

The impact of sleep disorders on glymphatic function in Parkinson’s disease using diffusion-tensor MRI

Rationale and ObjectivesTo investigate the impact of sleep disorders on glymphatic system in Parkinson’s disease (PD) using a non-invasive imaging technique called diffusion tensor image analysis along perivascular space (DTI-ALPS). Materials and MethodsA total of 114 PD patients and 54 healthy controls (HCs) underwent sleep questionnaires and MRI examinations as part of data collection from the Parkinson’s Progression Markers Initiative cohort. An automated pipeline was proposed for ALPS calculation to reduce biases from manually region delineation. ALPS indices were compared between PD and HCs, as well as between PD with and without sleep disorders. Correlation was assessed between ALPS index and clinical characteristics. Furthermore, a 2-year follow-up analysis was performed to explore longitudinal impact of sleep disorders on glymphatic function. ResultsPD patients showed significantly decreased ALPS indices compared with HCs (P=0.038). PD patients with sleep disorders showed slightly but not significantly decreased ALPS index compared with those with normal sleep (P=0.058). However, PD patients who transitioned from normal sleep to sleep disorders showed significantly decreased ALPS index at follow-up compared to baseline (P=0.047). In contrast, patients who maintained normal sleep showed no significant difference in ALPS index between follow-up and baseline (P=0.934). In addition, PD patients who transitioned from normal sleep to sleep disorders showed significantly increased ΔMDS-UPDRS Part I score (P=0.004), ΔMDS-UPDRS total score (P=0.040) and ΔSCOPA-AUT (P=0.048) compared with PD who remained normal sleep. ConclusionSleep disorders accelerate dysfunction of glymphatic system as indicated by ALPS index in PD patients, which may be associated with symptom progression in follow-up analysis. Therefore, more attention should be devoted to prevent sleep disorders in early PD.

Hepato Web App for Classification and Segmentation of Liver Lesions in CT Scans Using EFF Net

The liver is a large organ situated in the upper right section of the abdomen, located beneath the diaphragm and above the stomach. Liver cancer is a type of cancer that originates in the liver, resulting from uncontrolled cell growth. Computed tomography (CT scan or CAT scan) is a non-invasive imaging technique that integrates x-rays with computer technology. CT scans are essential for diagnosing liver cancer in patients. A cascaded model of convolutional neural networks is employed to segment the liver, while an efficient net is used to detect liver lesions from CT scans. This method achieves an outstanding accuracy of 0.998 for both liver segmentation and liver lesion detection

Dermoscopy, Line-Field Confocal Optical Coherence Tomography, Reflectance Confocal Microscopy, and Ultra-High-Frequency Ultrasound: Clues for the Diagnosis of Hidrocystomas

Background/Objectives: Hidrocystomas, eccrine and apocrine, are rare cystic lesions that form benign tumors of the sweat glands. This study aimed to describe the clinical features of hidrocystomas and evaluate the role of non-invasive imaging techniques, including dermoscopy, Line-field Confocal Optical Coherence Tomography (LC-OCT), Reflectance Confocal Microscopy (RCM), and Ultra-High-Frequency Ultrasound (UHFUS), in their diagnosis. Methods: In total, seven cases of hidrocystomas were collected from the Dermatologic Clinic of the University of Siena, Italy. Predefined dermoscopic, LC-OCT, RCM, and UHFUS features were retrospectively described. Results: Overall, hidrocystomas were located on the face, mainly presenting as blue/purple-bluish translucent papules (71%). Dermoscopic examination revealed a homogeneous purple-bluish color (71%), white pale halo (71%), and shiny globules at the periphery (57%). LC-OCT identified a hyporeflective cupoliform round structure in the dermis with a bright and thick contour, while UHFUS showed a round structure in the dermis filled with hypoechoic content. Conclusions: Non-invasive imaging techniques could significantly enhance the diagnostic accuracy of hidrocystomas, aid in differentiation from other lesions, and minimize unnecessary biopsies.

Clinical guideline for non-invasive imaging techniques in diagnosing adult cardiomyopathies (2024 edition)

Cardiomyopathies encompass a spectrum of disorders characterized by alterations in cardiac structure, function, histology, and the conduction system due to myocardial cell injury, with significant variability in clinical presentation and prognosis. Despite overlapping morphological features among different types of cardiomyopathies, phenotypic similarities limit the precision of diagnosis and consequently affect the development of therapeutic strategies and prognosis assessment. Diagnosis begins with the screening of myocardial morphology and function abnormalities, primarily relying on multimodal non-invasive imaging techniques such as echocardiography, CT scans, and cardiac magnetic resonance imaging. These techniques comprehensively summarize the structural, functional, and tissue characteristics of various cardiomyopathies, highlighting differences among them. In view of the unique needs and current situation in this field in China, an expert group has jointly developed the"Clinical guideline for non-invasive imaging techniques in diagnosing adult cardiomyopathies (2024 edition)."These guidelines integrate the latest evidence, and systematically introduce the diagnosis and monitoring of cardiomyopathy, the application of non-invasive imaging techniques, and the analysis of features of cardiomyopathies, thereby to enhance the precise diagnosis of cardiomyopathy and aid in offering information for therapeutic decision-making, aiming to provide reasonable recommendations for clinicians in China.

Advances in Cardiac Imaging and Genetic Testing for Diagnosis and Risk Stratification in Cardiomyopathies: 2024 Update

Cardiomyopathies represent a diverse group of heart muscle diseases marked by structural and functional abnormalities that are not primarily caused by coronary artery disease. Recent advances in non-invasive imaging techniques, such as echocardiography, cardiac magnetic resonance, and computed tomography, have transformed diagnostic accuracy and risk stratification, reemphasizing the role of cardiac imaging in diagnosis, phenotyping, and management of these conditions. Genetic testing complements imaging by clarifying inheritance patterns, assessing sudden cardiac death risk, and informing therapeutic choices. Integrating imaging data, such as left ventricular wall thickness, fibrosis, and apical aneurysms, with genetic findings enhances decision-making for implantable cardioverter-defibrillators in high-risk patients. Emerging technologies like artificial intelligence, strain imaging, and molecular imaging, alongside genetic testing, hold the promise of further refining diagnosis and personalized treatment approaches. This article summarizes the current state and future perspectives of cardiac imaging and genetic testing for diagnosis and risk stratification in cardiomyopathies, offering practical insights for patients’ management.

Relaxation-exchange magnetic resonance imaging (REXI): a non-invasive imaging method for evaluating trans-barrier water exchange in the choroid plexus.

The choroid plexus (CP) plays a crucial role in cerebrospinal fluid (CSF) production and brain homeostasis. However, non-invasive imaging techniques to assess its function remain limited. This study was conducted to develop a novel, contrast-agent-free MRI technique, termed relaxation-exchange magnetic resonance imaging (REXI), for evaluating CP-CSF water transport, a potential biomarker of CP function. REXI utilizes the inherent and large difference in magnetic resonance transverse relaxation times (T2s) between CP tissue (e.g., blood vessels and epithelial cells) and CSF. It uses a filter block to remove most CP tissue magnetization (shorter T2), a mixing block for CP-CSF water exchange with mixing time tm, and a detection block with multi-echo acquisition to determine the CP/CSF component fraction after exchange. The REXI pulse sequence was implemented on a 9.4 T preclinical MRI scanner. For validation of REXI's ability to measure exchange, we conducted preliminary tests on urea-water proton-exchange phantoms with various pH levels. We measured the steady-state water efflux rate from CP to CSF in rats and tested the sensitivity of REXI in detecting CP dysfunction induced by the carbonic anhydrase inhibitor acetazolamide. REXI pulse sequence successfully captured changes in the proton exchange rate (from short-T2 component to long-T2 component [i.e., ksl]) of urea-water phantoms at varying pH, demonstrating its sensitivity to exchange processes. In rat CP, REXI significantly suppressed the CP tissue signal, reducing the short-T2 fraction (fshort) from 0.44 to 0.23 (p < 0.0001), with significant recovery to 0.28 after a mixing time of 400 ms (p = 0.014). The changes in fshort at various mixing times can be accurately described by a two-site exchange model, yielding a steady-state water efflux rate from CP to CSF (i.e., kbc) of 0.49 s-1. A scan-rescan experiment demonstrated that REXI had excellent reproducibility in measuring kbc (intraclass correlation coefficient = 0.90). Notably, acetazolamide-induced CSF reduction resulted in a 66% decrease in kbc within rat CP. This proof-of-concept study demonstrates the feasibility of REXI for measuring trans-barrier water exchange in the CP, offering a promising biomarker for future assessments of CP function.

Noninvasive Imaging Test to Assess Liver Fibrosis: Vibration-controlled Transient Elastography

Liver fibrosis refers to the formation of scar tissue in the liver when inflammation persists over a long period. Assessing liver fibrosis is crucial for predicting the prognosis of chronic liver disease and managing patients with these conditions. Although a liver biopsy remains the gold standard for assessing liver fibrosis, it is limited by its invasive nature. Consequently, continuous efforts have been made to develop non-invasive methods for evaluating liver fibrosis, including imaging techniques and serum biomarkers. Vibration-controlled transient elastography (VCTE), a representative non-invasive imaging technique, has been used widely for liver fibrosis assessment since its introduction in 2003. This paper discusses the principles and methods of measurement, the advantages and disadvantages, and the considerations for interpreting VCTE based on the 2024 KASL Clinical Practice Guidelines for Non-invasive Tests to Assess Liver Fibrosis in Chronic Liver Disease. In addition, the diagnostic utility of VCTE in chronic viral hepatitis is reviewed.

Advanced Artificial Intelligence Techniques for Comprehensive Dermatological Image Analysis and Diagnosis

With the growing complexity of skin disorders and the challenges of traditional diagnostic methods, AI offers exciting new solutions that can enhance the accuracy and efficiency of dermatological assessments. Reflectance Confocal Microscopy (RCM) stands out as a non-invasive imaging technique that delivers detailed views of the skin at the cellular level, proving its immense value in dermatology. The manual analysis of RCM images, however, tends to be slow and inconsistent. By combining artificial intelligence (AI) with RCM, this approach introduces a transformative shift toward precise, data-driven dermatopathology, supporting more accurate patient stratification, tailored treatments, and enhanced dermatological care. Advancements in AI are set to revolutionize this process. This paper explores how AI, particularly Convolutional Neural Networks (CNNs), can enhance RCM image analysis, emphasizing machine learning (ML) and deep learning (DL) methods that improve diagnostic accuracy and efficiency. The discussion highlights AI’s role in identifying and classifying skin conditions, offering benefits such as a greater consistency and a reduced strain on healthcare professionals. Furthermore, the paper explores AI integration into dermatological practices, addressing current challenges and future possibilities. The synergy between AI and RCM holds the potential to significantly advance skin disease diagnosis, ultimately leading to better therapeutic personalization and comprehensive dermatological care.

Near-Infrared Autofluorescence: Early Detection of Retinal Pigment Epithelial Alterations in Inherited Retinal Dystrophies.

Near-infrared autofluorescence (NIA) is a non-invasive retinal imaging technique used to examine the retinal pigment epithelium (RPE) based on the autofluorescence of melanin. Melanin has several functions within RPE cells. It serves as a protective antioxidative factor and is involved in the phagocytosis of photoreceptor outer segments. Disorders affecting the photoreceptor-RPE complex result in alterations of RPE cells which are detectable by alterations of NIA. NIA allows us to detect early alterations in various chorioretinal disorders, frequently before they are ophthalmoscopically visible and often prior to alterations in lipofuscin-associated fundus autofluorescence (FAF) or optical coherence tomography (OCT). Although NIA and FAF relate to disorders affecting the RPE, the findings for both imaging methods differ and the area involved has been demonstrated to be larger in NIA compared to FAF in several disorders, especially inherited retinal dystrophies (IRDs), indicating that NIA detects earlier alterations compared to FAF. Foveal alterations can be much more easily detected using NIA compared to FAF. A reduced subfoveal NIA intensity is the earliest sign of autosomal dominant Best disease, when FAF and OCT are still normal. In other IRDs, a preserved subfoveal NIA intensity is associated with good visual acuity. So far, the current knowledge on NIA in IRD has been presented in multiple separate publications but has not been summarized in an overview. This review presents the current knowledge on NIA in IRD and demonstrates NIA biomarkers.

In vivo reflectance confocal microscopy role for early to advanced lentigo maligna melanoma spectrum: A systematic review and pooled analysis.

Lentigo maligna (LM) is a growing problem worldwide and the main type of melanoma insitu in some Caucasian populations. It presents as a spectrum from atypical intraepidermal melanocytic proliferation (AIMP) to invasive lentigo maligna melanoma (LMM). Accurate diagnosis and staging are crucial for determining appropriate management strategies. To assess the role of invivo reflectance confocal microscopy (RCM) in differentiating early and advanced stages of lentigo maligna. A systematic search was conducted on Medline, PubMed, Scopus, Web of Science, Proquest Central, Embase, Cochrane and Google Scholar. References of included and excluded studies were reviewed for additional sources. Studies involving RCM for AIMP, LM and LMM diagnosis published until 1 December 2023, were selected for analysis. Publications on other non-invasive imaging techniques, other skin diseases or those addressing only RCM surgical margins and therapeutic response without reporting RCM features were excluded. Each article was reviewed by two experts, with disagreements resolved by a third expert. Data for each RCM feature were pooled, and odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Features were stratified for architecture, cellular morphology and distribution. Twenty-seven articles involving 303 lesions were included. Features favouring LM over AIMP included (1) Architecture: the presence of non-edged papillae (OR 4.50, 95% CI 1.92-10.52) and (2) Cellular distribution: widespread pagetoid cells and junctional atypical cells (OR 25.06, 95% CI 4.19-148.45) and junctional nests (OR 18.06, 95% CI 3.04-106.32). Features favouring LMM over LM included (1) Architecture: epidermal disarray (OR 5.03, 95% CI 1.90-13.22), junctional disarray (OR 4.19, 95% CI 1.54-11.33) and destroyed collagen fibres (OR 3.91, 95% CI 1.80-8.44) and (2) cellular distribution: widespread pagetoid and junctional atypical cells (OR 2.71, 95% CI 1.27-5.75) and junctional nests (OR 4.48, 95% CI 2.12-9.43). This study identifies the RCM features associated with the LM/LMM spectrum.

Brain tumor diagnosis in MRI scans images using Residual/Shuffle Network optimized by augmented Falcon Finch optimization

Brain tumor diagnosis is an important task in prognosing and treatment planning of the patients with brain cancer. in the meantime, using the Magnetic Resonance Imaging (MRI) as a commonly used non-invasive imaging technique provide the experts a helpful view for detecting the brain tumors. While deep learning methods have shown significant success in analyzing medical images, they often require careful design of architecture and tuning of hyperparameters to achieve optimal results. This study presents a new approach for diagnosing brain tumors in MRI scans using deep learning, focusing on Residual/Shuffle Networks. The designed network structures offer efficient results when compared to traditional deep learning models. To enhance the proposed network for brain tumor classification, a modified metaheuristic algorithm named Augmented Falcon Finch Optimization (AFFO) is introduced. AFFO utilizes bio-inspired principles to effectively search for the best hyperparameter configurations, thereby enhancing the reliability and accuracy of the deep learning model. The performance of the proposed method is evaluated on a standard brain tumor MRI dataset and compared with existing techniques, including ResNet, AlexNet, VGG-16, Inception V3, and U-Net to illustrate the effectiveness of combining Residual/Shuffle Networks with AFFO for brain tumor diagnosis.

Application of Noninvasive Imaging Techniques for High Energy Density Lithium Metal Rechargeable Batteries

AbstractLithium metal batteries (LMBs) have the potential to exceed the energy density of current lithium‐ion batteries. Achieving this requires a thick positive electrode, a thin Li metal negative electrode, and minimal electrolyte‐loading. Despite their promise, high energy density LMBs with high‐loading positive electrodes, thin Li, and low electrolytes face significant challenges. A key issue is the high reactivity of Li metal with nonaqueous electrolytes, leading to the consumption of both during each cycle. This reaction causes insulating Li compounds to accumulate, increases electrode porosity and thickness, depletes the electrolyte, raises cell impedance, and reduces capacity. Therefore, understanding the interphase evolution of the Li metal electrode is crucial to addressing cell failure. While various ex situ and in situ techniques have been used to study these interphases, they often involve non‐practical cell configurations and sample‐damaging preparation processes. In this regard, noninvasive methods like X‐ray and neutron‐based imaging are beneficial as they do not damage samples, can be used both in situ and ex situ, employ practical cell configurations, and enable long‐term data collection. This review explores recent advancements in X‐ray and neutron‐based techniques for characterizing high‐energy LMBs, emphasizing their potential to improve understanding of interphasial dynamics and advance robust high‐energy‐density batteries.

The impact of body position on neurofluid dynamics: present insights and advancements in imaging.

The intricate neurofluid dynamics and balance is essential in preserving the structural and functional integrity of the brain. Key among these forces are: hemodynamics, such as heartbeat-driven arterial and venous blood flow, and hydrodynamics, such as cerebrospinal fluid (CSF) circulation. The delicate interplay between these dynamics is crucial for maintaining optimal homeostasis within the brain. Currently, the widely accepted framework for understanding brain functions is the Monro-Kellie's doctrine, which posits a constant sum of intracranial CSF, blood flow and brain tissue volumes. However, in recent decades, there has been a growing interest in exploring the dynamic interplay between these elements and the impact of external factors, such as daily changes in body position. CSF circulation in particular plays a crucial role in the context of neurodegeneration and dementia, since its dysfunction has been associated with impaired clearance mechanisms and accumulation of toxic substances. Despite the implementation of various invasive and non-invasive imaging techniques to investigate the intracranial hemodynamic or hydrodynamic properties, a comprehensive understanding of how all these elements interact and are influenced by body position remains wanted. Establishing a comprehensive overview of this topic is therefore crucial and could pave the way for alternative care approaches. In this review, we aim to summarize the existing understanding of intracranial hemodynamic and hydrodynamic properties, fundamental for brain homeostasis, along with factors known to influence their equilibrium. Special attention will be devoted to elucidating the effects of body position shifts, given their significance and remaining ambiguities. Furthermore, we will explore recent advancements in imaging techniques utilized for real time and non-invasive measurements of dynamic body fluid properties in-vivo.

Unsupervised Image Segmentation on 2D Echocardiogram

Echocardiography is a widely used, non-invasive imaging technique for diagnosing and monitoring heart conditions. However, accurate segmentation of cardiac structures, particularly the left ventricle, remains a complex task due to the inherent variability and noise in echocardiographic images. Current supervised models have achieved state-of-the-art results but are highly dependent on large, annotated datasets, which are costly and time-consuming to obtain and depend on the quality of the annotated data. These limitations motivate the need for unsupervised methods that can generalize across different image conditions without relying on annotated data. In this study, we propose an unsupervised approach for segmenting 2D echocardiographic images. By combining customized objective functions with convolutional neural networks (CNNs), our method effectively segments cardiac structures, addressing the challenges posed by low-resolution and gray-scale images. Our approach leverages techniques traditionally used outside of medical imaging, optimizing feature extraction through CNNs in a data-driven manner and with a new and smaller network design. Another key contribution of this work is the introduction of a post-processing algorithm that refines the segmentation to isolate the left ventricle in both diastolic and systolic positions, enabling the calculation of the ejection fraction (EF). This calculation serves as a benchmark for evaluating the performance of our unsupervised method. Our results demonstrate the potential of unsupervised learning to improve echocardiogram analysis by overcoming the limitations of supervised approaches, particularly in settings where labeled data are scarce or unavailable.

Impact of light polarization on laser speckle contrast imaging with a custom phantom for microvascular flow

Laser speckle contrast imaging (LSCI) is a non-invasive, powerful, and cost-effective imaging technique that has seen widespread adoption across various medical fields, particularly for blood flow imaging. While LSCI provides physicians with valuable insights into changes or occlusions in blood flow, the technique is susceptible to various factors and parameters that can impact measurement sensitivity and signal-to-noise ratio (SNR). These include the scattering of light, which can affect the quality and reliability of the LSCI data. The polarization of light holds significant promise to enhance the performance of LSCI. In this study, we employed polarization manipulation of light to investigate its impact on the performance of LSCI for measuring flow. Focusing on the application of LSCI in microcirculation within capillaries, we examined the effect of polarization control on the technique's flow measurement capabilities using a custom-designed phantom system. This phantom consisted of three tubes with inner diameters of 1.1 mm, 1.6 mm, and 2.8 mm, embedded in a polydimethylsiloxane (PDMS) matrix with optical properties similar to biological tissue. By manipulating the polarization of both the incident and reflected light, alternating between parallel and perpendicular states, we compared the performance of our LSCI system in detecting flow for different tube diameters and depths within the phantom. Our study revealed that while depth is a critical parameter influencing flow detection using LSCI, employing perpendicular polarization (between incident and reflected light) resulted in the lowest measurement error and highest SNR compared to parallel polarization and the absence of polarization control.

In Vivo Detection of Lymph Nodes Metastasis of ESCC Using CXCR4-Targeted Tracer [64Cu]Cu-NOTA-CP01.

Esophageal squamous cell carcinoma (ESCC) frequently exhibits skip metastasis to lymph nodes; however, non-invasive imaging techniques capable of directly visualizing metastatic lymph nodes (MLN) are still lacking. Although biopsy is the clinical standard method, it is invasive and poses risks to patient health. This study aims to detect MLN in an intralymphatic tumor metastasis model of ESCC using the CXCR4-targeted tracer [64Cu]Cu-NOTA-CP01. The CXCR4 expression in ESCC cell lines was assessed using Western blot and immunofluorescence. An intralymphatic tumor metastasis model was established and monitored using bioluminescence imaging (BLI). Small animal PET studies and biodistribution studies were performed to evaluate the specificity of [64Cu]Cu-NOTA-CP01 for MLN. Histopathology evaluation was employed to check for the presence of metastatic tumor cells and to assess CXCR4 expression levels in the metastatic lymph nodes. The intralymphatic tumor metastasis model was successfully established using the EC109/Luc cell line, which exhibited high CXCR4 expression, as verified by BLI. PET/CT imaging showed that the MLN uptakes in the baseline group were significantly inhibited in the blocking group. The ratios of MLN/muscle and MLN/blood werealso significantly higher in the baseline group than in the blocking group. Ex vivo PET/CT imaging of MLN corroborated the in vivo data. Biodistribution studies further supported the PET imaging studies, showing rapid clearance of the tracer from the blood and major organs, with significantly higher MLN/muscle and MLN/blood ratios in the baseline group compared to the blocking group. Histopathological staining verified positive CXCR4 expression in these lymph nodes containing metastatic tumor cells. Targeting CXCR4 with [64Cu]Cu-NOTA-CP01 for PET imaging of lymph nodes metastasis represents a promising approach that warrants further investigation. These findings have the potential to enhance diagnostic and therapeutic strategies for individuals with lymph nodes metastasis of ESCC.

LC-OCT for early diagnosis and characterization of dermatologic adverse events to oncologic drugs and correlation to histopathology.

Targeted and immune therapies have recently been associated with the occurrence of multiple cutaneous toxicities, often challenging to differentiate by clinical examination alone without histology. Line-field confocal optical coherence tomography (LC-OCT) is a non-invasive and innovative imaging technique that has been shown to be almost as effective as histology in diagnosing several skin conditions. Our study aimed to assess the effectiveness of LC-OCT in predicting the clinical evolution of early maculopapular eruptions related to antineoplastic targeted therapy and immunotherapy. In the period between May 2023 and December 2023, consecutive patients with clinical cutaneous maculopapular reactions caused by oncologic targeted therapy or immunotherapy were enrolled at the dermatologic outpatient clinic of the Fondazione Policlinico A. Gemelli IRCCS, Rome. Patients underwent clinical dermatological examination, video-dermoscopy, LC-OCT, and incisional skin biopsy of a target cutaneous lesion. To investigate the evolutionary pattern of maculopapular lesions (psoriasic eruption, lichenoid eruption, eczematous eruption, bullous eruption), LC-OCT and histopathological images have been compared based on specific characteristics (hyperkeratosis, acanthosis, spongiosis; papillomatosis; lichenoid inflammatory infiltrate; presence of intraepidermal/subepidermal cleavage plan with formation of a bulla). Eighteen patients were included in this study (11 males, 7 females, median age 64.5). LC-OCT demonstrated an overall concordance with histology of 77.8%, with a Cohen's Kappa of 0.69 (P < 0.0001). Sensitivity exceeded 70%, and specificity was ≥88.2%. The Area Under the Curve (AUC) ranged from 0.9 to 1 for psoriasis and lichenoid eruption and from 0.7 to 0.9 for eczematous and bullous eruption. LC-OCT appears to be a promising tool for the early differential diagnosis of adverse skin reactions related to targeted therapy and immunotherapy, offering the potential to avoid skin biopsies in fragile cancer patients.

Integrating Echocardiographic Findings to Improve Diagnostic and Prognostic Assessments in Moderate Aortic Valve Stenosis: A Comprehensive Review

Aortic valve stenosis (AS) is the most common valvular heart disease in developed countries, with its prevalence on the rise due to aging populations. While severe AS has long been recognized as high-risk, recent studies have shed light on the significant association between moderate AS and cardiovascular morbidity and mortality. Yet, the data are still inconclusive. With noninvasive multi-modality imaging techniques advancing rapidly, we now have more insight into the underlying biology of AS. Echocardiography continues to serve as the primary noninvasive imaging modality for diagnosing and grading AS. This comprehensive review delves into the role of echocardiography in diagnosing moderate AS and how the findings can support clinicians in making well-informed decisions that impact patients’ prognoses.