Expression of TGF-β/Smad pathway-related indices in patients with isolated iliac artery aneurysms complicated with iliac arteriovenous fistula and their relationship with prognosis

OBJECTIVE

This study investigated the expression of TGF-β/Smad pathway-related indices in patients with isolated iliac artery aneurysms (IIAA) complicated with iliac arteriovenous fistula (IAVF) and their relationship with prognosis.

METHODS

From January 2016 to June 2022, 83 patients with IIAA complicated with IAVF (Study group) and 54 patients with IIAA not complicated with IAVF (control group) were studied. The related indices of TGF-β/Smad pathway were evaluated, and the effects of each index on the formation of IAVF were analyzed. The patients were divided into the survival group (64 cases) and death group (19 cases), and the prognostic value of indices in combination was analyzed.

RESULTS

TGF-β, p-Smad2, p-Smad3, p-JNK, and p-ERK in the study group were higher than those in the control group. Abnormal increase of pSmad3 expression was a risk factor for IAVF formation in patients with IIAA. TGF-β level in the death group was higher than that in the survival group, and p-Smad3 and p-JNK proteins were higher than those in the survival group. The AUC value of indices in the TGF-β/Smad pathway in combination was greater than that of each index alone. Abnormal increased expression of pSmad3 was a risk factor for prognosis of patients with IIAA complicated with IAVF.

CONCLUSION

The abnormal increase of TGF-β/Smad pathway-related indices is related to poor prognosis of patients with IIAA complicated with IAVF, and the combined detection of all indices has a predictive value for patients' prognosis.

Diagnostic value of ultrasound elastography in polycystic ovary syndrome: a systematic review and meta-analysis

OBJECTIVE

The main purpose of this systematic review and meta-analysis was to investigate the diagnostic value of ultrasound elastography in the evaluation of polycystic ovary syndrome (PCOS).

METHODS

A comprehensive and methodical investigation was carried out in the databases of PubMed, EMBASE, Cochrane, Scopus, Web of Science, and China National Knowledge Infrastructure, covering the entire duration of these databases until October 18, 2023. The primary purpose of this research was to evaluate and contrast ovarian tissue elasticity in people with and without PCOS. The elasticity of ovarian tissue was quantified using standardized mean difference (SMD).

RESULTS

A total of eight studies were ultimately selected for systematic evaluation and meta-analysis. Five studies used shear wave elastography (SWE) as a diagnostic tool, and it was discovered that women with PCOS had higher levels of ovarian shear wave elasticity than their healthy counterparts. The SMD was determined to be 1.86 kilopascal (95% CI: 1.27 to 2.44). Three studies were conducted using strain elastography (SE) to compare the ovarian strain ratio of patients with PCOS to that of a healthy control group. The SMD for the PCOS group was 2.07 (95% CI: 1.79 to 2.34), which indicated that the ovarian strain ratio was significantly higher in that group.

CONCLUSION

This systematic review and meta-analysis found that women with PCOS had stiffer ovarian tissue than women without the disorder. Ultrasound elastography may provide clinicians with value beyond 2D ultrasound in the diagnosis of PCOS.

Radiomics-based analysis of dynamic contrast-enhanced magnetic resonance image: A prediction nomogram for lymphovascular invasion in breast cancer

OBJECTIVE

To develop and validate a nomogram for quantitively predicting lymphovascular invasion (LVI) of breast cancer (BC) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and morphological features.

METHODS

We retrospectively divided 238 patients with BC into training and validation cohorts. Radiomic features from DCE-MRI were subdivided into A1 and A2, representing the first and second post-contrast images respectively. We utilized the minimal redundancy maximal relevance filter to extract radiomic features, then we employed the least absolute shrinkage and selection operator regression to screen these features and calculate individualized radiomics score (Rad score). Through the application of multivariate logistic regression, we built a prediction nomogram that integrated DCE-MRI radiomics and MR morphological features (MR-MF). The diagnostic capabilities were evaluated by comparing C-indices and calibration curves.

RESULTS

The diagnostic efficiency of the A1/A2 radiomics model surpassed that of the A1 and A2 alone. Furthermore, we incorporated the MR-MF (diffusion-weighted imaging rim sign, peritumoral edema) and optimized Radiomics into a hybrid nomogram. The C-indices for the training and validation cohorts were 0.868 (95% CI: 0.839-0.898) and 0.847 (95% CI: 0.787-0.907), respectively, indicating a good level of discrimination. Moreover, the calibration plots demonstrated excellent agreement in the training and validation cohorts, confirming the effectiveness of the calibration.

CONCLUSION

This nomogram combined MR-MF and A1/A2 Radiomics has the potential to preoperatively predict LVI in patients with BC.

Ischemia-reperfusion injury in a salvaged penumbra: Longitudinal high-tesla perfusion magnetic resonance imaging in a rat model

INTRODUCTION

Although ischemia-reperfusion (I/R) injury varies between cortical and subcortical regions, its effects on specific regions remain unclear. In this study, we used various magnetic resonance imaging (MRI) techniques to examine the spatiotemporal dynamics of I/R injury within the salvaged ischemic penumbra (IP) and reperfused ischemic core (IC) of a rodent model, with the aim of enhancing therapeutic strategies by elucidating these dynamics.

MATERIALS AND METHODS

A total of 17 Sprague-Dawley rats were subjected to 1 h of transient middle cerebral artery occlusion with a suture model. MRI, including diffusion tensor imaging (DTI), T2-weighted imaging, perfusion-weighted imaging, and T1 mapping, was conducted at multiple time points for up to 5 days during the I/R phases. The spatiotemporal dynamics of blood-brain barrier (BBB) modifications were characterized through changes in T1 within the IP and IC regions and compared with mean diffusivity (MD), T2, and cerebral blood flow.

RESULTS

During the I/R phases, the MD of the IC initially decreased, normalized after recanalization, decreased again at 24 h, and peaked on day 5. By contrast, the IP remained relatively stable. Both the IP and IC exhibited hyperperfusion, with the IP reaching its peak at 24 h, followed by resolution, whereas hyperperfusion was maintained in the IC until day 5. Despite hyperperfusion, the IP maintained an intact BBB, whereas the IC experienced persistent BBB leakage. At 24 h, the IC exhibited an increase in the T2 signal, corresponding to regions exhibiting BBB disruption at 5 days.

CONCLUSIONS

Hyperperfusion and BBB impairment have distinct patterns in the IP and IC. Quantitative T1 mapping may serve as a supplementary tool for the early detection of malignant hyperemia accompanied by BBB leakage, aiding in precise interventions after recanalization. These findings underscore the value of MRI markers in monitoring ischemia-specific regions and customizing therapeutic strategies to improve patient outcomes.

Non-linear relationship between age and subfoveal choroidal thickness in Chinese patients with proliferative diabetic retinopathy

BACKGROUND

No study has investigated the change regularity between age and subfoveal choroidal thickness (SFCT) in proliferative diabetic retinopathy (PDR).

AIM

To investigate the relationship between the SFCT and age in Chinese patients with PDR.

METHODS

This was a cross-sectional retrospective study. The participants were hospitalized individuals with type 2 diabetes who underwent vitrectomy for PDR. Con-tralateral eyes that met the criteria were included in the study. All necessary laboratory tests were performed at the time of admission. Central macular thickness (CMT) and SFCT were two quantitative assessments made using enhanced depth imaging optical coherence tomography. CMT was measured automatically and SFCT was measured manually with digital calipers provided by the Heidelberg Eye Explorer software.

RESULTS

The final analysis included a total of 234 individuals with PDR. The average age was 55.60 years old ± 10.03 years old, and 57.69% of the population was male. Univariate analysis revealed a significant negative connection between age and SFCT in patients with PDR [β = -2.44, 95% confidence interval (95%CI): -3.46 to -1.42; P < 0.0001]. In the fully adjusted model, the correlation between SFCT and age remained steady (β = -1.68, 95%CI: -2.97 to -0.39; P = 0.0117). Spline smoothing showed that the relationship between SFCT and age in patients with PDR was non-linear, with an inflection point at 54 years of age.

CONCLUSION

Our findings suggest that age is a key determinant of choroidal thickness. The non-linear link between SFCT and age in PDR patients should be taken into account.

Artificial intelligence-driven multiomics predictive model for abdominal aortic aneurysm subtypes to identify heterogeneous immune cell infiltration and predict disease progression

BACKGROUND

Abdominal aortic aneurysm (AAA) poses a significant health risk and is influenced by various compositional features. This study aimed to develop an artificial intelligence-driven multiomics predictive model for AAA subtypes to identify heterogeneous immune cell infiltration and predict disease progression. Additionally, we investigated neutrophil heterogeneity in patients with different AAA subtypes to elucidate the relationship between the immune microenvironment and AAA pathogenesis.

METHODS

This study enrolled 517 patients with AAA, who were clustered using k-means algorithm to identify AAA subtypes and stratify the risk. We utilized residual convolutional neural network 200 to annotate and extract contrast-enhanced computed tomography angiography images of AAA. A precise predictive model for AAA subtypes was established using clinical, imaging, and immunological data. We performed a comparative analysis of neutrophil levels in the different subgroups and immune cell infiltration analysis to explore the associations between neutrophil levels and AAA. Quantitative polymerase chain reaction, Western blotting, and enzyme-linked immunosorbent assay were performed to elucidate the interplay between CXCL1, neutrophil activation, and the nuclear factor (NF)-κB pathway in AAA pathogenesis. Furthermore, the effect of CXCL1 silencing with small interfering RNA was investigated.

RESULTS

Two distinct AAA subtypes were identified, one clinically more severe and more likely to require surgical intervention. The CNN effectively detected AAA-associated lesion regions on computed tomography angiography, and the predictive model demonstrated excellent ability to discriminate between patients with the two identified AAA subtypes (area under the curve, 0.927). Neutrophil activation, AAA pathology, CXCL1 expression, and the NF-κB pathway were significantly correlated. CXCL1, NF-κB, IL-1β, and IL-8 were upregulated in AAA. CXCL1 silencing downregulated NF-κB, interleukin-1β, and interleukin-8.

CONCLUSION

The predictive model for AAA subtypes demonstrated accurate and reliable risk stratification and clinical management. CXCL1 overexpression activated neutrophils through the NF-κB pathway, contributing to AAA development. This pathway may, therefore, be a therapeutic target in AAA.

Sonographic Measurement of Median Nerve Cross-Sectional Area to Determine Severity of Carpal Tunnel Syndrome: A Cautionary Tale

OBJECTIVES

Evaluate the diagnostic accuracy of median nerve cross-sectional area (CSA) to determine the severity of carpal tunnel syndrome (CTS) vs the presence of CTS across existing electrodiagnostic-based (EDX) classification systems.

METHODS

Retrospective analysis of cross-sectional patient data. Receiver operating characteristic (ROC) analysis was used to determine CSA cutoff values and associated diagnostic likelihood ratios for all consolidated and binary EDX-based classifications of CTS severity. Identification of CSA cutoff values associated with likelihood ratios capable of achieving conclusive (but at least moderate) shifts in diagnostic probability.

RESULTS

Binary categorizations of CTS (ie, "Normal" vs "Absent") were statistically superior to consolidated categorizations of CTS severity (ie, "Normal," "Mild," "Moderate," or "Severe"). Binary categorizations established consistent CSA cutoff values across all EDX-based classifications examined and achieved conclusive shifts in diagnostic probability based on the following values of distal CSA or delta CSA: <7 or <1 mm2 to rule out and >13 or >7 mm2 to rule in CTS, respectively. Additionally, the following values of distal CSA and delta CSA may be used in certain circumstances because they produce only small shifts in diagnostic probability: ≤10 or ≤3 mm2 to rule out and ≥11 or ≥4 mm2 to rule in CTS, respectively.

CONCLUSIONS

Using median nerve CSA to categorize the severity of CTS is not recommended based on lack of consistent and meaningful shifts in diagnostic probability. Rather, binary categorizations to rule out or rule in CTS based on the proposed CSA cutoff values consistently provided conclusive shifts in diagnostic probability across all EDX-based classifications examined.

Advances and applications of biomimetic biomaterials for endogenous skin regeneration

Endogenous regeneration is becoming an increasingly important strategy for wound healing as it facilitates skin's own regenerative potential for self-healing, thereby avoiding the risks of immune rejection and exogenous infection. However, currently applied biomaterials for inducing endogenous skin regeneration are simplistic in their structure and function, lacking the ability to accurately mimic the intricate tissue structure and regulate the disordered microenvironment. Novel biomimetic biomaterials with precise structure, chemical composition, and biophysical properties offer a promising avenue for achieving perfect endogenous skin regeneration. Here, we outline the recent advances in biomimetic materials induced endogenous skin regeneration from the aspects of structural and functional mimicry, physiological process regulation, and biophysical property design. Furthermore, novel techniques including in situ reprograming, flexible electronic skin, artificial intelligence, single-cell sequencing, and spatial transcriptomics, which have potential to contribute to the development of biomimetic biomaterials are highlighted. Finally, the prospects and challenges of further research and application of biomimetic biomaterials are discussed. This review provides reference to address the clinical problems of rapid and high-quality skin regeneration.

A comprehensive identification of potential molecular targets and small drugs candidate for melanoma cancer using bioinformatics and network-based screening approach

Melanoma is the third most common malignant skin tumor and has increased in morbidity and mortality over the previous decade due to its rapid spread into the bloodstream or lymphatic system. This study used integrated bioinformatics and network-based methodologies to reliably identify molecular targets and small molecular medicines that may be more successful for Melanoma diagnosis, prognosis and treatment. The statistical LIMMA approach utilized for bioinformatics analysis in this study found 246 common differentially expressed genes (cDEGs) between case and control samples from two microarray gene-expression datasets (GSE130244 and GSE15605). Protein-protein interaction network study revealed 15 cDEGs (PTK2, STAT1, PNO1, CXCR4, WASL, FN1, RUNX2, SOCS3, ITGA4, GNG2, CDK6, BRAF, AGO2, GTF2H1 and AR) to be critical in the development of melanoma (KGs). According to regulatory network analysis, the most important transcriptional and post-transcriptional regulators of DEGs and hub-DEGs are ten transcription factors and three miRNAs. We discovered the pathogenetic mechanisms of MC by studying DEGs' biological processes, molecular function, cellular components and KEGG pathways. We used molecular docking and dynamics modeling to select the four most expressed genes responsible for melanoma malignancy to identify therapeutic candidates. Then, utilizing the Connectivity Map (CMap) database, we analyzed the top 4-hub-DEGs-guided repurposable drugs. We validated four melanoma cancer drugs (Fisetin, Epicatechin Gallate, 1237586-97-8 and PF 431396) using molecular dynamics simulation with their target proteins. As a result, the results of this study may provide resources to researchers and medical professionals for the wet-lab validation of MC diagnosis, prognosis and treatments.Communicated by Ramaswamy H. Sarma.

Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research

Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.

Impact of intelligent convolutional neural network -based algorithms on head computed tomography evaluation and comprehensive rehabilitation acupuncture therapy for patients with cerebral infarction

This work was to evaluate the impacts of comprehensive rehabilitation acupuncture therapy on the recovery of neurological function in cerebral infarction (CI) patients and to utilize convolutional neural network (CNN) intelligent algorithms to optimize head computed tomography (CT) images and improve lesion localization accuracy. 98 CI patients were divided into a control group (Ctrl group) and an experimental group (Exp group), with 48 patients in each group. The patients in the Ctrl group received CT evaluation combined with comprehensive rehabilitation acupuncture therapy. While, those in the Exp group received CT evaluation with the use of CNN algorithms for optimization, along with comprehensive rehabilitation acupuncture therapy. Acupuncture therapy included selecting acupoints on the patient's head, selecting two horizontal needling needles from top to bottom at the acupoints on the front side of the lesion, and then horizontal needling along the top midline. The differences in treatment outcomes were compared between the two groups based on Fugl-Meyer upper limb assessment (FMA) scores, Barthel Index (BI) scores, National Institutes of Health Stroke Scale (NIHSS4) scores, Modified Edinburgh-Scandinavian Stroke Scale (MESSS) scores, and hemodynamics. Simultaneously, the CT images were optimized using CNN intelligent algorithms to improve image quality and lesion localization accuracy. The results showed that the CI CT images processed by the CNN-based intelligent algorithm showed significant improvements in clarity and contrast compared to conventional CT images. The CNN-based intelligent algorithm demonstrated higher sensitivity (97.5 %, 93.8 %), higher PSNR (30.14 dB, 24.72 dB), and lower missed detection rate (0.52 %, 1.88 %) in detecting CI lesions. The total effective rate in the Exp group was 95.83 %, which was significantly higher than the 85.42 % in the Ctrl group (P < 0.05). The Exp group showed significantly higher levels in FMA and BI scores (P < 0.05). After treatment, the NIHSS4 and MESSS scores in the Exp group were lower than those in the Ctrl group (P < 0.05). Additionally, post-treatment, the plasma concentrations and whole-blood viscosity (low shear and high shear) in the Exp group were lower than those in the Ctrl group, and the plasma concentration and whole-blood viscosity (high shear) were also lower than those in the Ctrl group (P < 0.05). In conclusion, comprehensive rehabilitation acupuncture therapy had a positive impact on the recovery of neurological function in CI patients. By applying CNN-based intelligent algorithms to optimize head CT images, lesion localization accuracy can be improved, thereby guiding rehabilitation treatment more effectively.

Challenges and limitations in applying radiomics to PET imaging: Possible opportunities and avenues for research

Radiomics, the high-throughput extraction of quantitative imaging features from medical images, holds immense potential for advancing precision medicine in oncology and beyond. While radiomics applied to positron emission tomography (PET) imaging offers unique insights into tumor biology and treatment response, it is imperative to elucidate the challenges and constraints inherent in this domain to facilitate their translation into clinical practice. This review examines the challenges and limitations of applying radiomics to PET imaging, synthesizing findings from the last five years (2019-2023) and highlights the significance of addressing these challenges to realize the full clinical potential of radiomics in oncology and molecular imaging. A comprehensive search was conducted across multiple electronic databases, including PubMed, Scopus, and Web of Science, using keywords relevant to radiomics issues in PET imaging. Only studies published in peer-reviewed journals were eligible for inclusion in this review. Although many studies have highlighted the potential of radiomics in predicting treatment response, assessing tumor heterogeneity, enabling risk stratification, and personalized therapy selection, various challenges regarding the practical implementation of the proposed models still need to be addressed. This review illustrates the challenges and limitations of radiomics in PET imaging across various cancer types, encompassing both phantom and clinical investigations. The analyzed studies highlight the importance of reproducible segmentation methods, standardized pre-processing and post-processing methodologies, and the need to create large multicenter studies registered in a centralized database to promote the continuous validation and clinical integration of radiomics into PET imaging.

How to Approach the Imaging Differential Diagnosis of Rheumatic Diseases by Anatomic Location

The purpose of this review is to present an approach to differential diagnosis based on the particular features of involvement of the most common rheumatological conditions focused on anatomic location (by joint). The most common radiological signs and how they are demonstrated in different modalities, as well as the typical patterns of involvement are analyzed, with the aim to facilitate the differential diagnosis. Early and adequate adjustment of treatment has an effect on outcome, and on this basis, early diagnosis and characterization are paramount to appropiately manage patients.

Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion

Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.

Median Nerve Cross-Sectional Area and Carpal Tunnel Syndrome in Specific Populations: Sonographic Analysis of Patients With Type 2 Diabetes or Bifid Anatomy

OBJECTIVES

Evaluate diagnostic accuracy of median nerve cross-sectional area (CSA) to determine severity versus presence of carpal tunnel syndrome (CTS) across existing electrodiagnostic-based (EDX) classification systems in patients with type 2 diabetes or bifid anatomy.

METHODS

Retrospective analysis of cross-sectional patient data. Receiver operating characteristic analysis used to determine median nerve CSA cutoff values and associated diagnostic likelihood ratios for all consolidated and binary EDX-based classifications of CTS in patients with type 2 diabetes or bifid anatomy. Determine shifts in diagnostic probability based on established cutoff values and associated likelihood ratios. Distal CSA measured at or near carpal tunnel inlet, proximal CSA at level of pronator quadratus muscle, and delta CSA by subtracting proximal from distal.

RESULTS

Binary categorizations of CTS were statistically superior to consolidated categorizations of CTS severity in patients with type 2 diabetes or bifid anatomy. Binary categorizations established reasonably consistent median nerve CSA cutoff values across all EDX-based classifications examined resulting in the following for distal CSA or delta CSA: ≤10 or ≤4 mm2 to rule out and ≥11 or ≥5 mm2 to rule in CTS, respectively. These cutoff values resulted in small shifts in diagnostic probability in patients with type 2 diabetes and small to conclusive shifts in diagnostic probability in patients with bifid anatomy.

CONCLUSIONS

Binary categorizations to rule out or rule in CTS based on the proposed cutoff values provide the most meaningful information about shifts in diagnostic probability across all EDX-based classifications examined. The use of median nerve CSA to categorize severity of CTS is not recommended in patients with type 2 diabetes or bifid anatomy.

Magnetic resonance lymphography findings across different clinical stages of lower limb lymphedema

OBJECTIVE

To investigate the findings on lower limb lymphedema using magnetic resonance lymphography (MRL).

METHODS

MRL was used to record the lymphatic vessel morphology, distribution of lymphatic vessels, dermal backflow (DBF), and morphology of inguinal lymph nodes in 112 patients (175 affected limbs) with lower limb lymphedema at different clinical stages (according to the International Society of Lymphology staging criteria 2020).

RESULTS

The lymphatic vessel morphology significantly differed at different clinical stages (X2 =59.306; P = 0.000). ISL stage I is dominated by "scattered beads" and "branch-like" distribution, ISL stage Ⅱ has tree branch or "capillary-like" distribution, and ISL stage Ⅲ primarily has a capillary pattern and contrast agent accumulation in the foot. There were statistically significant differences in the distribution of lymphatic vessels and DBF in different clinical stages. Distribution of the enhanced lymphatic vessels was distal to the knee in ISL stage I, involved areas below the knee joint or the whole limb in ISL stage II, and involved the whole limb in ISL stage III (X2 =44.591; P = 0.000). With the progression of edema, DBF severity increased (X2 =76.416; P = 0.000).

CONCLUSION

MRL revealed the morphology and distribution of lymphatic vessels and detected abnormal inguinal lymph nodes in patients at different stages of lymphedema, which can be used as reference information for surgical treatment.

Accurate classification of glomerular diseases by hyperspectral imaging and transformer

BACKGROUND AND OBJECTIVE

In renal disease research, precise glomerular disease diagnosis is crucial for treatment and prognosis. Currently reliant on invasive biopsies, this method bears risks and pathologist-dependent variability, yielding inconsistent results. There is a pressing need for innovative diagnostic tools that enhance traditional methods, streamline processes, and ensure accurate and consistent disease detection.

METHODS

In this study, we present an innovative Convolutional Neural Networks-Vision Transformer (CVT) model leveraging Transformer technology to refine glomerular disease diagnosis by fusing spectral and spatial data, surpassing traditional diagnostic limitations. Using interval sampling, preprocessing, and wavelength optimization, we also introduced the Gramian Angular Field (GAF) method for a unified representation of spectral and spatial characteristics.

RESULTS

We captured hyperspectral images ranging from 385.18 nm to 1009.47 nm and employed various methods to extract sample features. Initial models based solely on spectral features achieved a accuracy of 85.24 %. However, the CVT model significantly outperformed these, achieving an average accuracy of 94 %. This demonstrates the model's superior capability in utilizing sample data and learning joint feature representations.

CONCLUSIONS

The CVT model not only breaks through the limitations of existing diagnostic techniques but also showcases the vast potential of non-invasive, high-precision diagnostic technology in supporting the classification and prognosis of complex glomerular diseases. This innovative approach could significantly impact future diagnostic strategies in renal disease research.

CONCISE ABSTRACT

This study introduces a transformative hyperspectral image classification model leveraging a Transformer to significantly improve glomerular disease diagnosis accuracy by synergizing spectral and spatial data, surpassing conventional methods. Through a rigorous comparative analysis, it was determined that while spectral features alone reached a peak accuracy of 85.24 %, the novel Convolutional Neural Network-Transformer (CVT) model's integration of spatial-spectral features via the Gramian Angular Field (GAF) method markedly enhanced diagnostic precision, achieving an average accuracy of 94 %. This methodological innovation not only overcomes traditional diagnostic limitations but also underscores the potential of non-invasive, high-precision technologies in advancing the classification and prognosis of complex renal diseases, setting a new benchmark in the field.

Prognostic value of liver stiffness in patients hospitalized for acute decompensated heart failure: a meta-analysis

PURPOSE

Heart failure (HF) is a major health problem affecting millions of people worldwide. In the latest years, many efforts have been made to identify predictors of poor prognosis in these patients. The aim of this systematic review and meta-analysis was to enlighten the correlation between liver stiffness (LS), assessed by Shear Wave Elastography techniques, and HF, particularly focusing on the prognostic value of LS on cardiovascular outcomes.

METHODS

We searched the PUBMED databases (up to May 1st, 2023) for studies that enlightened the correlation between LS and cardiovascular outcomes in patients hospitalized for acute decompensated heart failure (ADHF). We performed a meta-analysis to estimate the efficacy of LS in predicting the prognosis of patients with ADHF.

RESULTS

We analyzed data from 7 studies, comprising 677 patients, that assessed the prognostic value of LS in predicting cardiovascular outcomes in patients hospitalized for ADHF. The pooled analysis showed that increased liver stiffness was associated with higher risk of adverse cardiac events (hazard ratio 1.07 [1.03, 1.12], 95% CI).

CONCLUSION

Increased LS is associated with poor prognosis in patients hospitalized for HF and might help effectively identify those patients at high risk for worse outcomes.

PneumaOCT: Pneumatic optical coherence tomography endoscopy for targeted distortion-free imaging in tortuous and narrow internal lumens

The complex anatomy of internal luminal organs, like bronchioles, poses challenges for endoscopic optical coherence tomography (OCT). These challenges include limited steerability for targeted imaging and nonuniform rotation distortion (NURD) with proximal scanning. Using rotary micromotors for distal scanning could address NURD but raises concerns about electrical safety and costs. We present pneumaOCT, the first pneumatic OCT endoscope, comprising a steerable catheter with a soft pneumatic actuator and an imaging probe with a miniature pneumatic turbine. With a diameter of 2.8 mm, pneumaOCT allows for a bending angle of up to 237°, facilitating navigation through narrow turns. The pneumatic turbine enables adjustable imaging speeds from 51 to 446 revolutions per second. We demonstrate the pneumaOCT in vivo imaging of mouse esophagus and colon, as well as targeted and distortion-free imaging of peripheral bronchioles in a bronchial phantom and a porcine lung. This advancement substantially improves endoscopic OCT for navigational imaging in curved and narrow lumens.

Systematic literature review to inform the EULAR recommendations for the use of imaging in crystal-induced arthropathies in clinical practice

OBJECTIVE

To summarise current data regarding the use of imaging in crystal-induced arthropathies (CiAs) informing a European Alliance of Associations for Rheumatology task force.

METHODS

We performed four systematic searches in Embase, Medline and Central on imaging for diagnosis, monitoring, prediction of disease severity/treatment response, guiding procedures and patient education in gout, calcium pyrophosphate dihydrate deposition (CPPD) and basic calcium phosphate deposition (BCPD). Records were screened, manuscripts reviewed and data of the included studies extracted. The risk of bias was assessed by validated instruments.

RESULTS

For gout, 88 studies were included. Diagnostic studies reported good to excellent sensitivity and specificity of dual-energy CT (DECT) and ultrasound (US), high specificity and lower sensitivity for conventional radiographs (CR) and CT. Longitudinal studies demonstrated sensitivity to change with regard to crystal deposition by US and DECT and inflammation by US and structural progression by CR and CT. For CPPD, 50 studies were included. Diagnostic studies on CR and US showed high specificity and variable sensitivity. There was a single study on monitoring, while nine assessed the prediction in CPPD. For BCPD, 56 studies were included. There were two diagnostic studies, while monitoring by CR and US was assessed in 43 studies, showing a reduction in crystal deposition. A total of 12 studies with inconsistent results assessed the prediction of treatment response. The search on patient education retrieved two studies, suggesting a potential role of DECT.

CONCLUSION

This SLR confirmed a relevant and increasing role of imaging in the field of CiAs.

RAC1 inhibition ameliorates IBSP-induced bone metastasis in lung adenocarcinoma

Macrophage-to-osteoclast differentiation (osteoclastogenesis) plays an essential role in tumor osteolytic bone metastasis (BM), while its specific mechanisms remain largely uncertain in lung adenocarcinoma BM. In this study, we demonstrate that integrin-binding sialoprotein (IBSP), which is highly expressed in the cancer cells from bone metastatic and primary lesions of patients with lung adenocarcinoma, can facilitate BM and directly promote macrophage-to-osteoclast differentiation independent of RANKL/M-CSF. In vivo results further suggest that osteolytic BM in lung cancer specifically relies on IBSP-induced macrophage-to-osteoclast differentiation. Mechanistically, IBSP regulates the Rac family small GTPase 1 (Rac1)-NFAT signaling pathway and mediates the forward shift of macrophage-to-osteoclast differentiation, thereby leading to early osteolysis. Moreover, inhibition of Rac1 by EHT-1864 or azathioprine in mice models can remarkably alleviate IBSP-induced BM of lung cancer. Overall, our study suggests that tumor-secreted IBSP promotes BM by inducing macrophage-to-osteoclast differentiation, with potential as an early diagnostic maker for BM, and Rac1 can be the therapeutic target for IBSP-promoted BM in lung cancer.

Relationships between serotonin 1A receptor DNA methylation, self-reported history of childhood abuse and gray matter volume in major depression

BACKGROUND

Early life adversity is a risk factor for psychopathology and is associated with epigenetic alterations in the 5-HT1A receptor gene promoter. The 5-HT1A receptor mediates neurotrophic effects, which could affect brain structure and function. We examined relationships between self-reported early childhood abuse, 5-HT1A receptor promoter DNA methylation, and gray matter volume (GMV) in Major Depressive Disorder (MDD).

METHODS

Peripheral DNA methylation of 5-HT1A receptor promoter CpG sites -681 and -1007 was assayed in 50 individuals with MDD, including 18 with a history of childhood abuse. T1-weighted structural magnetic resonance imaging (MRI) was performed. Voxel-based morphometry (VBM) was quantified in amygdala, hippocampus, insula, occipital lobe, orbitofrontal cortex, temporal lobe, parietal lobe, and at the voxel level.

RESULTS

No relationship was observed between DNA methylation and history of childhood abuse. We observed regional heterogeneity comparing -681 CpG site methylation and GMV (p = 0.014), with a positive relationship to GMV in orbitofrontal cortex (p = 0.035). Childhood abuse history was associated with higher GMV considering all ROIs simultaneously (p < 0.01). In whole-brain analyses, childhood abuse history was positively correlated with GMV in multiple clusters, including insula and orbitofrontal cortex (pFWE = 0.005), and negatively in intracalcarine cortex (pFWE = 0.001).

LIMITATIONS

Small sample size, childhood trauma assessment instrument used, and assay of peripheral, rather than CNS, methylation.

CONCLUSIONS

These cross-sectional findings support hypotheses of 5-HT1A receptor-related neurotrophic effects, and of increased regional GMV as a potential regulatory mechanism in the setting of childhood abuse. Orbitofrontal cortex was uniquely associated with both childhood abuse history and 5-HT1A receptor methylation.

Predicting tumor invasion depth in gastric cancer: developing and validating multivariate models incorporating preoperative IVIM-DWI parameters and MRI morphological characteristics

INTRODUCTION

Accurate assessment of the depth of tumor invasion in gastric cancer (GC) is vital for the selection of suitable patients for neoadjuvant chemotherapy (NAC). Current problem is that preoperative differentiation between T1-2 and T3-4 stage cases in GC is always highly challenging for radiologists.

METHODS

A total of 129 GC patients were divided into training (91 cases) and validation (38 cases) cohorts. Pathology from surgical specimens categorized patients into T1-2 and T3-4 stages. IVIM-DWI and MRI morphological characteristics were evaluated, and a multimodal nomogram was developed. The MRI morphological model, IVIM-DWI model, and combined model were constructed using logistic regression. Their effectiveness was assessed using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).

RESULTS

The combined nomogram, integrating preoperative IVIM-DWI parameters (D value) and MRI morphological characteristics (maximum tumor thickness, extra-serosal invasion), achieved the highest area under the curve (AUC) values of 0.901 and 0.883 in the training and validation cohorts, respectively. No significant difference was observed between the AUCs of the IVIM-DWI and MRI morphological models in either cohort (training: 0.796 vs. 0.835, p = 0.593; validation: 0.794 vs. 0.766, p = 0.79).

CONCLUSION

The multimodal nomogram, combining IVIM-DWI parameters and MRI morphological characteristics, emerges as a promising tool for assessing tumor invasion depth in GC, potentially guiding the selection of suitable candidates for neoadjuvant chemotherapy (NAC) treatment.

Hybrid optimal feature selection-based iterative deep convolution learning for COVID-19 classification system

The COVID-19 pandemic has necessitated the development of innovative and efficient methods for early detection and diagnosis. Integrating Internet of Things (IoT) devices and applications in healthcare has facilitated various functions. This work aims to employ practical artificial intelligence (AI) approaches to extract meaningful information from the vast amount of IoT data to perform disease prediction tasks. However, traditional AI methods need help in feature analysis due to the complexity and scale of IoT data. So, this work implements the optimal iterative COVID-19 classification network (OICC-Net) using machine learning optimization and deep learning approaches. Initially, the preprocessing operation normalizes the dataset with uniform values. Here, random forest infused particle swarm-based black widow optimization (RFI-PS-BWO) algorithm was used to get the disease-specific patterns from SARS-CoV-2 (SC2), and other disease classes, where patterns of the SC2 virus are very similar to those of other virus classes. In addition, an iterative deep convolution learning (IDCL) feature selection method is used to distinguish features from the RFI-PS-BWO data. This iterative process enhances the performance of feature selection by providing improved representation and reducing the dimensionality of the input data. Then, a one-dimensional convolutional neural network (1D-CNN) was employed to classify and identify the extracted features from SC2 with no virus classes. The 1D-CNN model is trained using a large dataset of COVID-19 samples, enabling it to learn intricate patterns and make accurate predictions. It was tested and found that the proposed OICC-Net system is more accurate than current methods, with a score of 99.97 % for F1-score, 100 % for sensitivity, 100 % for specificity, 99.98 % for precision, and 99.99 % for recall.

Dexamethasone and N-acetylcysteine before transarterial chemoembolization in hepatocellular carcinoma: A Western perspective

Post-embolization syndrome (PES) is the most common complication in patients with hepatocellular carcinoma treated with transarterial chemoembolization. Many strategies have been evaluated to reduce the incidence of PES, but no standard prevention guidelines currently exist. In a single-center, placebo-controlled trial, Simasingha et al evaluated the prophylactic administration of a combination of dexamethasone and N-acetylcysteine and documented a significant reduction in the incidence of PES (from 80% to 6%), of post-procedural liver decompensation (from 14% to 0%), and a shorter hospital stay (4 days vs 6 days), alongside an acceptable safety profile. The results of this study raise several controversial points regarding their applicability in the Western world. In the West, there is a greater and increasing prevalence of metabolic and alcoholic etiologies of liver cirrhosis, so a not negligible number of patients with type II diabetes or hypertension would be excluded from high-dosage dexamethasone prophylaxis. Furthermore, in the West, there is a preferred use of drug-eluting beads loaded with doxorubicin, which are associated with a lower incidence of PES. A study on prophylaxis with dexamethasone and/or N-acetylcysteine in a Western population is hopefully awaited.

Deep learning assisted segmentation of the lumbar intervertebral disc: a systematic review and meta-analysis

BACKGROUND

In recent years, deep learning (DL) technology has been increasingly used for the diagnosis and treatment of lumbar intervertebral disc (IVD) degeneration. This study aims to evaluate the performance of DL technology for IVD segmentation in magnetic resonance (MR) images and explore improvement strategies.

METHODS

We developed a PRISMA systematic review protocol and systematically reviewed studies that used DL algorithm frameworks to perform IVD segmentation based on MR images published up to April 10, 2024. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess methodological quality, and the pooled dice similarity coefficient (DSC) score and Intersection over Union (IoU) were calculated to evaluate segmentation performance.

RESULTS

45 studies were included in this systematic review, of which 16 provided complete segmentation performance data and were included in the quantitative meta-analysis. The results indicated that DL models showed satisfactory IVD segmentation performance, with a pooled DSC of 0.900 (95% confidence interval [CI]: 0.887-0.914) and IoU of 0.863 (95% CI: 0.730-0.995). However, the subgroup analysis did not show significant effects of factors on IVD segmentation performance, including network dimensionality, algorithm type, publication year, number of patients, scanning direction, data augmentation, and cross-validation.

CONCLUSIONS

This study highlights the potential of DL technology in IVD segmentation and its further applications. However, due to the heterogeneity in algorithm frameworks and result reporting of the included studies, the conclusions should be interpreted with caution. Future research should focus on training generalized models on large-scale datasets to enhance their clinical application.

Application of the 2022 ACR/EULAR criteria for Takayasu arteritis to previously diagnosed patients based on the 1990 ACR criteria

OBJECTIVES

Recently, a joint group of the American College of Rheumatology (ACR) and the European Alliance of Associations for Rheumatology (EULAR) proposed new criteria for Takayasu arteritis (TAK) (the 2022 ACR/EULAR criteria). This study applied the 2022 ACR/EULAR criteria to patients with previously diagnosed TAK based on the 1990 ACR criteria and investigated the concordance rate between the two criteria according to the four imaging modalities.

METHODS

This study reviewed the medical records of 179 patients who met the 1990 ACR criteria for TAK. The imaging modalities included conventional angiography, computed tomography angiography, fluorodeoxyglucose-positron emission tomography, and magnetic resonance angiography.

RESULTS

Regardless of the imaging modalities, the concordance rate between the two criteria was 85.5% when including all patients, whereas it increased to 98.1% when only patients aged ≤60 years were included. Among the four imaging modalities, computed tomography angiography exhibited the highest concordance rate between the two criteria (85.6%). The concordance rate among patients aged >60 years was 95.7%. Only one patient aged 50-60 years was reclassified as having both TAK and giant cell arteritis.

CONCLUSIONS

The concordance rate was 85.5% regardless of the imaging modalities and increased to 86.9% on simultaneous computed tomography angiography and fluorodeoxyglucose-positron emission tomography imaging.

Pressure-enhanced sensing of tissue oxygenation via endogenous porphyrin: Implications for dynamic visualization of cancer in surgery

Fluorescence guidance is routinely used in surgery to enhance perfusion contrast in multiple types of diseases. Pressure-enhanced sensing of tissue oxygenation (PRESTO) via fluorescence is a technique extensively analyzed here, that uses an FDA-approved human precursor molecule, 5-aminolevulinic acid (ALA), to stimulate a unique delayed fluorescence signal that is representative of tissue hypoxia. The ALA precontrast agent is metabolized in most tissues into a red fluorescent molecule, protoporphyrin IX (PpIX), which has both prompt fluorescence, indicative of the concentration, and a delayed fluorescence, that is amplified in low tissue oxygen situations. Applied pressure from palpation induces transient capillary stasis and a resulting transient PRESTO contrast, dominant when there is near hypoxia. This study examined the kinetics and behavior of this effect in both normal and tumor tissues, with a prolonged high PRESTO contrast (contrast to background of 7.3) across 5 tumor models, due to sluggish capillaries and inhibited vasodynamics. This tissue function imaging approach is a fundamentally unique tool for real-time palpation-induced tissue response in vivo, relevant for chronic hypoxia, such as vascular diseases or oncologic surgery.

Advanced lung imaging with photon-counting detectors: Insights from thermoluminescence dosimetry

RATIONALE AND OBJECTIVES

This study investigates the dose burden of photon-counting detector (PCD) lung CT with ultra-high-resolution (UHR) and standard mode using organ-based tube current modulation (OBTCM).

MATERIALS AND METHODS

An anthropomorphic Alderson-Rando phantom was scanned in UHR and standard mode with and without OBTCM on three dose levels (IQ 5, 20, 50). Effective radiation dose was determined by thermoluminescent dosimetry in 13 measurement sites and compared with the calculated effective dose derived from the dose-length product. Image quality was evaluated subjectively by six radiologists using an equidistant 7-point scale and objectively by means of modulation transfer function analysis.

RESULTS

Measured effective radiation exposure was lower in UHR and OBTCM studies than in standard mode (IQ 5: 0.34-0.36, IQ 20: 1.57-1.70, IQ 50: 3.76-3.99 mSv). Compared with the calculated effective dose, the radiation exposure measured with thermoluminescence dosimetry was 131-170% higher. Noise in UHR mode was rated lower than in standard (all p ≤ 0.042) and OBTCM images (all p ≤ 0.028) for all dose levels, while image sharpness was deemed highest for UHR protocols (all p ≤ 0.042). The use of OBTCM had no significant effect on either dimension of subjective image quality (all p ≥ 0.999). Modulation transfer function analysis confirmed the highest spatial frequency in UHR datasets (all p ≤ 0.016).

CONCLUSION

In PCD-CT of the lung, full field-of-view UHR imaging entails no dose disadvantage over standard mode despite superior image quality. OBTCM possesses moderate dose saving potential. Thermoluminescence dosimetry yielded considerably higher effective doses than those calculated from dose-length products.

Transcallosal white matter and cortical gray matter variations in autistic adults ages 30-73 years: A bi-tensor free water imaging approach

Background: Autism spectrum disorder (ASD) has long been recognized as a lifelong condition, but brain aging studies in autistic adults aged >30 years are limited. Free water, a novel brain imaging marker derived from diffusion MRI (dMRI), has shown promise in differentiating typical and pathological aging and monitoring brain degeneration. We aimed to examine free water and free water corrected dMRI measures to assess white and gray matter microstructure and their associations with age in autistic adults. Methods: Forty-three autistic adults ages 30-73 years and 43 age, sex, and IQ matched neurotypical controls participated in this cross-sectional study. We quantified fractional anisotropy (FA), free water, and free water-corrected FA (fwcFA) across 32 transcallosal white matter tracts and 94 gray matter areas in autistic adults and neurotypical controls. Follow-up analyses assessed age effect on dMRI metrics of the whole brain for both groups and the relationship between dMRI metrics and clinical measures of ASD in regions that significantly differentiated autistic adults from controls. Results: We found globally elevated free water in 24 transcallosal tracts in autistic adults. We identified negligible differences in dMRI metrics in gray matter between the two groups. Age-associated FA reductions and free water increases were featured in neurotypical controls; however, this brain aging profile was largely absent in autistic adults. Additionally, greater autism quotient (AQ) total raw score was associated with increased free water in the inferior frontal gyrus pars orbitalis and lateral orbital gyrus in autistic adults. Limitations: All autistic adults were cognitively capable individuals, minimizing the generalizability of the research findings across the spectrum. This study also involved a cross-sectional design, which limited inferences about the longitudinal microstructural changes of white and gray matter in ASD. Conclusions: We identified differential microstructural configurations between white and gray matter in autistic adults and that autistic individuals present more heterogeneous brain aging profiles compared to controls. Our clinical correlation analysis offered new evidence that elevated free water in some localized white matter tracts may critically contribute to autistic traits in ASD. Our findings underscored the importance of quantifying free water in dMRI studies of ASD.

EAAC-Net: An Efficient Adaptive Attention and Convolution Fusion Network for Skin Lesion Segmentation

Accurate segmentation of skin lesions in dermoscopic images is of key importance for quantitative analysis of melanoma. Although existing medical image segmentation methods significantly improve skin lesion segmentation, they still have limitations in extracting local features with global information, do not handle challenging lesions well, and usually have a large number of parameters and high computational complexity. To address these issues, this paper proposes an efficient adaptive attention and convolutional fusion network for skin lesion segmentation (EAAC-Net). We designed two parallel encoders, where the efficient adaptive attention feature extraction module (EAAM) adaptively establishes global spatial dependence and global channel dependence by constructing the adjacency matrix of the directed graph and can adaptively filter out the least relevant tokens at the coarse-grained region level, thus reducing the computational complexity of the self-attention mechanism. The efficient multiscale attention-based convolution module (EMA⋅C) utilizes multiscale attention for cross-space learning of local features extracted from the convolutional layer to enhance the representation of richly detailed local features. In addition, we designed a reverse attention feature fusion module (RAFM) to enhance the effective boundary information gradually. To validate the performance of our proposed network, we compared it with other methods on ISIC 2016, ISIC 2018, and PH2 public datasets, and the experimental results show that EAAC-Net has superior segmentation performance under commonly used evaluation metrics.

A comprehensive overview of diffuse correlation spectroscopy: Theoretical framework, recent advances in hardware, analysis, and applications

Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already-complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry to this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, sensors, and correlators), as well as data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed and an outlook for the future directions is provided, to offer effective guidance to embark on DCS research.

Segmentation of ovarian cyst in ultrasound images using AdaResU-net with optimization algorithm and deep learning model

Ovarian cysts pose significant health risks including torsion, infertility, and cancer, necessitating rapid and accurate diagnosis. Ultrasonography is commonly employed for screening, yet its effectiveness is hindered by challenges like weak contrast, speckle noise, and hazy boundaries in images. This study proposes an adaptive deep learning-based segmentation technique using a database of ovarian ultrasound cyst images. A Guided Trilateral Filter (GTF) is applied for noise reduction in pre-processing. Segmentation utilizes an Adaptive Convolutional Neural Network (AdaResU-net) for precise cyst size identification and benign/malignant classification, optimized via the Wild Horse Optimization (WHO) algorithm. Objective functions Dice Loss Coefficient and Weighted Cross-Entropy are optimized to enhance segmentation accuracy. Classification of cyst types is performed using a Pyramidal Dilated Convolutional (PDC) network. The method achieves a segmentation accuracy of 98.87%, surpassing existing techniques, thereby promising improved diagnostic accuracy and patient care outcomes.

Methodological considerations on diffusion MRI tractography in infants aged 0-2 years: a scoping review

Diffusion MRI (dMRI) enables studying the complex architectural organization of the brain's white matter (WM) through virtual reconstruction of WM fiber tracts (tractography). Despite the anticipated clinical importance of applying tractography to study structural connectivity and tract development during the critical period of rapid infant brain maturation, detailed descriptions on how to approach tractography in young infants are limited. Over the past two decades, tractography from infant dMRI has mainly been applied in research settings and focused on diffusion tensor imaging (DTI). Only few studies used techniques superior to DTI in terms of disentangling information on the brain's organizational complexity, including crossing fibers. While more advanced techniques may enhance our understanding of the intricate processes of normal and abnormal brain development and extensive knowledge has been gained from application on adult scans, their applicability in infants has remained underexplored. This may partially be due to the higher technical requirements versus the need to limit scan time in young infants. We review various previously described methodological practices for tractography in the infant brain (0-2 years-of-age) and provide recommendations to optimize advanced tractography approaches to enable more accurate reconstructions of the brain WM's complexity. IMPACT: Diffusion tensor imaging is the technique most frequently used for fiber tracking in the developing infant brain but is limited in capability to disentangle the complex white matter organization. Advanced tractography techniques allow for reconstruction of crossing fiber bundles to better reflect the brain's complex organization. Yet, they pose practical and technical challenges in the fast developing young infant's brain. Methods on how to approach advanced tractography in the young infant's brain have hardly been described. Based on a literature review, recommendations are provided to optimize tractography for the developing infant brain, aiming to advance early diagnosis and neuroprotective strategies.

Dual-Modal Aptasensor for Sensitive Detection of Non-Small Cell Lung Cancer Exosomes Utilizing Two-Dimensional Nanopaper Co@g-C3N4@PB

Nonsmall cell lung cancer (NSCLC), due to its lack of early symptoms, has become one of the leading causes of cancer-related deaths globally. Exosomes, small membrane vesicles secreted by cells, are widely present in human bodily fluids. In the bodily fluids of NSCLC patients, the quantity of extracellular vesicles is double that of healthy individuals, suggesting their potential as biomarkers for screening NSCLC. This study designed a dual-modal aptasensor that integrated excellent sensitivity in electrochemical detection and portability in fluorescence detection into one device. AuNPs were functionalized with exosome-capturing probes containing thiol-modified CD63 aptamers, which were immobilized on screen-printed gold electrodes. On the other hand, the carboxylated CD63 aptamer was immobilized on the surface of PB-modified g-C3N4 loaded with Co-SANs particles (Co@g-C3N4@PB). By combining these components, a sandwich structure (AuNPs/Apt1/Exo/Apt2- Co@g-C3N4@PB) was constructed, forming a probe for specific exosome recognition. First, the samples were preliminarily assessed for their positive or negative status under a fluorescence inverted microscope. Subsequently, a more in-depth quantitative analysis was conducted on suspected positive samples using electrochemical or fluorescence analysis methods. The detection limits for electrochemical analysis and fluorescence analysis were 66.68 and 33.5particles/mL, respectively. In the analysis of clinical serum exosome samples, the developed dual-modal aptasensor effectively distinguished serum specimens from those of NSCLC patients and healthy volunteers. This highlighted the inspection capability of the dual-modal adapter sensor, especially in point-of-care testing, making it a highly suitable tool for clinical applications.

Evaluation of left ventricular ejection fraction by a new automatic tool on a pocket ultrasound device: Concordance study with cardiac magnetic resonance imaging

INTRODUCTION

Assessment of left ventricular ejection fraction (LVEF) is one of the primary objectives of echocardiography. The gold standard assessment technique in emergency medicine is eyeballing. A new tool is now available on pocket ultrasound devices (PUD): automatic LVEF. The primary aim of this study was to evaluate the concordance between LVEF values estimated by automatic LVEF with PUD and by cardiac magnetic resonance imaging (MRI).

MATERIALS

This was a prospective, monocentric, and observational study. All adult patients with an indication for cardiac MRI underwent a point-of-care ultrasound. Blinded to the MRI results, the emergency physician assessed LVEF using the automatic PUD tool and by visual evaluation.

RESULTS

Sixty patients were included and analyzed. Visual estimation of LVEF was feasible for all patients and automatic evaluation for 52 (87%) patients. Lin's concordance correlation coefficient between automatic ejection fraction with PUD and by cardiac MRI was 0.23 (95% CI, 0.03-0.40).

CONCLUSION

Concordance between LVEF estimated by the automatic ejection fraction with PUD and LVEF estimated by MRI was non-existent.

Intelligent Diagnosis of Kawasaki Disease From Real-World Data Using Interpretable Machine Learning Models

OBJECTIVE

This study aimed to leverage real-world electronic medical record (EMR) data to develop interpretable machine learning models for diagnosis of Kawasaki disease, while also exploring and prioritizing the significant risk factors.

METHODS

A comprehensive study was conducted on 4,087 pediatric patients at the Children's Hospital of Chongqing, China. The study collected demographic data, physical examination results, and laboratory findings. Statistical analyses were performed using SPSS 26.0. The optimal feature subset was employed to develop intelligent diagnostic prediction models based on the Light Gradient Boosting Machine (LGBM), Explainable Boosting Machine (EBM), Gradient Boosting Classifier (GBC), Fast Interpretable Greedy-Tree Sums (FIGS), Decision Tree (DT), AdaBoost Classifier (AdaBoost), and Logistic Regression (LR). Model performance was evaluated in three dimensions: discriminative ability via Receiver Operating Characteristic curves, calibration accuracy using calibration curves, and interpretability through Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME).

RESULTS

In this study, Kawasaki disease was diagnosed in 2,971 participants. Analysis was conducted on 31 indicators, including red blood cell distribution width and erythrocyte sedimentation rate. The EBM model demonstrated superior performance compared to other models, with an Area Under the Curve (AUC) of 0.97, second only to the GBC model. Furthermore, the EBM model exhibited the highest calibration accuracy and maintained its interpretability without relying on external analytical tools like SHAP and LIME, thus reducing interpretation biases. Platelet distribution width, total protein, and erythrocyte sedimentation rate were identified by the model as significant predictors for the diagnosis of Kawasaki disease.

CONCLUSIONS

This study employed diverse machine learning models for early diagnosis of Kawasaki disease. The findings demonstrated that interpretable models, like EBM, outperformed traditional machine learning models in terms of both interpretability and performance. Ensuring consistency between predictive models and clinical evidence is crucial for the successful integration of artificial intelligence into real-world clinical practice.

The role of blockchain to secure internet of medical things

This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain's transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is essential to follow standards like HIPAA, and blockchain technology can help ensure these criteria are met. Furthermore, the study explores the potential benefits of blockchain technology for enhancing inter-system communication in the healthcare industry while maintaining patient privacy protection. The results highlight the effectiveness of blockchain's consistency and cryptographic techniques in combining identity management and healthcare data protection, protecting patient privacy and data integrity. Blockchain is an unchangeable distributed ledger system. In short, the paper provides important insights into how blockchain technology may transform the healthcare industry by effectively addressing significant challenges and generating legal, safe, and interoperable solutions. Researchers, doctors, and graduate students are the audience for our paper.

Solitary fibrous tumor of the pineal gland: a case report and review of the literature

Solitary fibrous tumor (SFT) is a type of fibroblastic neoplasm that can occur in various parts of the body, with SFT of the pineal gland being exceedingly rare. We report the case of a 58-year-old male presenting with recurrent hiccups, acid reflux, and headache. Magnetic resonance imaging revealed an occupying lesion in the pineal region, suggestive of a neoplastic process. Intraoperatively, the lesion was located in the pineal region, exhibiting a grayish-red color, and was largely resected. Pathological examination confirmed the diagnosis of solitary fibrous tumor (CNS WHO Grade 1). Postoperatively, the patient was supplemented with radiotherapy, and long-term follow-up showed no signs of recurrence or metastasis.

Combining Image similarity and Predictive AI Models to Decrease Subjectivity in Thyroid Nodule Diagnosis and Improve Malignancy Prediction

OBJECTIVES

To evaluate the efficacy of combining predictive artificial intelligence (AI) and image similarity model to risk stratify thyroid nodules, using retrospective external validation study.

METHODS

Two datasets were used to determine efficacy of the AI application. One was Stanford dataset ultrasound images of 192 nodules between April 2017 to May 2018 and the second was private practice consisting of 118 thyroid nodule images between January 2018 to December 2023. The nodules had definitive diagnosis by cytology or surgical pathology. The AI application was used to predict the diagnosis and American College of Radiology Thyroid Imaging and Data System (ACR TI-RADS) score.

RESULTS

In the Stanford dataset, the AI application predicted malignancies with sensitivity of 1.0 and specificity of 0.55. Positive predictive value (PPV) was 0.18 and negative predictive value (NPV) was 1.0. The Area Under the Curve - Receiver Operating Characteristic (AUC-ROC) was 0.78. ACR TI-RADS based clinical recommendation had a polychoric correlation of 0.67. In the private dataset, the AI application predicted malignancies with sensitivity of 0.91 and specificity of 0.95. PPV was 0.8 and NPV was 0.98. AUC-ROC was 0.93 and accuracy was 0.94. ACR TI-RADS based score had a polychoric correlation of 0.94.

CONCLUSION

The AI application showed good performance for sensitivity and NPV between the two datasets and demonstrated potential for 61.5% reduction in the need for fine needle aspiration (FNA) and strong correlation to ACR TI-RADS. However, PPV was variable between the datasets possibly from variability in image selection and prevalence of malignancy. If implemented widely and consistently among various clinical settings, this could lead to decreased patient burden associated with an invasive procedure and possibly to decreased health care spending.

Enhancing Retina Images by Lowpass Filtering Using Binomial Filter

This study presents a method to enhance the contrast and luminosity of fundus images with boundary reflection. In this work, 100 retina images taken from online databases are utilized to test the performance of the proposed method. First, the red, green and blue channels are read and stored in separate arrays. Then, the area of the eye also called the region of interest (ROI) is located by thresholding. Next, the ratios of R to G and B to G at every pixel in the ROI are calculated and stored along with copies of the R, G and B channels. Then, the RGB channels are subjected to average filtering using a 3 × 3 mask to smoothen the RGB values of pixels, especially along the border of the ROI. In the background brightness estimation stage, the ROI of the three channels is filtered by binomial filters (BFs). This step creates a background brightness (BB) surface of the eye region by levelling the foreground objects like blood vessels, fundi, optic discs and blood spots, thus allowing the estimation of the background illumination. In the next stage, using the BB, the luminosity of the ROI is equalized so that all pixels will have the same background brightness. This is followed by a contrast adjustment of the ROI using CLAHE. Afterward, details of the adjusted green channel are enhanced using information from the adjusted red and blue channels. In the color correction stage, the intensities of pixels in the red and blue channels are adjusted according to their original ratios to the green channel before the three channels are reunited. The resulting color image resembles the original one in color distribution and tone but shows marked improvement in luminosity and contrast. The effectiveness of the approach is tested on the test images and enhancement is noticeable visually and quantitatively in greyscale and color. On average, this method manages to increase the contrast and luminosity of the images. The proposed method was implemented using MATLAB R2021b on an AMD 5900HS processor and the average execution time was less than 10 s. The performance of the filter is compared to those of two other filters and it shows better results. This technique can be a useful tool for ophthalmologists who perform diagnoses on the eyes of diabetic patients.

Automatic diagnosis for adenomyosis in ultrasound images by deep neural networks

OBJECTIVE

To present a new noninvasive technique for automatic diagnosis of adenomyosis, using a novel end-to-end unified network framework based on transformer networks.

STUDY DESIGN

This is a prospective descriptive study conducted at a university hospital.1654 patients were recruited to the study according to adenomyosis diagnosed by transvaginal ultrasound (TVS). For adenomyosis characteristics and ultrasound images, automatic identification of adenomyosis were performed based on deep learning methods. We called this unique technique A2DNet: Adenomyosis Auto Diagnosis Network.

RESULTS

The A2DNet exhibits excellent performance in diagnosis of adenomyosis, achieving an accuracy of 92.33%, a precision of 96.06%, a recall of 91.71% and an F1 score of 93.80% in the test group. The confusion matrix of experimental results show that the A2DNet can achieve a correct diagnosis rate of 92% or more for both normal and adenomyosis samples, which demonstrate the superiority of the A2DNet comparing with the state-of-the-arts.

CONCLUSION

The A2DNet is a safe and effective technique to aid in automatic diagnosis of adenomyosis. The technique which is nondestructive and non-invasive, is new and unique due to the advantages of artificial intelligence.

Geometric and dosimetric evaluation for breast and regional nodal auto-segmentation structures

The accuracy of artificial intelligence (AI) generated contours for intact-breast and post-mastectomy radiotherapy plans was evaluated. Geometric and dosimetric comparisons were performed between auto-contours (ACs) and manual-contours (MCs) produced by physicians for target structures. Breast and regional nodal structures were manually delineated on 66 breast cancer patients. ACs were retrospectively generated. The characteristics of the breast/post-mastectomy chestwall (CW) and regional nodal structures (axillary [AxN], supraclavicular [SC], internal mammary [IM]) were geometrically evaluated by Dice similarity coefficient (DSC), mean surface distance, and Hausdorff Distance. The structures were also evaluated dosimetrically by superimposing the MC clinically delivered plans onto the ACs to assess the impact of utilizing ACs with target dose (Vx%) evaluation. Positive geometric correlations between volume and DSC for intact-breast, AxN, and CW were observed. Little or anti correlations between volume and DSC for IM and SC were shown. For intact-breast plans, insignificant dosimetric differences between ACs and MCs were observed for AxNV95% (p = 0.17) and SCV95% (p = 0.16), while IMNV90% ACs and MCs were significantly different. The average V95% for intact-breast MCs (98.4%) and ACs (97.1%) were comparable but statistically different (p = 0.02). For post-mastectomy plans, AxNV95% (p = 0.35) and SCV95% (p = 0.08) were consistent between ACs and MCs, while IMNV90% was significantly different. Additionally, 94.1% of AC-breasts met ΔV95% variation <5% when DSC > 0.7. However, only 62.5% AC-CWs achieved the same metrics, despite AC-CWV95% (p = 0.43) being statistically insignificant. The AC intact-breast structure was dosimetrically similar to MCs. The AC AxN and SC may require manual adjustments. Careful review should be performed for AC post-mastectomy CW and IMN before treatment planning. The findings of this study may guide the clinical decision-making process for the utilization of AI-driven ACs for intact-breast and post-mastectomy plans. Before clinical implementation of this auto-segmentation software, an in-depth assessment of agreement with each local facilities MCs is needed.

Bowel obstruction by sigmoid strangulation as complication after laparoscopic adjustable gastric banding

BACKGROUND

Laparoscopic adjustable gastric banding (LAGB) has been widely performed in the past, however, given its limited weight loss compared to some other procedures, this technique is less often used. Furthermore, a number of complications leading to band removal have been reported in the past few years.

METHODS

We present a late acute bowel obstruction by sigmoid strangulation in a female patient with the LAGB performed 15 years ago.

RESULTS

The laparoscopic exploration displayed a post-LAGB intestinal strangulation of the sigmoid loop which was caused by the connecting tube. Since the bowel was still viable, the tube was cut and the obstruction has been successfully resolved. The patient was discharged three days after the surgery.

CONCLUSION

Although less often performed, knowledge of LAGB complications can be of importance. We believe that, the present strangulation of the sigmoid by the LAGB tubing is the world's first ever case reported. Nevertheless, when it is still proposed to selected patients, an adequate length of the intra-abdominal tubing could reduce the risk of loop formation and prevent this kind of obstruction by internal hernia.

Pancreatic cancer and sarcopenia: a narrative review of the current status

Pancreatic cancer is still a difficult disease to treat, despite recent advances in surgical techniques and chemotherapeutic drugs. Its incidence continues to rise, as does the number of older patients. Sarcopenia is defined as a progressive and generalized loss of skeletal muscle mass and strength. Sarcopenia is present in approximately 40% in patients with pancreatic cancer. Sarcopenia is primarily diagnosed through imaging, and progress is being made in the development of automated methods and artificial intelligence, as well as biomarker research. Sarcopenia has been linked to a poor prognosis in pancreatic cancer patients. However, some studies suggest that sarcopenia is not always associated with a poor prognosis, depending on the resectability of pancreatic cancer and the nature of treatment, such as surgery or chemotherapy. Recent meta-analyses have found that sarcopenia is not linked to postoperative complications. It is still debated whether there is a link between sarcopenia and drug toxicity during chemotherapy. The relationship between sarcopenia and immunity has been investigated, but the mechanism is still unknown.

Differences between Alzheimer's disease and mild cognitive impairment using brain networks from magnetic resonance texture analysis

Several studies have aimed at identifying biomarkers in the initial phases of Alzheimer's disease (AD). Conversely, texture features, such as those from gray-level co-occurrence matrices (GLCMs), have highlighted important information from several types of medical images. More recently, texture-based brain networks have been shown to provide useful information in characterizing healthy individuals. However, no studies have yet explored the use of this type of network in the context of AD. This work aimed to employ texture brain networks to investigate the distinction between groups of patients with amnestic mild cognitive impairment (aMCI) and mild dementia due to AD, and a group of healthy subjects. Magnetic resonance (MR) images from the three groups acquired at two instances were used. Images were segmented and GLCM texture parameters were calculated for each region. Structural brain networks were generated using regions as nodes and the similarity among texture parameters as links, and graph theory was used to compute five network measures. An ANCOVA was performed for each network measure to assess statistical differences between groups. The thalamus showed significant differences between aMCI and AD patients for four network measures for the right hemisphere and one network measure for the left hemisphere. There were also significant differences between controls and AD patients for the left hippocampus, right superior parietal lobule, and right thalamus-one network measure each. These findings represent changes in the texture of these regions which can be associated with the cortical volume and thickness atrophies reported in the literature for AD. The texture networks showed potential to differentiate between aMCI and AD patients, as well as between controls and AD patients, offering a new tool to help understand these conditions and eventually aid early intervention and personalized treatment, thereby improving patient outcomes and advancing AD research.

Imaging of cerebrovascular complications from blunt skull base trauma

Cerebrovascular complications from blunt trauma to the skull base, though rare, can lead to potentially devastating outcomes, emphasizing the importance of timely diagnosis and management. Due to the insidious clinical presentation, subtle nature of imaging findings, and complex anatomy of the skull base, diagnosing cerebrovascular injuries and their complications poses considerable challenges. This article offers a comprehensive review of skull base anatomy and pathophysiology pertinent to recognizing cerebrovascular injuries and their complications, up-to-date screening criteria and imaging techniques for assessing these injuries, and a case-based review of the spectrum of cerebrovascular complications arising from skull base trauma. This review will enhance understanding of cerebrovascular injuries and their complications from blunt skull base trauma to facilitate diagnosis and timely treatment.

The importance and future of prostate MRI report templates: improving oncological care

The radiologist's report is crucial for guiding care post-imaging, with ongoing advancements in report construction. Recent studies across various modalities and organ systems demonstrate enhanced clarity and communication through structured reports. This article will explain the benefits of disease-state specific reporting templates using prostate MRI as the model system. We identify key reporting components for prostate cancer detection and staging as well as imaging in active surveillance and following therapy. We discuss relevant reporting systems including PI-QUAL, PI-RADS, PRECISE, PI-RR and PI-FAB systems. Additionally, we examine optimal reporting structure including disruptive technologies such as graphical reporting and using artificial intelligence to improve report clarity and applicability.