Assessment of magnetic resonance imaging findings in ovarian granulosa cell tumors along with clinical prognostic factors

AIM

To determine the role of preoperative MRI in the diagnosis and treatment of patients with granulosa cell tumors (GCTs) of the ovary.

MATERIALS AND METHODS

Twenty-four patients who were operated on between 2018 and 2022 and who were pathologically diagnosed with GHT and met the inclusion criteria were retrospectively examined. The findings were compared with the patients' demographic data, symptoms, surgical findings (laterality, stage, lymph node involvement, endometrial pathology, tumor size), and CA-125 levels.

RESULTS

The final cohort included 24 patients with a mean age of 54.71 ± 16.52. All the patients had the pathological diagnosis of adult type GCT. In the morphological evaluation, the most common finding was a solid-cystic mixed type (14 patients, 58.3%), while intratumoral hemorrhage signal was observed in 10 patients (41.7%). In the majority of cases (91.7%), the mass showed regular contours. The honeycomb/Swiss cheese sign was detected in 54.2% of the cases. When the T1 and T2 signal of the solid component of the mass were examined relative to the myometrium, the majority of GCTs appeared isointense on both sequences (83.3% and 62.5%, respectively). The mean ADC value of the solid component obtained from diffusion-weighted imaging was 0.78 ± 0.15 × 10-3. Pelvic fluid was observed in 41.7% of the cases. The average endometrial thickness was 9.74 ± 6.43 mm. Thickened endometrium more than 9 mm was observed in 9 out of the remaining 21 patients (42.9%).

CONCLUSION

Understanding the key imaging features for GCTs plays an essential role in the diagnosis and guiding the treatment effectively.

Transformers for colorectal cancer segmentation in CT imaging

PURPOSE

Most recently transformer models became the state of the art in various medical image segmentation tasks and challenges, outperforming most of the conventional deep learning approaches. Picking up on that trend, this study aims at applying various transformer models to the highly challenging task of colorectal cancer (CRC) segmentation in CT imaging and assessing how they hold up to the current state-of-the-art convolutional neural network (CNN), the nnUnet. Furthermore, we wanted to investigate the impact of the network size on the resulting accuracies, since transformer models tend to be significantly larger than conventional network architectures.

METHODS

For this purpose, six different transformer models, with specific architectural advancements and network sizes were implemented alongside the aforementioned nnUnet and were applied to the CRC segmentation task of the medical segmentation decathlon.

RESULTS

The best results were achieved with the Swin-UNETR, D-Former, and VT-Unet, each transformer models, with a Dice similarity coefficient (DSC) of 0.60, 0.59 and 0.59, respectively. Therefore, the current state-of-the-art CNN, the nnUnet could be outperformed by transformer architectures regarding this task. Furthermore, a comparison with the inter-observer variability (IOV) of approx. 0.64 DSC indicates almost expert-level accuracy. The comparatively low IOV emphasizes the complexity and challenge of CRC segmentation, as well as indicating limitations regarding the achievable segmentation accuracy.

CONCLUSION

As a result of this study, transformer models underline their current upward trend in producing state-of-the-art results also for the challenging task of CRC segmentation. However, with ever smaller advances in total accuracies, as demonstrated in this study by the on par performances of multiple network variants, other advantages like efficiency, low computation demands, or ease of adaption to new tasks become more and more relevant.

Considering Context-Specific microRNAs in Ischemic Stroke with Three "W": Where, When, and What

MicroRNAs are short non-coding RNA molecules that function as critical regulators of various biological processes through negative regulation of gene expression post-transcriptionally. Recent studies have indicated that microRNAs are potential biomarkers for ischemic stroke. In this review, we first illustrate the pathogenesis of ischemic stroke and demonstrate the biogenesis and transportation of microRNAs from cells. We then discuss several promising microRNA biomarkers in ischemic stroke in a context-specific manner from three dimensions: biofluids selection for microRNA extraction (Where), the timing of sample collection after ischemic stroke onset (When), and the clinical application of the differential-expressed microRNAs during stroke pathophysiology (What). We show that microRNAs have the utilities in ischemic stroke diagnosis, risk stratification, subtype classification, prognosis prediction, and treatment response monitoring. However, there are also obstacles in microRNA biomarker research, and this review will discuss the possible ways to improve microRNA biomarkers. Overall, microRNAs have the potential to assist clinical treatment, and developing microRNA panels for clinical application is worthwhile.

Predicting outcomes of unruptured intracranial artery dissection with clear symptoms onset using clinical and radiological features

We investigated the clinical and radiologic predictors of unruptured symptomatic intracranial artery dissection (IAD) outcomes. Unruptured symptomatic IAD patients who underwent vessel wall magnetic resonance imaging (VW-MRI) and time-of-flight magnetic resonance angiography (TOF-MRA) within 1 month after symptom onset, followed for over 12 months were included. Baseline features predicting the clinical outcome of recurrent symptoms and radiologic outcomes of aneurysmal dilatation and occlusion were analyzed using logistic regression analysis. The Kaplan-Meier method calculated the median time to morphological stability. Patients with aneurysmal dilatation were categorized into progressive and non-progressive enlargement subgroups. Seventy-three IADs from 65 patients were included. All patients showed benign clinical course (mRS 0-1). No baseline features were predictive of recurrent symptoms. Aneurysmal dilatation was associated with increased outer diameter in baseline VW-MRI (OR, 23.15; 95% CI, 3.78-141.75, P < 0.001) and TOF-MRA (OR, 10.81; 95% CI, 2.16-53.99, P = 0.004). Occlusion was inversely associated with preserved patency in baseline VW-MRI (OR, 0.1; 95% CI, 0.01-0.74, P = 0.024) and TOF-MRA (OR, 0.14; 95% CI, 0.02-0.98; P = 0.048). The median time to morphological stability was 3.9 months (95% CI, 3.16-5.5). While baseline features did not significantly differ between aneurysmal dilatation subgroups, follow-up imaging revealed significant differences in remodeling index, normalized wall index, relative signal intensity of intramural hematoma, and presence of onion-skin appearance and intramural hematoma (all P < 0.05). Our findings suggest that while unruptured IAD presents a benign clinical outcome, follow-up imaging may be necessary to monitor the progressive enlargement of aneurysmal dilatation.

Meta-analysis: Radial Scar and Breast MRI

BACKGROUND

The implementation of digital breast tomosynthesis has increased the detection of radial scar (RS). Managing this finding may be experienced as a clinical dilemma in daily practice. Breast Contrast-Enhanced MRI (CE-BMR) is a known modality in case of problem-solving tool for mammographic abnormalities. However, the data about AD and CE-BMR are scant.

OBJECTIVE

The purpose was to estimate the benefit of CE-BMR in the setting of RS detected mammographically through a systematic review and meta-analysis of the literature.

METHODS

A search of MEDLINE and EMBASE databases were conducted in 2022. Based on the PRISMA guidelines, an analysis was performed. The primary endpoint was the correlation between CE-BMR findings and definite outcome for RS (pure RS versus RS associated with atypia or malignancy).

RESULTS

Three studies were available. The negative predictive value (NPV) was 100% for each.

CONCLUSION

The high NPV could allow for deferral of a biopsy in favor of a short-interval imaging follow-up in the setting of a negative CE-BMR.

Diffusion Tensor Imaging (DTI) Biomarker Alterations in Brain Metastases and Comparable Tumors: A Systematic Review of DTI and Tractography Findings

BACKGROUND

Brain metastases (BMs) are the most frequent tumors of the central nervous system. Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides insights into brain microstructural alterations and tensor metrics and generates tractography to visualize white matter fiber tracts based on diffusion directionality. This systematic review assessed evidence from DTI biomarker alterations in BMs and comparable tumors such as glioblastoma.

METHODS

PubMed, Scopus, and Web of Science were searched, and published between January 2000 and August 2023. The key inclusion criteria were studies reporting DTI metrics in BMs and comparisons with other tumors. Data on study characteristics, tumor types, sample details, and main DTI findings were extracted.

RESULTS

Fifty-seven studies with 1592 BM patients and 1578 comparable brain tumors were included. Peritumoral fractional anisotropy (FA) consistently differentiates BMs from primary brain tumors, whereas intratumoral FA shows limited discriminatory power. Mean diffusivity increased in BMs versus comparators. Intratumoral metrics were less consistent but revealed differences in BM origin. Axial and radial diffusivity have provided insights into the effects of radiation, tumor origin, and infiltration. Axial diffusivity/radial diffusivity differentiated tumor infiltration from vasogenic edema. Tractography revealed anatomical relationships between white matter tracts and BMs. In addition, tractography-guided BM surgery and radiotherapy planning are required. Machine learning models incorporating DTI biomarkers/metrics accurately classified BMs versus comparators and improved diagnostic classification.

CONCLUSIONS

DTI metrics provide noninvasive biomarkers for distinguishing BMs from other tumors and predicting outcomes. Key metrics included peritumoral FA and mean diffusivity.

Fusion model of gray level co-occurrence matrix and convolutional neural network faced for histopathological images

The image recognition of cancer cells plays an important role in diagnosing and treating cancer. Deep learning is suitable for classifying histopathological images and providing auxiliary technology for cancer diagnosis. The convolutional neural network is employed in the classification of histopathological images; however, the model's accuracy may decrease along with the increase in network layers. Extracting appropriate image features is helpful for image classification. In this paper, different features of histopathological images are represented by extracting features of the gray co-occurrence matrix. These features are recombined into a 16 × 16 × 3 matrix to form an artificial image. The original image and the artificial image are fused by summing the softmax output. The histopathological images are divided into the training set, validation set, and testing set. Each training dataset consists of 1500 images, while the validation dataset and test dataset each consist of 500 images. The results indicate that the effectiveness of our fusion model is demonstrated through significant improvements in accuracy, precision, recall, and F1-score, with an average accuracy reaching 99.31%. This approach not only enhances the classification performance of tissue pathology images but also holds promise for advancing computer-aided diagnosis in cancer pathology.

Prevalence of Sarcopenia Determined by Computed Tomography in Pancreatic Cancer: A Systematic Review and Meta-Analysis of Observational Studies

Introduction: Sarcopenia, a condition characterized by a loss of skeletal muscle mass, is increasingly recognized as a significant factor influencing patient outcomes in pancreatic cancer (PC). This systematic review and meta-analysis aimed to estimate the prevalence of sarcopenia in patients with PC using computed tomography and to explore how different measurement methods and cut-off values impact such prevalence. Materials and Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a comprehensive search of PubMed, Web of Science, and EMBASE databases was performed, identifying 48 observational studies involving 9063 patients. Results: The overall pooled prevalence of sarcopenia was 45% (95% CI, 40-50%), but varied significantly by the method used: 47% when measured with the skeletal muscle index and 33% when assessed with the total psoas area. In addition, in studies using SMI, sarcopenia prevalence was 19%, 45%, and 57% for cutoff values <40 cm2/m2, 40-50 cm2/m2, and >50 cm2/m2, respectively. Moreover, the prevalence was higher in patients receiving palliative care (50%) compared to those treated with curative intent (41%). High heterogeneity was observed across all analyses, underscoring the need for standardized criteria in sarcopenia assessment. Conclusions: Our findings highlight the substantial variability in sarcopenia prevalence, which could influence patient outcomes, and stress the importance of consensus in measurement techniques to improve clinical decision making and research comparability.

3D quantitative myocardial perfusion imaging with hyperpolarized HP001(bis-1,1-(hydroxymethyl)-[1-13C]cyclopropane-d8): Application of gradient echo and balanced SSFP sequences

PURPOSE

This study aims to show the viability of conducting three-dimensional (3D) myocardial perfusion quantification covering the entire heart using both GRE and bSSFP sequences with hyperpolarized HP001.

METHODS

A GRE sequence and a bSSFP sequence, both with a stack-of-spirals readout, were designed and applied to three pigs. The images were reconstructed using  13 $$ {}^{13} $$ C coil sensitivity maps measured in a phantom experiment. Perfusion was quantified using a constrained decomposition method, and the estimated rest/stress perfusion values from  13 $$ {}^{13} $$ C GRE/bSSFP and Dynamic contrast-enhanced MRI (DCE-MRI) were individually analyzed through histograms and the mean perfusion values were compared with reference values obtained from PET(  15 $$ {}^{15} $$ O-water). The Myocardial Perfusion Reserve Index (MPRI) was estimated for  13 $$ {}^{13} $$ C GRE/bSSFP and DCE-MRI and compared with the reference values.

RESULTS

Perfusion values, estimated by both DCE and  13 $$ {}^{13} $$ C MRI, were found to be lower than reference values. However, DCE-MRI's estimated perfusion values were closer to the reference values than those obtained from  13 $$ {}^{13} $$ C MRI. In the case of MPRI estimation, the  13 $$ {}^{13} $$ C estimated MPRI values (GRE/bSSFP: 2.3/2.0) more closely align with the literature value (around 3) than the DCE estimated MPRI value (1.6).

CONCLUSION

This study demonstrated the feasibility of 3D whole-heart myocardial perfusion quantification using hyperpolarized HP001 with both GRE and bSSFP sequences. The  13 $$ {}^{13} $$ C perfusion measurements underestimated perfusion values compared to the  15 $$ {}^{15} $$ O PET literature value, while the  13 $$ {}^{13} $$ C estimated MPRI value aligned better with the literature. This preliminary result indicates  13 $$ {}^{13} $$ C imaging may more accurately estimate MPRI values compared to DCE-MRI.

Multi-label deep learning for comprehensive optic nerve head segmentation through data of fundus images

Early diagnosis and continuous monitoring of patients with eye diseases are critical in computer-aided detection (CAD) techniques. Semantic segmentation, a key component in computer vision, enables pixel-level classification and provides detailed information about objects within images. In this study, we present three U-Net models designed for multi-class semantic segmentation, leveraging the U-Net architecture with transfer learning. To generate ground truth for the HRF dataset, we combine two U-Net models, namely MSU-Net and BU-Net, to predict probability maps for the optic disc and cup regions. Binary masks are then derived from these probability maps to extract the optic disc and cup regions from retinal images. The dataset used in this study includes pre-existing blood vessels and manually annotated peripapillary atrophy zones (alpha and beta) provided by expert ophthalmologists. This comprehensive dataset, integrating existing blood vessels and expert-marked peripapillary atrophy zones, fulfills the study's objectives. The effectiveness of the proposed approach is validated by training nine pre-trained models on the HRF dataset comprising 45 retinal images, successfully segmenting the optic disc, cup, blood vessels, and peripapillary atrophy zones (alpha and beta). The results demonstrate 87.7 % pixel accuracy, 87 % Intersection over Union (IoU), 86.9 % F1 Score, 85 % mean IoU (mIoU), and 15 % model loss, significantly contributing to the early diagnosis and monitoring of glaucoma and optic nerve disorders.

Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment

BACKGROUND

Continuous breakthroughs in computational algorithms have positioned AI-based models as some of the most sophisticated technologies in the healthcare system. AI shows dynamic contributions in advancing various medical fields involving data interpretation and monitoring, imaging screening and diagnosis, and treatment response and survival prediction. Despite advances in clinical oncology, more effort must be employed to tailor therapeutic plans based on each patient's unique transcriptomic profile within the precision/personalized oncology frame. Furthermore, the standard analysis method is not compatible with the comprehensive deciphering of significant data streams, thus precluding the prediction of accurate treatment options.

METHODOLOGY

We proposed a novel approach that includes obtaining different tumour tissues and preparing RNA samples for comprehensive transcriptomic interpretation using specifically trained, programmed, and optimized AI-based models for extracting large data volumes, refining, and analyzing them. Next, the transcriptomic results will be scanned against an expansive drug library to predict the response of each target to the tested drugs. The obtained target-drug combination/s will be then validated using in vitro and in vivo experimental models. Finally, the best treatment combination option/s will be introduced to the patient. We also provided a comprehensive review discussing AI models' recent innovations and implementations to aid in molecular diagnosis and treatment planning.

RESULTS

The expected transcriptomic analysis generated by the AI-based algorithms will provide an inclusive genomic profile for each patient, containing statistical and bioinformatics analyses, identification of the dysregulated pathways, detection of the targeted genes, and recognition of molecular biomarkers. Subjecting these results to the prediction and pairing AI-based processes will result in statistical graphs presenting each target's likely response rate to various treatment options. Different in vitro and in vivo investigations will further validate the selection of the target drug/s pairs.

CONCLUSIONS

Leveraging AI models will provide more rigorous manipulation of large-scale datasets on specific cancer care paths. Such a strategy would shape treatment according to each patient's demand, thus fortifying the avenue of personalized/precision medicine. Undoubtedly, this will assist in improving the oncology domain and alleviate the burden of clinicians in the coming decade.

Assessing Quality of Ultrasound Attenuation Coefficient Results for Liver Fat Quantification

BACKGROUND/OBJECTIVES

Algorithms for quantifying liver fat content based on the ultrasound attenuation coefficient (AC) are currently available; however, little is known about whether their accuracy increases by applying quality criteria such as the interquartile range-to-median ratio (IQR/M) or whether the median or average AC value should be used.

METHODS

AC measurements were performed with the Aplio i800 ultrasound system using the attenuation imaging (ATI) algorithm (Canon Medical Systems, Otawara, Tochigi, Japan). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) was the reference standard. The diagnostic performance of the AC median value of 5 measurements (AC-M) was compared to that of AC average value (AC-A) of 5 or 3 acquisitions and different levels of IQR/M for median values or standard deviation/average (SD/A) for average values were also analyzed. Concordance between AC-5M, AC-5A, and AC3A was evaluated with concordance correlation coefficient (CCC).

RESULTS

A total of 182 individuals (94 females; mean age, 51.2y [SD: 15]) were evaluated. A total of 77 (42.3%) individuals had S0 steatosis (MRI-PDFF < 6%), 75 (41.2%) S1 (MRI-PDFF 6-17%), 10 (5.5%) S2 (MRI-PDFF 17.1-22%), and 20 (11%) S3 (MRI-PDFF ≥ 22.1%). Concordance of AC-5A and AC-3A with AC-5M was excellent (CCC: 0.99 and 0.96, respectively). The correlation with MRI-PDFF was almost perfect. Diagnostic accuracy of AC-5M, AC-5A, and AC3A was not significantly affected by different levels of IQR/M or SD/A.

CONCLUSIONS

The accuracy of AC in quantifying liver fat content was not affected by reducing the number of acquisitions (from five to three), by using the mean instead of the median, or by reducing the IQR/M or SD/A to ≤5%.

Metabolic dysfunction-associated steatotic liver disease and cardiovascular risk: a comprehensive review

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed nonalcoholic fatty liver disease (NAFLD), poses a significant global health challenge due to its increasing prevalence and strong association with cardiovascular disease (CVD). This comprehensive review summarizes the current knowledge on the MASLD-CVD relationship, compares analysis of how different terminologies for fatty liver disease affect cardiovascular (CV) risk assessment using different diagnostic criteria, explores the pathophysiological mechanisms connecting MASLD to CVD, the influence of MASLD on traditional CV risk factors, the role of noninvasive imaging techniques and biomarkers in the assessment of CV risk in patients with MASLD, and the implications for clinical management and prevention strategies. By incorporating current research and clinical guidelines, this review provides a comprehensive overview of the complex interplay between MASLD and cardiovascular health.

Effectiveness and safety of rivaroxaban or low-molecular-weight heparin in non-major orthopedic surgery: a meta-analysis of randomized controlled trials

BACKGROUND

Patients undergoing non-major orthopedic surgery often face an increased risk of venous thromboembolism due to the necessity of immobilization postoperatively. Current guidelines commonly recommend the use of low-molecular-weight heparin (LMWH) for prophylaxis, but it is associated with low patient compliance and certain side effects. We conducted a meta-analysis of randomized controlled trials (RCTs) to assess the effectiveness and safety of rivaroxaban or LMWH for thromboprophylaxis following non-major orthopedic surgery.

METHOD

Relevant literature was systematically searched in PubMed, Web of Science, Cochrane Library, and Embase from their inception to October 1, 2023, to evaluate the effectiveness and safety of rivaroxaban or LMWH in RCTs for thromboprophylaxis following non-major orthopedic surgery.

RESULTS

A total of 5 randomized controlled trials involving 5,101 patients were included. There was no statistically significant difference in the preventive effect against venous thromboembolism (VTE) when using rivaroxaban or LMWH following non-major orthopedic surgery (RR 0.80; 95%CI 0.31 to 2.07). In terms of safety, there was also no statistically significant difference in the incidence of bleeding events in patients undergoing non-major orthopedic surgery when using rivaroxaban or LMWH (RR 1.15; 95% CI 0.75 to 1.76).

CONCLUSION

In non-major orthopedic surgery, the risk of venous thromboembolism and bleeding complications is similar when using rivaroxaban or LMWH.

Photoplethysmography-Based Angiography of Skin Tumors in Arbitrary Areas of Human Body

Noninvasive, rapid, and robust diagnostic techniques for clinical screening of tumors located in arbitrary areas of the human body are in demand. To address this challenge, we analyzed the feasibility of photoplethysmography-based angiography for assessing vascular structures within malignant and benign tumors. The proposed hardware and software were approved in a clinical study involving 30 patients with tumors located in the legs, torso, arms, and head. High-contrast and detailed vessel maps within both benign and malignant tumors were obtained. We demonstrated that capillary maps are consistent and can be interpreted using well-established dermoscopic criteria for vascular morphology. Vessel mapping provides valuable details, which may not be available in dermoscopic images and can aid in determining whether a tumor is benign or malignant. We believe that the proposed approach may become a valuable tool in the preliminary cancer diagnosis and is suitable for large-scale screening.

An Information Quality Framework for Managed Health Care

Introduction

Data and information quality play a critical role in the managed healthcare sector, where accurate and reliable information is crucial for optimal decision-making, operations, and patient outcomes. However, managed care organizations face significant challenges in ensuring information quality due to the complexity of data sources, regulatory requirements, and the need for effective data management practices. The goal of this article is to develop and justify an information quality framework for managed healthcare, thereby enabling the sector to better meet its unique information quality challenges.

Methods

The information quality framework provided here was designed using other information quality frameworks as exemplars, as well as a qualitative survey involving interviews of twenty industry leaders structured around 17 questions. The responses were analyzed and tabulated to obtain insights into the information quality needs of the managed healthcare domain.

Results

The novel framework we present herein encompasses strategies for data integration, standardization and validation, and is followed by a justification section that draws upon existing literature and information quality frameworks in addition to the survey of leaders in the industry.

Discussion

Emphasizing objectivity, utility, integrity, and standardization as foundational pillars, the proposed framework provides practical guidelines to empower healthcare organizations in effectively managing information quality within the managed care model.

Prostate-specific membrane antigen PET versus [99mTc]Tc-MDP bone scan for diagnosing bone metastasis in prostate cancer: a head-to-head comparative meta-analysis

Purpose

To evaluate the diagnostic performance of PSMA PET/CT, including [68Ga]Ga-PSMA-11 and [18F]DCFPyL, in comparison with the [99mTc]Tc-MDP bone scan (BS) in identifying bone metastases among prostate cancer patients.

Methods

A search was performed in the PubMed and Embase databases to locate pertinent publications from inception to February 12, 2024. The studies included were those that examined the diagnostic effectiveness of PSMA PET/CT (covering [68Ga]Ga-PSMA-11 and [18F]DCFPyL) compared to [99mTc]Tc-MDP BS in identifying bone metastases among prostate cancer patients. The quality of the selected studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) checklist.

Results

The meta-analysis included nine articles involving 702 patients. The sensitivity of PSMA PET/CT was higher compared to [99mTc]Tc-MDP BS (0.98 vs. 0.85, P < 0.01), while the specificity of PSMA PET/CT was also higher than [99mTc]Tc-MDP BS (0.97 vs. 0.70,P < 0.01). In subgroup analysis, the sensitivity of [68Ga]Ga-PSMA-11 PET/CT was higher compared to [99mTc]Tc-MDP BS (0.98 vs. 0.86), while the specificity of [68Ga]Ga-PSMA-11 PET/CT was also higher than [99mTc]Tc-MDP BS (0.98 vs. 0.65).

Conclusion

Our meta-analysis demonstrates that PSMA PET/CT exhibits superior sensitivity and specificity in comparison with [99mTc]Tc-MDP BS for identifying bone metastases in prostate cancer patients. Further research with head-to-head design is necessary to validate these results and evaluate the clinical effectiveness of these imaging methods.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier PROSPERO CRD42024545112.

Gastrointestinal Sequelae of COVID-19: Investigating Post-Infection Complications-A Systematic Review

Gastrointestinal (GI) complications are significant manifestations of COVID-19 and are increasingly being recognized. These complications range from severe acute pancreatitis to colitis, adding complexity to diagnosis and management. A comprehensive database search was conducted using several databases. Our inclusion criteria encompassed studies reporting severe and long-term GI complications of COVID-19. Digestive disorders were categorized into infections, inflammatory conditions, vascular disorders, structural abnormalities, other diagnoses, and undiagnosed conditions. Of the 73 studies that were selected for full-text review, only 24 met our inclusion criteria. The study highlights a broad range of gastrointestinal complications following COVID-19 infection (excluding liver complications, which are examined separately), including inflammatory conditions, such as ulcerative colitis (UC), acute pancreatitis, and multisystem inflammatory syndrome in children (MIS-C). Other GI complications were reported such as vascular disorders, including diverse thrombotic events and structural abnormalities, which ranged from bowel perforations to adhesions. Additionally, undiagnosed conditions like nausea and abdominal pain were prevalent across different studies involving 561 patients. The findings emphasize the substantial impact of COVID-19 on the GI tract. Ongoing research and monitoring are crucial to understanding the long-term effects and developing effective management strategies for these complications.

Evaluation of ARFI elastography for detecting active mastitis in sheep with previous fibrous lesions: a study of mammary parenchyma and supramammary lymph nodes

The aim of the study was to evaluate the use of Acustic Radiation Force Impulse (ARFI) elastography in mammary parenchyma and supramammary lymph nodes, for detection of active mastitis in sheep with naturally infected chronic fibrous lesions. 27 female sheep were included and B-mode ultrasound and ARFI elastography images were obtained, acquiring qualitative (echogenicity and echotexture) and quantitative (shear rate, depth and short/long axis ratio) variables of 48 mammary glands. The glands were divided into three experimental groups: control group (CG) - healthy animals; LSCC- animals that presented fibrous lesions and SCC (somatic cell count) less than 500 x 103 cls/mL; HSCC: animals that presented fibrous lesions and SCC (somatic cell count) more than 500 x 103 cls/mL; The qualitative variables using B-mode ultrasonography, including echotexture and echogenicity, showed no significant differences between the evaluated groups and tissues (p = 0.9336 and p = 0.233, respectively) .In healthy areas of the gland, it was an increasing in shear wave velocity (SWV) in LSCC than in HSCC (p=0.04). When comparing the fibrosis in the LSCC and HSCC groups with their respective normal areas, the velocity increased in both groups: LSCC (p= 0,0007) and HSCC (p= 0,0001). When comparing the areas of fibrosis in LSCC and HSCC with the CG parenchyma, there was an increase in LSCC (p=0.001) and HSCC (p=0.0001). B-mode ultrasound indicate predominance of hypoechoic echogenicity in lymph nodes and reduced short/long axis ratio in cases of active subclinical mastitis. The supramammary lymph node showed increased SWV when comparing the CG with HSCC groups (p=0.02) and GC with LSCC (p=0.04). B-mode ultrasonography is useful for evaluating the mammary parenchyma, however, its application as a standalone diagnostic technique is not recommended. ARFI elastography indicates potential cutoff points for differentiating subclinical mastitis from healed mastitis, highlighting its importance as a tool for distinguishing normal areas from fibrous parenchymal areas. While this study did not establish specific cutoff points due to sample size limitations, further research with larger sample sizes could explore and define these critical thresholds.

Thyroid Screening Techniques via Bioelectromagnetic Sensing: Imaging Models and Analytical and Computational Methods

The thyroid gland, which is sensitive to electromagnetic radiation, plays a crucial role in the regulation of the hormonal levels of the human body. Biosensors, on the other hand, are essential to access information and derive metrics about the condition of the thyroid by means of of non-invasive techniques. This paper provides a systematic overview of the recent literature on bioelectromagnetic models and methods designed specifically for the study of the thyroid. The survey, which was conducted within the scope of the radiation transmitter-thyroid model-sensor system, is centered around the following three primary axes: the bands of the frequency spectrum taken into account, the design of the model, and the methodology and/or algorithm. Our review highlights the areas of specialization and underscores the limitations of each model, including its time, memory, and resource requirements, as well as its performance. In this manner, this specific work may offer guidance throughout the selection process of a bioelectromagnetic model of the thyroid, as well as a technique for its analysis based on the available resources and the specific parameters of the electromagnetic problem under consideration.

Objective Determination of Site-of-Lesion in Auditory Neuropathy

OBJECTIVE

Auditory neuropathy (AN), a complex hearing disorder, presents challenges in diagnosis and management due to limitations of current diagnostic assessment. This study aims to determine whether diffusion-weighted magnetic resonance imaging (MRI) can be used to identify the site and severity of lesions in individuals with AN.

METHODS

This case-control study included 10 individuals with AN of different etiologies, 7 individuals with neurofibromatosis type 1 (NF1), 5 individuals with cochlear hearing loss, and 37 control participants. Participants were recruited through the University of Melbourne's Neuroaudiology Clinic and the Murdoch Children's Research Institute specialist outpatient clinics. Diffusion-weighted MRI data were collected for all participants and the auditory pathways were evaluated using the fixel-based analysis metric of apparent fiber density. Data on each participant's auditory function were also collected including hearing thresholds, otoacoustic emissions, auditory evoked potentials, and speech-in-noise perceptual ability.

RESULTS

Analysis of diffusion-weighted MRI showed abnormal white matter fiber density in distinct locations within the auditory system depending on etiology. Compared with controls, individuals with AN due to perinatal oxygen deprivation showed no white matter abnormalities ( p > 0.05), those with a neurodegenerative conditions known/predicted to cause VIII cranial nerve axonopathy showed significantly lower white matter fiber density in the vestibulocochlear nerve ( p < 0.001), while participants with NF1 showed lower white matter fiber density in the auditory brainstem tracts ( p = 0.003). In addition, auditory behavioral measures of speech perception in noise and gap detection were correlated with fiber density results of the VIII nerve.

CONCLUSIONS

Diffusion-weighted MRI reveals different patterns of anatomical abnormality within the auditory system depending on etiology. This technique has the potential to guide management recommendations for individuals with peripheral and central auditory pathway abnormality.

Advancements in surgical treatments for Huntington disease: From pallidotomy to experimental therapies

Huntington disease (HD) is an autosomal dominant neurodegenerative disorder characterized by choreic movements, behavioral changes, and cognitive impairment. The pathogenesis of this process is a consequence of mutant protein toxicity in striatal and cortical neurons. Thus far, neurosurgical management of HD has largely been limited to symptomatic relief of motor symptoms using ablative and stimulation techniques. These interventions, however, do not modify the progressive course of the disease. More recently, disease-modifying experimental therapeutic strategies have emerged targeting intrastriatal infusion of neurotrophic factors, cell transplantation, HTT gene silencing, and delivery of intrabodies. Herein we review therapies requiring neurosurgical intervention, including those targeting symptom management and more recent disease-modifying agents, with a focus on safety, efficacy, and surgical considerations.

Outcome Prediction by Diffusion Tensor Imaging (DTI) in Patients with Traumatic Injuries of the Median Nerve

Background/Objectives: The accurate quantification of peripheral nerve axonal regeneration after injury is critically important. Current strategies are limited to detecting early reinnervation. DTI is an MRI modality permitting the assessment of fractional anisotropy, which increases with axonal regeneration. The aim of this pilot study is to evaluate DTI as a potential predictive factor of clinical outcome after median nerve section and microsurgical repair. Methods: We included 10 patients with a complete section of the median nerve, who underwent microsurgical repair up to 7 days after injury. The follow-up period was 1 year, including the current strategy with clinical visits, the Rosén-Lundborg score and electroneuromyography. Additionally, DTI MRI of the injured wrist was planned 1, 3 and 12 months post-operatively and once for the contralateral wrist. Results: The interobserver reliability of DTI measures was almost perfect (ICC 0.802). We report an early statistically significant increase in the fractional anisotropy value after median nerve repair, especially in the region located distal to the suture. Meanwhile, Rosén-Lundborg score gradually increased between the third and sixth month, and continued to increase between the sixth and twelfth month. Conclusions: DTI outcomes three months post-operation could offer greater predictability compared to current strategies. This would enable faster decision-making regarding the need for a potential re-operation in cases of inadequate early reinnervation.

Role of intestinal ultrasound in ulcerative colitis: A systematic review

BACKGROUND

Intestinal ultrasound (IUS) is an emerging, non-invasive, and highly sensitive diagnostic tool in inflammatory bowel disease (IBD), including ulcerative colitis (UC). Despite its potential, its adoption in clinical practice is limited due to a lack of standardization and awareness.

AIM

To perform a comprehensive scoping review based on a systematic literature review on IUS in UC to inform current practice.

METHODS

Ninety-nine original articles about ultrasonography in UC were identified among 7608 citations searching PubMed and EMBASE databases for systematic review.

RESULTS

IUS can be useful as an initial diagnostic strategy in patients with suspected IBD/UC. In UC, IUS can predict endoscopic response, histologic healing, and steroid responsiveness in acute severe cases. IUS can predict response to biologics/small molecules (as early as 2 wk). IUS correlates well with ileo-colonoscopy, but IUS could miss rectal, jejunal, and upper GI lesions in suspected IBD and colon polyps or extra-intestinal manifestations in known IBD. IUS is useful in special situations (children, pregnancy, and postoperative Crohn's disease). Inter-observer agreement is acceptable and trained physicians have comparable diagnostic accuracy. Point-of-care ultrasound impacted management in 40%-60% of cases. Hand-held IUS has excellent agreement with conventional IUS.

CONCLUSION

IUS is a non-invasive, highly sensitive tool in the diagnosis and monitoring of UC, offering excellent patient satisfaction. Point-of-care ultrasound by IBD physicians can significantly impact clinical decision-making.

Toward Digital Periodontal Health: Recent Advances and Future Perspectives

Periodontal diseases, ranging from gingivitis to periodontitis, are prevalent oral diseases affecting over 50% of the global population. These diseases arise from infections and inflammation of the gums and supporting bones, significantly impacting oral health. The established link between periodontal diseases and systemic diseases, such as cardiovascular diseases, underscores their importance as a public health concern. Consequently, the early detection and prevention of periodontal diseases have become critical objectives in healthcare, particularly through the integration of advanced artificial intelligence (AI) technologies. This paper aims to bridge the gap between clinical practices and cutting-edge technologies by providing a comprehensive review of current research. We examine the identification of causative factors, disease progression, and the role of AI in enhancing early detection and treatment. Our goal is to underscore the importance of early intervention in improving patient outcomes and to stimulate further interest among researchers, bioengineers, and AI specialists in the ongoing exploration of AI applications in periodontal disease diagnosis.

Vascular complications of liver abscess: A literature review

Extensive vascular network and proximity to the gastrointestinal tract make the liver susceptible to abscess formation. While pyogenic liver abscesses account for the majority of liver abscesses in the Western world, amebic liver abscesses are more prevalent in tropical and developing nations. Most liver abscesses heal without complications. However, various vascular complications can occur in these patients, including compression of the inferior vena cava, thrombosis of the portal vein and/or hepatic veins, hepatic artery pseudoaneurysm, direct rupture into major vessels or the pericardium, and biliovascular fistula. These complications can present significant clinical challenges due to the potential for haemorrhage, ischemia, and systemic embolism, thereby increasing the risk of morbidity and mortality. Mechanical compression, flow stasis, inflammation, endothelial injury, and direct invasion are some of the proposed mechanisms that can cause vascular complications in the setting of a liver abscess. For the diagnosis, thorough assessment, and therapeutic planning of vascular complications, more sophisticated imaging techniques such as multidetector computed tomography angiography or magnetic resonance angiography may be necessary. Although most vascular complications resolve with abscess treatment alone, additional interventions may be required based on the nature, severity, and course of the complications. This article aims to provide a systematic update on the spectrum of vascular complications of liver abscesses, offering insights into their pathogenesis, diagnosis, and management strategies.

Evaluation of Intra- and Inter-Network Connectivity within Major Brain Networks in Drug-Resistant Depression Using rs-fMRI

Background: Major Depressive Disorder (MDD) is a significant challenge in modern medicine due to its unclear underlying causes. Brain network dysfunction is believed to play a key role in its pathophysiology. Resting-state functional MRI (rs-fMRI), a neuroimaging technique, enables the in vivo assessment of functional connectivity (FC) between brain regions, offering insights into these network dysfunctions. The aim of this study was to evaluate abnormalities in FC within major brain networks in patients with drug-resistant MDD. Methods: The study group consisted of 26 patients with drug-resistant MDD and an age-matched control group (CG) of 26 healthy subjects. The rs-fMRI studies were performed on a 3T MR scanner (Philips, Ingenia) using a 32-channel head and neck coil. Imaging data were statistically analyzed, focusing on the intra- and inter-network FC of the following networks: default mode (DMN), sensorimotor (SMN), visual (VN), salience (SN), cerebellar (CN), dorsal attention (DAN), language (LN), and frontoparietal (FPN). Results: In patients with MDD, the intra-network analysis showed significantly decreased FC between nodes within VN compared to CG. In contrast, the inter-network analysis showed significantly increased FC between nodes from VN and SN or VN and DAN compared to CG. Decreased FC was found between SN and CN or SN and FPN as well as VN and DAN nodes compared to CG. Conclusions: Patients with MDD showed significant abnormalities in resting-state cortical activity, mainly regarding inter-network functional connectivity. These results contribute to the knowledge on the pathomechanism of MDD and may also be useful for developing new treatments.

Multi-parametric Photoacoustic Imaging Combined with Acoustic Radiation Force Impulse Imaging for Applications in Tissue Engineering

Tissue engineering is a dynamic field focusing on the creation of advanced scaffolds for tissue and organ regeneration. These scaffolds are customized to their specific applications and are often designed to be complex, large structures to mimic tissues and organs. This study addresses the critical challenge of effectively characterizing these thick, optically opaque scaffolds that traditional imaging methods fail to fully image due to their optical limitations. We introduce a novel multi-modal imaging approach combining ultrasound, photoacoustic, and acoustic radiation force impulse imaging. This combination leverages its acoustic-based detection to overcome the limitations posed by optical imaging techniques. Ultrasound imaging is employed to monitor the scaffold structure, photoacoustic imaging is employed to monitor cell proliferation, and acoustic radiation force impulse imaging is employed to evaluate the homogeneity of scaffold stiffness. We applied this integrated imaging system to analyze melanoma cell growth within silk fibroin protein scaffolds with varying pore sizes and therefore stiffness over different cell incubation periods. Among various materials, silk fibroin was chosen for its unique combination of features including biocompatibility, tunable mechanical properties, and structural porosity which supports extensive cell proliferation. The results provide a detailed mesoscale view of the scaffolds' internal structure, including cell penetration depth and biomechanical properties. Our findings demonstrate that the developed multimodal imaging technique offers comprehensive insights into the physical and biological dynamics of tissue-engineered scaffolds. As the field of tissue engineering continues to advance, the importance of non-ionizing and non-invasive imaging systems becomes increasingly evident, and by facilitating a deeper understanding and better characterization of scaffold architectures, such imaging systems are pivotal in driving the success of future tissue-engineering solutions.

Association of diabetes and white blood cell count with stroke in patients with carotid artery dissection

BACKGROUND

Carotid artery dissection is an important cause of stroke. However, the predictors of ischemic stroke in patients with carotid artery dissection are controversial. The study aimed to analyze the predictors of ischemic stroke in patients with carotid artery dissection through retrospective medical records.

METHODS

Data of discharged patients diagnosed with carotid artery dissection during 2019-2023 were retrospectively collected. Based on the occurrence of ischemic stroke, the patients were divided into the ischemic stroke or non-ischemic stroke groups. Based on the results of univariate analyses, variables with an associated P value < 0.05 were introduced into the multivariable logistic regression analysis. .

RESULTS

A total of 165 patients were included in the study, with an average age of 55.00 (48.00, 66.00) years, including 86 patients with internal carotid artery dissection and 79 patients with vertebral artery dissection. Ischemic stroke occurred in 69 patients with carotid artery dissection. Multivariate logistic regression analysis indicated that diabetes (odds ratio [OR]: 3.144, 95% confidence interval [CI]: 1.552-6.508, P<0.002) and high white blood cells count (OR: 1.157, 95% CI: 1.02-1.327,P = 0.028) were related to the incidence of ischemic stroke in patients with carotid artery dissection.

CONCLUSION

Ischemic stroke caused by carotid artery dissection causes severe damage to the nervous system. This study found that diabetes and high white blood cells count were associated with the incidence of ischemic stroke in patients with carotid artery dissection. Therefore, monitoring and controlling blood glucose levels and infections is essential in patients with carotid artery dissection to reduce the incidence of stroke.

Perfusion-weighted imaging with dynamic contrast enhancement (PWI/DCE) morphologic, qualitative, semiquantitative, and radiomics features predicting undifferentiated pleomorphic sarcoma (UPS) treatment response

Undifferentiated pleomorphic sarcoma (UPS) is the largest subgroup of soft tissue sarcomas. This study determined the value of perfusion-weighted imaging with dynamic-contrast-enhancement (PWI/DCE) morphologic, qualitative, and semiquantitative features for predicting UPS pathology-assessed treatment effect (PATE). This retrospective study included 33 surgically excised extremity UPS patients with pre-surgical MRI. Volumetric tumor segmentation from PWI/DCE was obtained at Baseline (BL), Post-Chemotherapy (PC), and Post-Radiation Therapy (PRT). The surgical specimens' PATE separated cases into Responders (R) (≥ 90%, 16 patients), Partial-Responders (PR) (89 - 31%, 10 patients), and Non-Responders (NR) (≤ 30%, seven patients). Seven semiquantitative kinetic parameters and maps were extracted from time-intensity curves (TICs), and 107 radiomic features were derived. Statistical analyses compared R vs. PR/NR. At PRT, 79% of R displayed a "Capsular" morphology (P = 1.49 × 10-7), and 100% demonstrated a TIC-type II (P = 8.32 × 10-7). 80% of PR showed "Unipolar" morphology (P = 1.03 × 10-5), and 60% expressed a TIC-type V (P = 0.06). Semiquantitative wash-in rate (WiR) was able to separate R vs. PR/NR (P = 0.0078). The WiR radiomics displayed significant differences in the first_order_10 percentile (P = 0.0178) comparing R vs. PR/NR at PRT. The PWI/DCE TIC-type II curve, low WiR, and "Capsular" enhancement represent PRT patterns typically observed in successfully treated UPS and demonstrate potential for UPS treatment response assessment.

Multimodal three-dimensional characterization of murine skeletal muscle micro-scale elasticity, structure, and composition: Impact of dysferlinopathy, Duchenne muscular dystrophy, and age on three hind-limb muscles

Skeletal muscle tissue function is governed by the mechanical properties and organization of its components, including myofibers, extracellular matrix, and adipose tissue, which can be modified by the onset and progression of many disorders. This study used a novel combination of quantitative micro-elastography and clearing-enhanced three-dimensional (3D) microscopy to assess 3D micro-scale elasticity and micro-architecture of muscles from two muscular dystrophies: dysferlinopathy and Duchenne muscular dystrophy, using male BLA/J and mdx mice, respectively, and their wild-type (WT) controls. We examined three muscles with varying proportions of slow- and fast-twitch myofibers: the soleus (predominantly slow), extensor digitorum longus (EDL; fast), and quadriceps (mixed), from BLA/J and WTBLA/J mice aged 3, 10, and 24 months, and mdx and WTmdx mice aged 10 months. Both dysferlin deficiency and age reduced the elasticity and variability of elasticity of the soleus and quadriceps, but not EDL. Overall, the BLA/J soleus was 20% softer than WT and less mechanically heterogeneous (-14% in standard deviation of elasticity). The BLA/J quadriceps at 24 months was 72% softer than WT and less mechanically heterogeneous (-59% in standard deviation), with substantial adipose tissue accumulation. While mdx muscles did not differ quantitatively from WT, regional heterogeneity was evident in micro-scale elasticity and micro-architecture of quadriceps (e.g., 11.2 kPa in a region with marked pathology vs 3.8 kPa in a less affected area). These results demonstrate differing biomechanical changes in hind-limb muscles of two distinct muscular dystrophies, emphasizing the potential for this novel multimodal technique to identify important differences between various myopathies.

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.

Rapid identification and management of stress-induced cardiomyopathy using POCUS after strangulation: A case report

RATIONALE

Stress-induced cardiomyopathy (SCMP), also known as Takotsubo syndrome, is a transient cardiac condition often precipitated by severe emotional or physical stress. It is commonly mistaken for acute coronary syndrome due to similar clinical presentations. The use of point-of-care ultrasound (POCUS) provides a noninvasive, rapid diagnostic alternative that can potentially reduce the need for invasive coronary angiography, especially in emergency settings.

PATIENT CONCERNS

A 26-year-old woman with type 1 diabetes presented to the emergency department following a suicidal hanging attempt. Upon arrival, she was conscious but confused, with stable vital signs. There were visible signs of strangulation, but no other immediate physical abnormalities. Laboratory tests revealed elevated cardiac enzymes and hyperglycemia.

DIAGNOSES

Initial bedside POCUS revealed a reduced ejection fraction and regional wall motion abnormalities in the midportion of the left ventricle, suggesting SCMP. These findings, combined with the patient's history and absence of other contributory factors, led to a provisional diagnosis of SCMP.

INTERVENTIONS

The patient was admitted to the intensive care unit for close monitoring. Serial POCUS examinations were performed to track cardiac function. Due to the rapid improvement in regional wall motion abnormalities observed through POCUS, the planned coronary angiography was deferred.

OUTCOMES

The patient exhibited significant clinical improvement within 24 hours, with normalization of cardiac function as demonstrated by follow-up POCUS. Cardiac enzyme levels also returned to normal. The patient was discharged directly from the intensive care unit without the need for further invasive procedures.

LESSONS

This case underscores the diagnostic value of POCUS in rapidly identifying SCMP in emergency settings, which can guide timely and appropriate management. The noninvasive nature of POCUS may reduce the need for invasive diagnostics, minimize hospital stay duration, and enhance cost-effectiveness in managing SCMP.

Focal cortical dysplasia lesion segmentation using multiscale transformer

OBJECTIVES

Accurate segmentation of focal cortical dysplasia (FCD) lesions from MR images plays an important role in surgical planning and decision but is still challenging for radiologists and clinicians. In this study, we introduce a novel transformer-based model, designed for the end-to-end segmentation of FCD lesions from multi-channel MR images.

METHODS

The core innovation of our proposed model is the integration of a convolutional neural network-based encoder-decoder structure with a multiscale transformer to augment the feature representation of lesions in the global field of view. Transformer pathways, composed of memory- and computation-efficient dual-self-attention modules, leverage feature maps from varying depths of the encoder to discern long-range interdependencies among feature positions and channels, thereby emphasizing areas and channels relevant to lesions. The proposed model was trained and evaluated on a public-open dataset including MR images of 85 patients using both subject-level and voxel-level metrics.

RESULTS

Experimental results indicate that our model offers superior performance both quantitatively and qualitatively. It successfully identified lesions in 82.4% of patients, with a low false-positive lesion cluster rate of 0.176 ± 0.381 per patient. Furthermore, the model achieved an average Dice coefficient of 0.410 ± 0.288, outperforming five established methods.

CONCLUSION

Integration of the transformer could enhance the feature presentation and segmentation performance of FCD lesions. The proposed model has the potential to serve as a valuable assistive tool for physicians, enabling rapid and accurate identification of FCD lesions. The source code and pre-trained model weights are available at https://github.com/zhangxd0530/MS-DSA-NET .

CRITICAL RELEVANCE STATEMENT

This multiscale transformer-based model performs segmentation of focal cortical dysplasia lesions, aiming to help radiologists and clinicians make accurate and efficient preoperative evaluations of focal cortical dysplasia patients from MR images.

KEY POINTS

The first transformer-based model was built to explore focal cortical dysplasia lesion segmentation. Integration of global and local features enhances the segmentation performance of lesions. A valuable benchmark for model development and comparative analyses was provided.

Fibro-adhesive Bursitis: A Novel Sonographic Finding in Adhesive Capsulitis Patients and a Proposal of Management

INTRODUCTION

Adhesive capsulitis, also known as "frozen shoulder," is a debilitating shoulder condition increasingly linked to fibroadhesive bursitis, particularly after COVID-19 and related vaccinations. There is no definitive gold standard for its treatment, the primary therapeutic objectives of which are the reduction of pain and the restoration of shoulder range of motion. The aim of our study was to analyze treatment outcomes based on quantitative measures of shoulder function and symptom relief.

METHOD

Conducted between January 2022 and April 2023, the research involved 45 patients initially diagnosed with adhesive capsulitis and associated fibroadhesive bursitis. After excluding nine patients for other concomitant pathologies (five for calcific tendinopathy and four for rotator cuff injury), 36 patients were randomized into two groups: one group was treated with glenohumeral hydrodistension, the other with glenohumeral hydrodistension combined with bursal injection. Assessments were conducted at baseline and then 2, 4, and 6 months after treatment, focusing on changes in pain levels, functional scores, and range of motion in all planes. Each group followed a home-based rehabilitation protocol.

RESULTS

Significant improvements were observed in both treatment groups, with the combined hydrodistension and bursal injection group showing notably superior outcomes. Specifically, the range of motion in flexion improved from an initial median of 80° to 155° in the combined treatment group, compared to an increase from 75.5° to 129° in the group treated with hydrodistension alone. This enhancement was statistically significant (p < 0.001). Regarding pain reduction, the combined treatment group demonstrated a dramatic decrease in visual analogue scale (VAS) scores, from a baseline median of 7 to 1 at the 6-month follow-up. In contrast, the hydrodistension-only group showed a reduction from 7 to 3, with these differences also proving statistically significant (p < 0.001).

CONCLUSIONS

Ultrasound-guided hydrodistension of the glenohumeral joint, if combined with bursal injection and specific exercises, effectively reduces pain, decreases disability, and improves range of motion in patients with second-stage adhesive capsulitis. This study highlights the importance of a combined approach in the management of this complex condition, especially after the histological changes that occurred after COVID-19 and related vaccinations.

TRIAL REGISTRATION

ClinicalTrials.gov identifier NCT06062654.

Optimizing graph neural network architectures for schizophrenia spectrum disorder prediction using evolutionary algorithms

BACKGROUND AND OBJECTIVE

The accurate diagnosis of schizophrenia spectrum disorder plays an important role in improving patient outcomes, enabling timely interventions, and optimizing treatment plans. Functional connectivity analysis, utilizing functional magnetic resonance imaging data, has been demonstrated to offer invaluable biomarkers conducive to clinical diagnosis. However, previous studies mainly focus on traditional machine learning methods or hand-crafted neural networks, which may not fully capture the spatial topological relationship between brain regions.

METHODS

This paper proposes an evolutionary algorithm (EA) based graph neural architecture search (GNAS) method. EA-GNAS has the ability to search for high-performance graph neural networks for schizophrenia spectrum disorder prediction. Moreover, we adopt GNNExplainer to investigate the explainability of the acquired architectures, ensuring that the model's predictions are both accurate and comprehensible.

RESULTS

The results suggest that the graph neural network model, derived using genetic algorithm search, outperforms under five-fold cross-validation, achieving a fitness of 0.1850. Relative to conventional machine learning and other deep learning approaches, the proposed method yields superior accuracy, F1 score, and AUC values of 0.8246, 0.8438, and 0.8258, respectively.

CONCLUSION

Based on a multi-site dataset from schizophrenia spectrum disorder patients, the findings reveal an enhancement over prior methods, advancing our comprehension of brain function and potentially offering a biomarker for diagnosing schizophrenia spectrum disorder.

Symptom mapping and personalized care for depression, anxiety and stress: A data-driven AI approach

BACKGROUND

Depression, anxiety, and stress disorders have significant and widespread impacts worldwide, affecting millions of individuals and their communities. According to the World Health Organization, depression impacts the daily lives of more than 300 million people, making it one of the most important diseases globally. Treatment for these mental disorders (MD) typically involves medication and psychotherapies, but also incorporates technological resources like Artificial Intelligence (AI) to indicate personalized therapies and care. While various AI approaches have been applied in the context of MD in the literature, they often focus solely on aiding diagnosis.

OBJECTIVE

This research proposes an AI approach for mapping symptoms and assisting in the personalized care of depression, anxiety, and stress.

METHODS

Symptom mapping utilizes data mining (DM) techniques to generate rules representing knowledge extracted from data of 242 patients collected using the Depression, Anxiety, and Stress Scale (DASS-21). This knowledge elucidates how symptoms impact the severity degrees of considered MDs. Subsequently, the generated rules are employed to construct a Fuzzy Inference System (FIS) for inferring the severities of MDs based on patient symptoms and personal data.

RESULTS AND CONCLUSIONS

The results achieved in the DM (accuracy ≥92.98 %, sensibility ≥86.02 %, specificity ≥97.32 %, and kappa statistic ≥87.98 %), indicating consistent patterns, along with the results produced by the FIS, demonstrate the potential of the proposed approach to assist health professionals in rapidly predicting symptoms of depression, anxiety, and stress, thereby facilitating outpatient screening and emergency care. Furthermore, it can improve the association of symptoms, referral to specialized care, therapeutic proposals, and even investigations of other diseases unrelated to MD.

Rapid detection of liver metastasis risk in colorectal cancer patients through blood test indicators

Introduction

Colorectal cancer (CRC) is one of the most common malignancies, with liver metastasis being its most common form of metastasis. The diagnosis of colorectal cancer liver metastasis (CRCLM) mainly relies on imaging techniques and puncture biopsy techniques, but there is no simple and quick early diagnosisof CRCLM.

Methods

This study aims to develop a method for rapidly detecting the risk of liver metastasis in CRC patients through blood test indicators based on machine learning (ML) techniques, thereby improving treatment outcomes. To achieve this, blood test indicators from 246 CRC patients and 256 CRCLM patients were collected and analyzed, including routine blood tests, liver function tests, electrolyte tests, renal function tests, glucose determination, cardiac enzyme profiles, blood lipids, and tumor markers. Six commonly used ML models were used for CRC and CRCLM classification and optimized by using a feature selection strategy.

Results

The results showed that AdaBoost algorithm can achieve the highest accuracy of 89.3% among the six models, which improved to 91.1% after feature selection strategy, resulting with 20 key markers.

Conclusions

The results demonstrate that the combination of machine learning techniques with blood markers is feasible and effective for the rapid diagnosis of CRCLM, significantly im-proving diagnostic ac-curacy and patient prognosis.

The smallest traces of crime: Trace elements in forensic science

BACKGROUND

Securing the evidence in various investigative situations is often associated with trace analysis, including fingerprints or blood groups. However, when classic and conventional methods fail, trace elements, such as copper, zinc, fluorine, and many others found in exceedingly insignificant amounts in organisms, may prove useful and effective.

METHODS

The presented work reviews articles published between 2003 and 2023, describing the use of trace elements and the analytical methods employed for their analysis in forensic medicine and related sciences.

RESULTS & CONCLUSION

Trace elements can be valuable as traces collected at crime scenes and during corpse examination, aiding in determining characteristics like the sex or age of the deceased. Additionally, trace elements levels in the body can serve as alcohol or drug poisoning markers. In traumatology, trace elements enable the identification of various instruments and the injuries caused by their use.

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.

Houston, We Have AI Problem! Quality Issues with Neuroimaging-Based Artificial Intelligence in Parkinson's Disease: A Systematic Review

In recent years, many neuroimaging studies have applied artificial intelligence (AI) to facilitate existing challenges in Parkinson's disease (PD) diagnosis, prognosis, and intervention. The aim of this systematic review was to provide an overview of neuroimaging-based AI studies and to assess their methodological quality. A PubMed search yielded 810 studies, of which 244 that investigated the utility of neuroimaging-based AI for PD diagnosis, prognosis, or intervention were included. We systematically categorized studies by outcomes and rated them with respect to five minimal quality criteria (MQC) pertaining to data splitting, data leakage, model complexity, performance reporting, and indication of biological plausibility. We found that the majority of studies aimed to distinguish PD patients from healthy controls (54%) or atypical parkinsonian syndromes (25%), whereas prognostic or interventional studies were sparse. Only 20% of evaluated studies passed all five MQC, with data leakage, non-minimal model complexity, and reporting of biological plausibility as the primary factors for quality loss. Data leakage was associated with a significant inflation of accuracies. Very few studies employed external test sets (8%), where accuracy was significantly lower, and 19% of studies did not account for data imbalance. Adherence to MQC was low across all observed years and journal impact factors. This review outlines that AI has been applied to a wide variety of research questions pertaining to PD; however, the number of studies failing to pass the MQC is alarming. Therefore, we provide recommendations to enhance the interpretability, generalizability, and clinical utility of future AI applications using neuroimaging in PD. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Applications of Artificial Intelligence and Machine Learning in Spine MRI

Diagnostic imaging, particularly MRI, plays a key role in the evaluation of many spine pathologies. Recent progress in artificial intelligence and its subset, machine learning, has led to many applications within spine MRI, which we sought to examine in this review. A literature search of the major databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search yielded 1226 results, of which 50 studies were selected for inclusion. Key data from these studies were extracted. Studies were categorized thematically into the following: Image Acquisition and Processing, Segmentation, Diagnosis and Treatment Planning, and Patient Selection and Prognostication. Gaps in the literature and the proposed areas of future research are discussed. Current research demonstrates the ability of artificial intelligence to improve various aspects of this field, from image acquisition to analysis and clinical care. We also acknowledge the limitations of current technology. Future work will require collaborative efforts in order to fully exploit new technologies while addressing the practical challenges of generalizability and implementation. In particular, the use of foundation models and large-language models in spine MRI is a promising area, warranting further research. Studies assessing model performance in real-world clinical settings will also help uncover unintended consequences and maximize the benefits for patient care.

Chronic Pain and Bone-Related Pathologies: A Narrative Review

Purpose

Pain related to bone may occur as a result of trauma, bone fracture, genetic disease, arthritis, benign or malignant primary bone tumors and bone cancer metastases. We discuss the pathophysiology of chronic bone-related pain, treatment options and therapeutic perspectives.

Methods

Using predefined terms, we searched PubMed, MEDLINE, and Google Scholar for meta-analyses, evidence-based reviews, and clinical practice guidelines. This narrative article reviews pathologies linked to chronic bone pain and discusses the preventive and therapeutic strategies for better bone pain management.

Results

Pathophysiology of bone-related pain is complex, especially in cancer conditions and missing gaps are underlined. Treatment of pain, after adequate evaluation, includes classical analgesics, adjuvants for neuropathic and refractory pain, specific bone drugs, surgery and non-pharmacological approaches. Prevention of chronic bone pain encompasses prevention of central sensitization and of causal diseases.

Conclusion

Translational research, drug repurposing, an interdisciplinary approach and a person-centered assessment to evaluate, beyond pain, physical, social and functional abilities, are proposed future directions to improve chronic bone pain management and optimize independence and quality of life.

Summary

Chronic bone-related pain is frequent and is associated with an impairment of quality of life. In this review, we summarize the pathophysiology of chronic bone pain, describe treatment approaches and envisage new avenues for pain alleviation. Our article will help doctors manage chronic bone pain and address unmet needs for future research to alleviate bone-related pain.

Selection of X-ray Tube Settings for Relative Bone Density Quantification in the Knee Joint of Cats Using Computed Digital Absorptiometry

Bone mineral density (BMD) varies with aging and both systemic and local diseases; however, such evidence is lacking in feline medicine. This may be due to the need for general anesthesia in cats for direct BMD measurements using dual-energy X-ray absorptiometry (DXA) or quantitative computed tomography (QCT). In this study, computed digital absorptiometry (CDA), an indirect relative BMD-measuring method, was optimized to select an X-ray tube setting for the quantitative assessment of the feline knee joint. The knee joints of nine cats were radiographically imaged and processed using the CDA method with an aluminum density standard and five X-ray tube settings (from 50 to 80 kV; between 1.2 and 12 mAs). The reference attenuation of the X-ray beam for ten steps (S1-S10) of the density standard was recorded in Hounsfield units (HU), compared between X-ray tube settings, and used to determine the ranges of relative density applied for radiograph decomposition. The relative density decreased (p < 0.0001) with an increase in kV and dispersed with an increase in mAs. Then, the percentage of color pixels (%color pixels), representing ranges of relative density, was compared among S1-S10 and used for the recognition of background artifacts. The %color pixels was the highest for low steps and the lowest for high steps (p < 0.0001), regardless of X-ray tube settings. The X-ray tube setting was considered the most beneficial when it effectively covered the lowest possible HU ranges without inducing background artifacts. In conclusion, for further clinical application of the CDA method for quantitative research on knee joint OA in cats, 60 kV and 1.2 mAs settings are recommended.

Diagnostic value of multimodal ultrasound for breast cancer and prediction of sentinel lymph node metastases

Background

Sentinel lymph node metastasis (SLNM) is a critical factor in the prognosis and treatment planning for breast cancer (BC), as it indicates the potential spread of cancer to other parts of the body. The accurate prediction and diagnosis of SLNM are essential for improving clinical outcomes and guiding treatment decisions.

Objective

This study aimed to construct a Lasso regression model by integrating multimodal ultrasound (US) techniques, including US, shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS), to improve the predictive accuracy of sentinel lymph node metastasis in breast cancer and provide more precise guidance for clinical treatment.

Results

A total of 253 eligible samples were screened, of which 148 were group benign and 105 were group malignant. There were statistically significant differences (p < 0.05) between group malignant patients in terms of age, palpable mass, body mass index, distance to nipple, maximum diameter, blood flow, microcalcification, 2D border, 2D morphology, and 2D uniformity and group benign. The Lasso regression model was useful in the diagnosis of benign and malignant nodules with an AUC of 0.966 and in diagnosing SLNM with an AUC of 0.832.

Conclusion

In this study, we successfully constructed and validated a Lasso regression model based on the multimodal ultrasound technique for predicting whether SLNM occurs in BCs, showing high diagnostic accuracy.

Evaluation of prognostic risk factors of triple-negative breast cancer with 18F-FDG PET/CT parameters, clinical pathological features and biochemical indicators

Introduction

Breast cancer is a heterogeneous disease comprising various molecular subtypes, including Luminal A, Luminal B, human epidermal growth factor receptor-2 (HER2) positive, and triple negative types, each with distinct biological characteristics and behaviors. Triple negative breast cancer (TNBC) remains a particularly challenging subtype worldwide. Our study aims to evaluate whether Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (18F-FDG PET/CT) parameters, clinical pathological features, and biochemical indicators serve as prognostic risk factors for TNBC. Additionally, we explore correlations between biochemical indicators and 18F-FDG PET/CT parameters.

Methods

We conducted a retrospective analysis of 95 TNBC patients who underwent preoperative 18F-FDG PET/CT examinations at Tianjin Medical University Cancer Institute and Hospital from 2013 to 2018. Collected data included 18F-FDG PET/CT parameters, clinical and pathological features, and biochemical indicators. We used Kaplan-Meier survival analysis and multivariate Cox regression analysis to evaluate associations between 18F-FDG PET/CT parameters/biochemical indicators and disease free survival (DFS)/overall survival (OS). The log-rank test determined significant differences in survival curves, and the Spearman correlation coefficient analyzed correlations between quantitative variables. Visualization and analysis were performed using R packages.

Results

Among 95 TNBC patients, mean standardized uptake value (SUVmean) was significantly correlated with DFS. Fasting blood glucose (FBG), α- L-fucosylase (AFU) and Creatine kinase (CK) were independent predictors of DFS, while Precursor albumin (PALB) and CK were independent predictors of OS. FBG showed correlations with SUVpeak and SUVmean, and CK was correlated with peak standardized uptake value (SUVpeak). Our results indicated that 18F-FDG PET/CT parameters and biochemical indicators may constitute a new prognostic model for TNBC patients post-surgery.

Discussion

We found that SUVmean, FBG, AFU and CK are predictive factors for DFS in TNBC patients post-surgery, while PALB and CK are predictive factors for OS, which prompts us to pay more attention to these indicators in clinical practice. Also 18F-FDG PET/CT parameters and biochemical indicators have potential utility in constituting a new prognostic model for TNBC patients post-surgery.

Noise-imitation learning: unpaired speckle noise reduction for optical coherence tomography

Objective.Optical coherence tomography (OCT) is widely used in clinical practice for its non-invasive, high-resolution imaging capabilities. However, speckle noise inherent to its low coherence principle can degrade image quality and compromise diagnostic accuracy. While deep learning methods have shown promise in reducing speckle noise, obtaining well-registered image pairs remains challenging, leading to the development of unpaired methods. Despite their potential, existing unpaired methods suffer from redundancy in network structures or interaction mechanisms. Therefore, a more streamlined method for unpaired OCT denoising is essential.Approach.In this work, we propose a novel unpaired method for OCT image denoising, referred to as noise-imitation learning (NIL). NIL comprises three primary modules: the noise extraction module, which extracts noise features by denoising noisy images; the noise imitation module, which synthesizes noisy images and generates fake clean images; and the adversarial learning module, which differentiates between real and fake clean images through adversarial training. The complexity of NIL is significantly lower than that of previous unpaired methods, utilizing only one generator and one discriminator for training.Main results.By efficiently fusing unpaired images and employing adversarial training, NIL can extract more speckle noise information to enhance denoising performance. Building on NIL, we propose an OCT image denoising pipeline, NIL-NAFNet. This pipeline achieved PSNR, SSIM, and RMSE values of 31.27 dB, 0.865, and 7.00, respectively, on the PKU37 dataset. Extensive experiments suggest that our method outperforms state-of-the-art unpaired methods both qualitatively and quantitatively.Significance.These findings indicate that the proposed NIL is a simple yet effective method for unpaired OCT speckle noise reduction. The OCT denoising pipeline based on NIL demonstrates exceptional performance and efficiency. By addressing speckle noise without requiring well-registered image pairs, this method can enhance image quality and diagnostic accuracy in clinical practice.

Fully automated epicardial adipose tissue volume quantification with deep learning and relationship with CAC score and micro/macrovascular complications in people living with type 2 diabetes: the multicenter EPIDIAB study

BACKGROUND

The aim of this study (EPIDIAB) was to assess the relationship between epicardial adipose tissue (EAT) and the micro and macrovascular complications (MVC) of type 2 diabetes (T2D).

METHODS

EPIDIAB is a post hoc analysis from the AngioSafe T2D study, which is a multicentric study aimed at determining the safety of antihyperglycemic drugs on retina and including patients with T2D screened for diabetic retinopathy (DR) (n = 7200) and deeply phenotyped for MVC. Patients included who had undergone cardiac CT for CAC (Coronary Artery Calcium) scoring after inclusion (n = 1253) were tested with a validated deep learning segmentation pipeline for EAT volume quantification.

RESULTS

Median age of the study population was 61 [54;67], with a majority of men (57%) a median duration of the disease 11 years [5;18] and a mean HbA1c of7.8 ± 1.4%. EAT was significantly associated with all traditional CV risk factors. EAT volume significantly increased with chronic kidney disease (CKD vs no CKD: 87.8 [63.5;118.6] vs 82.7 mL [58.8;110.8], p = 0.008), coronary artery disease (CAD vs no CAD: 112.2 [82.7;133.3] vs 83.8 mL [59.4;112.1], p = 0.0004, peripheral arterial disease (PAD vs no PAD: 107 [76.2;141] vs 84.6 mL[59.2; 114], p = 0.0005 and elevated CAC score (> 100 vs  < 100 AU: 96.8 mL [69.1;130] vs 77.9 mL [53.8;107.7], p < 0.0001). By contrast, EAT volume was neither associated with DR, nor with peripheral neuropathy. We further evidenced a subgroup of patients with high EAT volume and a null CAC score. Interestingly, this group were more likely to be composed of young women with a high BMI, a lower duration of T2D, a lower prevalence of microvascular complications, and a higher inflammatory profile.

CONCLUSIONS

Fully-automated EAT volume quantification could provide useful information about the risk of both renal and macrovascular complications in T2D patients.

Artificial intelligence techniques in liver cancer

Hepatocellular Carcinoma (HCC), the most common primary liver cancer, is a significant contributor to worldwide cancer-related deaths. Various medical imaging techniques, including computed tomography, magnetic resonance imaging, and ultrasound, play a crucial role in accurately evaluating HCC and formulating effective treatment plans. Artificial Intelligence (AI) technologies have demonstrated potential in supporting physicians by providing more accurate and consistent medical diagnoses. Recent advancements have led to the development of AI-based multi-modal prediction systems. These systems integrate medical imaging with other modalities, such as electronic health record reports and clinical parameters, to enhance the accuracy of predicting biological characteristics and prognosis, including those associated with HCC. These multi-modal prediction systems pave the way for predicting the response to transarterial chemoembolization and microvascular invasion treatments and can assist clinicians in identifying the optimal patients with HCC who could benefit from interventional therapy. This paper provides an overview of the latest AI-based medical imaging models developed for diagnosing and predicting HCC. It also explores the challenges and potential future directions related to the clinical application of AI techniques.