Data System Research Articles

The role of contrast-enhanced ultrasound in the radiological classification of liver observations identified by CT and MRI.

Timely and accurate diagnosis of hepatocellular carcinoma (HCC) is essential for improving patient outcomes and guiding treatment. This multicenter study aimed to optimize the diagnostic workflow for HCC through a step-wise combination of CT/MRI and contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS). This was a multicenter, retrospective analysis of prospectively recruited high-risk HCC participants with liver observations from 4 institutions, between January 2017 and December 2021. These participants initially underwent CT/MRI followed by CEUS, with observations categorized according to CT/MRI/CEUS LI-RADS. Three step-wise diagnostic strategies were evaluated, starting with CT/MRI and followed by CEUS, and compared to CT/MRI LI-RADS alone. Performance metrics included AUC, accuracy, sensitivity, specificity, PPV, and NPV, using pathology or over one year of follow-up as standards. The impact on clinical decisions was measured by false-negative, false-positive, and biopsy rates. Of 1264 participants, 874 (69%) were confirmed as HCC. The step-wise strategies outperformed CT/MRI LI-RADS. Strategy-3, which involved subsequent CEUS for CT/MRI LR-3/4 observations, significantly improved sensitivity (88.8% vs. 79.9%, P < 0.001) while maintaining comparable specificity (88.2% vs. 91.3%, P > 0.05). Strategy-3 reduced biopsy rate (31.5-22.4%, P = 0.028) and decreased false-negative rate (20.1-11.2%, P < 0.001). Additionally, 96% (55/57) of CT/MRI LR-3 and 97% (77/79) of CT/MRI LR-4 observations were accurately diagnosed and treated as HCC, with 61% (74/121) of CT/MRI LR-4 observations avoiding biopsy with CEUS-assisted. A step-wise approach using CT/MRI followed by CEUS for LR-3/4 observations improved the diagnostic performance and further refined clinical decision-making in HCC. Clinical Trial Registration Number: ChiCTR-DDD-16010089.

Optimization of network performance of distributed storage system for biomechanical big data based on cloud computing

This study proposes a network performance optimization strategy based on cloud computing to address the stringent demands of biomechanical big data on the efficiency of distributed storage systems. Biomechanical data, including motion capture, force plate measurements, and tissue strain analysis, involve large-scale, high-frequency, and heterogeneous datasets that necessitate efficient storage and real-time processing. By optimizing data transmission paths, designing an efficient caching mechanism, dynamically allocating bandwidth resources, and implementing network congestion control, the system significantly enhances throughput, reduces latency, and improves bandwidth utilization and data transmission reliability.

An international comparison of longitudinal health data collected on long COVID in nine high income countries: a qualitative data analysis

BackgroundLong coronavirus disease (COVID) presents a significant health challenge. Long-term monitoring is critical to support understanding of the condition, service planning and evaluation. We sought to identify and examine longitudinal health data collected on long COVID to inform potential decisions in England regarding the rationale for data collection, the data collected, the sources from which data were collected and the methods used for collection.MethodsWe included datasets in high-income countries that experienced similar coronavirus disease 2019 (COVID-19) waves to England pre-vaccine rollout. Relevant datasets were identified through literature searches, the authors’ networks and participants’ recommendations. We undertook semi-structured interviews with individuals involved in the development and running of the datasets. We held a focus group discussion with representatives of three long COVID patient organisations to capture the perspective of those with long COVID. Emergent findings were tested in a workshop with country interviewees.ResultsWe analysed 17 datasets from nine countries (Belgium, Canada, Germany, Italy, the Netherlands, New Zealand, Sweden, Switzerland and the United Kingdom). Datasets sampled different populations, used different data collection tools and measured different outcomes, reflecting different priorities. Most data collection was research (rather than health care system)-funded and time-limited. For datasets linked to specialist services, there was uncertainty surrounding how long these would continue. Definitions of long COVID varied. Patient representatives’ favoured self-identification, given challenges in accessing care and receiving a diagnosis; New Zealand’s long COVID registry was the only example identified using this approach. Post-exertion malaise, identified by patients as a critical outcome, was absent from all datasets. The lack of patient-reported outcome measures (PROMs) was highlighted as a limitation of datasets reliant on routine health data, although some had developed mechanisms to extend data collection using patient surveys.ConclusionsAddressing research questions related to the management of long COVID requires diverse data sources that capture different populations with long COVID over the long-term. No country examined has developed a comprehensive long-term data system for long COVID, and, in many settings, data collection is ending leaving a gap. There is no obvious model for England or other countries to follow, assuming there remains sufficient policy interest in establishing a long-term long COVID patient registry.

Global research trends and hotspots on imaging of bladder cancer: A bibliometric and visual analysis from 1981 to 2023.

There was currently no bibliometric analysis available regarding to bladder cancer (BCa) imaging. The aim of this study was to conduct a comprehensive bibliometric analysis of relevant literature on the imaging of BCa and elucidate global research hotspots and further trends in this field. All relevant literature on the imaging of BCa published between 1981 and 2023 were retrieved from the Web of Science Core Collection. VOSviewer, Bibliometrix, and Citespace were utilized for bibliometric analysis of publications, countries, authors, institutions, journals, references, and keywords. A total of 4462 articles were retrieved. The research in this field has been increasing consistently since 1981. The United States of America was the most productive country and most productive institutions were from it. Shariat SF was the most productive author with 36 articles and the author with the highest co-citations was Herr HW (472). Journal of Urology was the most productive journal and Frontiers in Oncology, Abdominal Radiology and Cancers exhibited heightened activity in recent years. A study by Siegel RL, published in CA-A Cancer Journal for Clinicians in 2019, had the highest number of co-citations. Further analysis of the keyword analysis and timeline view revealed that "radiomics," "deep learning," "multiparametric MRI," "VI-RADS," "muscle-invasive bladder cancer," "immunotherapy," and "long term outcome" were the most recent hotspots. In totally, in the period of 1981 to 2023 year, the USA occupies a critical position in the field of BCa imaging. It is anticipated that MRI-based imaging-reporting and data system and deep learning will be the research hotspots in the future.

Evaluation of Educational Information Systems for Data and Decision Management

Evaluation of Educational Information Systems for Data and Decision Management

Additive hazard causal model with a binary instrumental variable.

The causal effect of a treatment on a censored outcome is often of fundamental interest and instrumental variable (IV) is a useful tool to tame bias caused by unmeasured confounding. The two-stage least squares commonly used for IV analysis in linear regression have been developed for regression analysis in a survival context under an additive hazards model. In this work, we study a distinctive binary IV framework with censored data where the causal treatment effect is quantified through an additive hazard model for compliers. Employing the special characteristics of the binary IV and adapting the principle of conditional score, we establish a weighted estimator with explicit form. We establish the asymptotic properties of the proposed estimators and provide plug-in and perturbed variance estimators. The finite sample performance of the proposed estimator is examined by extensive simulations. The proposed method is applied to a data set from the U.S. renal data system to compare dialytic modality-specific survival for end-stage renal disease patients.

Increased cancer incidence in patients with pre-existing heart failure: results from a French nationwide cohort study.

There is conflicting evidence as to whether patients with pre-existing heart failure (HF) are at increased risk of developing cancer, especially because of common risk factors. We aimed to assess the incidence of cancer in patients with pre-existing HF compared with patients without known HF. The French National Administrative Health Data System, containing all healthcare information for 99% of the French population, was used to identify adult patients with a first diagnosis of HF between 2010 and 2019 and without a history of cancer before HF diagnosis. HF patients were matched for sex and age to HF-free and cancer-free individuals (3:1 ratio). We found 330,867 HF patients, and 992,601 matched controls (54.7% women, mean age 77.7±13.5 years). A first cancer was diagnosed in 28,151 (8.5%) HF patients over a mean follow-up of 4.3±2.8 years compared to 77,325 (7.8%) in the controls over 4.9±2.8 years of follow-up (unadjusted subdistribution HR, sHR: 1.12 [1.11-1.13], P<0.001). The higher risk of new cancer in HF patients remained after full adjustment for major comorbidities, age, sex, year of diagnosis, region of residence, tobacco use and alcohol consumption (adjusted sHR=1.06, [1.04-1.07]; P<0.0001). Overall, the calculated attributable risk of new cancer after HF was 16.5% (9.9-16.8%). This increased risk was observed for most solid malignancies (especially colorectal and lung cancer), and for multiple myeloma. Patients with a history of HF have a higher risk of developing cancer than the general population. Cancer screening strategies should be advocated in patients with HF.

Dermatofibrosarcoma of the breast: A case report.

Dermatofibrosarcoma of the breast: A case report.

Combined Mammographic Breast Density and Breast Arterial Calcification as an Incremental Predictor of Coronary Artery Disease.

Background: Contemporary risk calculators underestimate coronary artery disease (CAD) risk in women. Breast arterial calcification (BAC) associates with CAD. Low breast density (BD) (greater breast adipose tissue) associates with cardiometabolic disease. Both are readily identifiable on screening mammography. We sought to evaluate the association between the combined features of BD, BAC, and CAD. Methods: We retrospectively studied women with clinically indicated mammography and contemporaneous coronary computed tomography angiography. CAD risk was estimated by CAD Consortium Scoring (CCS;>15% high risk). BD was visually assessed by four-level Breast Imaging-Reporting and Data System (BI-RADS) (low:BI-RADS A-B, high:BI-RADS C-D). BAC was visually assessed as present/absent. CAD was categorized as presence/absence of coronary artery plaque. Results are presented with odds ratio (OR) and [95% confidence intervals], and area under the curve (AUC). Results: In 153 patients (age 62 ± 10), low BD (67%) and BAC presence (24%) were both associated with CAD, respectively: OR: 3.21 [1.58-6.60], p = 0.001, and OR: 4.36 [1.58-12.00], p = 0.004. CAD proportion in low BD (68.9%) and BAC (42.9%) was lower than with combined low BD+BAC positive (89.7%). Compared with (high BD+BAC negative), the presence of (low BD+BAC positive) associated with CAD independent of modifiable (OR: 9.12 [2.44-45.83], p = 0.002) and nonmodifiable (OR: 4.87 [1.22-25.02], p = 0.035) risk factors. CCS >15% was seen in 33%. Significant incremental value was seen with the addition of BD/BAC status to CCS (AUC 0.64 versus 0.73, p = 0.004). Conclusions: Mammographic BAC and low BD, both alone and combined, associate with CAD, and improve risk prediction beyond standard coronary risk estimation. Standardized reporting of these features may provide benefit and should be tested in prospective screening studies.

Predicting variant histology in bladder cancer: the role of multiparametric MRI and vesical imaging-reporting and data system (VI-RADS).

(1) To evaluate the diagnostic performance of the VI-RADS score in detecting muscle invasion in variant urothelial carcinomas (VUC). (2) To identify spesific MRI features that may serve as predicting for VUC. Two hundred four patients who underwent TUR-B and/or radical cystectomy and a bladder mpMRI scan within three months prior to the procedure were retrospectively enrolled. The tumors were divided into two histological cohorts: pure urothelial carcinoma (PUC) and VUC. Various MRI features, including largest tumor diameter, long-to-short axis ratio, morphology, heterogeneous signal intensity (SI), presence of necrosis, and normalized ADC (ADCn) value, were analyzed. The diagnostic performance of the VI-RADS score in predicting muscle invasion was calculated using a cut-off point of ≥ 4 in both cohorts. Univariate logistic regression were also performed to identify MRI features that predict VUC. Inter-reader agreement was assessed with the weighted kappa coefficient. Our study identified several MRI features significantly associated with VUC, including heterogeneous SI on T2-weighted images (OR: 3.055; 95% CI: 1.312-7.112; p < 0.001), dynamic contrast enhancement images (OR: 2.935; 95% CI: 1.263-6.821; p < 0.001), and the presence of necrosis (OR: 3.575; 95% CI: 1.798-7.107; p < 0.001). Additionally, ADCn values were significantly lower in the VUC cohort (p = 0.003). The VI-RADS score demonstrated high diagnostic performance across both VUC and PUC cohorts, with sensitivity ranging from 94.4 to 86.8% (reader 1) and 94.2-82.2% (reader 2), specificity ranging from 100 to 94.6% (reader 1) and 100-94% (reader 2), and accuracy ranging from 96 to 90.6% (reader 1) and 96-88.2% (reader 2). The area under the curve (AUC) ranged between 0.972 and 0.972 (reader 1) and 0.838-0.781 (reader 2). No significant differences in diagnostic performance were observed between readers or cohorts (p > 0.05), and inter-reader agreement for VI-RADS scores was excellent for both cohorts. VI-RADS score can be used with high performance to detect muscle invasion in VUC, regardless of reader experience. Additionally, specific MRI features such as heterogeneous SI, the presence of necrosis, and ADCn values can serve as potential predictors of VUC.

Adherence to Annual Lung Cancer Screening and Rates of Cancer Diagnosis

Adherence to annual lung cancer screening (LCS) is a proposed quality metric for LCS programs, but data linking annual adherence to lung cancer outcomes are lacking. To investigate annual LCS adherence rates across 2 subsequent LCS rounds among adults undergoing baseline LCS and examine the association of adherence with lung cancer diagnosis rates. This retrospective cohort study included adults aged 55 to 75 years who formerly or currently smoked and underwent baseline LCS between January 1, 2015, and December 31, 2018, across 5 US health care systems in the Population-Based Research to Optimize the Screening Process-Lung Consortium. Participants with missing Lung Computed Tomography Screening Reporting & Data System scores or a lung cancer diagnosis prior to LCS initiation were excluded. Data were analyzed from October 2023 to October 2024. For negative baseline screening results, T1 and T2 screening adherence was defined as chest computed tomography (CT) between 10 and 18 months and 22 and 30 months after baseline, respectively. For positive baseline screening results, T1 and T2 adherence was defined as chest CT between 11 and 21 months and 28 and 36 months after baseline, respectively. The main outcomes were annual T1 and T2 LCS adherence rates and associations between T1 and T2 screening adherence; annual incident lung cancer diagnoses in rounds T0 (0-12 months after baseline), T1 (>12 to 24 months after baseline), and T2 (>24 to 36 months after baseline); and cancer stage distribution. A total of 10 170 individuals received baseline LCS (median age, 65 years [IQR, 60-69 years]; 5415 [53.2%] male). During round T1, 6141 of 10 033 eligible patients (61.2% [95% CI, 60.2%-62.2%]) were adherent, and during round T2, 5028 of 9966 eligible patients (50.5% [95% CI, 49.5%-51.4%]) were adherent. T1 adherence was significantly associated with T2 adherence (adjusted relative risk, 2.40; 95% CI, 2.06-2.79). Across 36 months of follow-up, 279 patients (2.7%; 95% CI, 2.4%-3.1%) were diagnosed with lung cancer. Incident lung cancer diagnosis rates were 1.3% (95% CI, 1.1%-1.6%), 0.7% (95% CI, 0.5%-0.8%), and 0.8% (95% CI, 0.6%-0.9%) during rounds T0, T1, and T2, respectively. Lung cancer diagnosis rates were higher among individuals who were LCS adherent vs nonadherent during both rounds T1 (59 of 6141 [1.0%; 95% CI, 0.7%-1.2%] vs 8 of 3892 [0.2%; 95% CI, 0.1%-0.4%]; P < .001) and T2 (63 of 5028 [1.3%; 95% CI, 1.0%-1.6%] vs 12 of 4938 [0.2%; 95% CI, 0.1%-0.4%]; P < .001). A greater proportion of early-stage lung cancers were diagnosed among individuals adherent to screening at T2 compared with those who were not (46 of 63 [73.0%] vs 3 of 12 [25.0%]; P = .006). In this multicenter cohort study of adults undergoing LCS, screening adherence was associated with increased overall and early-stage lung cancer detection rates; however, adherence decreased annually after baseline screening, suggesting that it is an important LCS quality metric.

Water mite diversity from southwestern Türkiye through the lens of the DNA barcodes, with the description of one new species (Acari, Hydrachnidia)

This study presents the molecular and morphological results from an analysis of water mites collected in southwestern Türkiye. 83 COI barcodes are provided, clustered into 40 BINs, with 23 BINs being unique and deposited for the first time in the Barcode of Life Data Systems (BOLD). The first DNA barcodes for eight water mite species are uploaded into the BOLD database. In total, 34 water mite species were identified and one of them, Iranothyas marismortui (Gerecke, 1999) is newly reported from Türkiye. Iranothyas alhajarica Pešić, Gerecke &amp; Smit, 2009 is excluded from the fauna of Türkiye. Sperchon fundamentalis Bader &amp; Sepasgozarian, 1980, a species previously synonymized with S. glandulosus Koenike, 1886 is resurrected as a valid species. One species, Atractides turani Pešić, Zawal, Gülle &amp; Smit, sp. nov. (Hygrobatidae), is described as new to science.

FAIR Digital Object Assessment and Acceleration Approaches at the World Data System

FAIR Digital Object Assessment and Acceleration Approaches at the World Data System

Determinant Factors Influencing Part-time Students Withdrawal in SPACE, UTM

One of the significant problems is student withdrawal from higher education, which harms institutional effectiveness, students' career paths, and national education policies. Even though much work has been done on student attrition, most of the studies focus on the full-time students with little emphasis on the part-time students. This gap is significant because part-time students are distinct in their challenges, which include work-study balance, financial constraints, and limited institutional support. This study aims to fill this gap by exploring the factors leading to part-time student withdrawal in the School of Professional and Continuing Education (SPACE) of University Technology Malaysia (UTM), thereby contributing to a better understanding of student retention strategies. This research used a descriptive research design to explore the factors that lead to student withdrawal at SPACE, UTM. The study participants were 749, consisting of 369 students who had left the institution and 380 graduates who had finished their programs. The sample was taken from institutional records, specifically, the university system's data, which comprised part-time bachelor's degree students registered between 2010 and 2022. The findings indicate that both internal and external factors contribute to student withdrawal. However, external factors were identified as the primary cause, encompassing health-related issues, financial constraints, bereavement, program suitability, and personal circumstances. In contrast, internal factors accounted for only a tiny proportion of student withdrawals, suggesting that external pressures play a more significant role in students' decisions to discontinue their studies.

Negative Predictive Value of a Prostate MRI in Black Men: Implications for Biopsy Decision-Making.

Negative Predictive Value of a Prostate MRI in Black Men: Implications for Biopsy Decision-Making.

Utility of Combining Prostate Health Index and Magnetic Resonance Imaging for the Diagnosis of Prostate Cancer.

In this study, we compared the prostate health index (PHI) and the Prostate Imaging Reporting and Data System (PI-RADS) before prostate biopsy and evaluated the utility of combining them in the diagnosis of prostate cancer (PC). Between November 2021 and March 2023, 105 patients admitted to Gunma University Hospital for prostate biopsy after undergoing both MRI and PHI measurements were enrolled in this study. We investigated the diagnostic impacts of [-2]proPSA related indexes on clinically significant PC (csPC) and the complementary effects of PHI and PI-RADS. The median of PHI was 59.9, and 72 patients (69%) were diagnosed with PC. The receiver operating characteristic (ROC) curve for patients diagnosed with PC indicated an area under the curve (AUC) of 0.816 for PHI and 0.753 for PI-RADS. For PHI, when the sensitivity was 90% and 95%, the specificity was 51.5% and 45.5%, respectively. Both are higher than those of the conventional PSA-related indices. In addition, the ROC curve in patients with a diagnosis of csPC (n = 67) indicated an AUC of 0.793 for PHI and 0.746 for PI-RADS. Furthermore, if biopsy was restricted to patients with PI-RADS ≥ 4 or PHI ≥ 38.1, 21% of unnecessary biopsies could be avoided, with only one (1.5%) patient with csPC being missed. PHI is more available for detecting csPC than PSA and PSA F/T. Moreover, there is a possibility that unnecessary prostate biopsies can be avoided by combining PHI and PI-RADS.

LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning.

Hepatocellular carcinoma (HCC) is often diagnosed using gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI). Standardized reporting according to the Liver Imaging Reporting and Data System (LI-RADS) can improve Gd-MRI interpretation but is rather complex and time-consuming. These limitations could potentially be alleviated using recent deep learning-based segmentation and classification methods such as nnU-Net. The study aims to create and evaluate an automatic segmentation model for HCC risk assessment, according to LI-RADS v2018 using nnU-Net. For this single-center retrospective study, 602 patients at risk for HCC were included, who had dynamic EOB-MRI examinations between 05/2005 and 09/2022, containing ≥ LR-3 lesion(s). Manual lesion segmentations in semantic segmentation masks as LR-3, LR-4, LR-5 or LR-M served as ground truth. A set of U-Net models with 14 input channels was trained using the nnU-Net framework for automatic segmentation. Lesion detection, LI-RADS classification, and instance segmentation metrics were calculated by post-processing the semantic segmentation outputs of the final model ensemble. For the external evaluation, a modified version of the LiverHccSeg dataset was used. The final training/internal test/external test cohorts included 383/219/16 patients. In the three cohorts, LI-RADS lesions (≥ LR-3 and LR-M) ≥ 10mm were detected with sensitivities of 0.41-0.85/0.40-0.90/0.83 (LR-5: 0.85/0.90/0.83) and positive predictive values of 0.70-0.94/0.67-0.88/0.90 (LR-5: 0.94/0.88/0.90). F1 scores for LI-RADS classification of detected lesions ranged between 0.48-0.69/0.47-0.74/0.84 (LR-5: 0.69/0.74/0.84). Median per lesion Sørensen-Dice coefficients were between 0.61-0.74/0.52-0.77/0.84 (LR-5: 0.74/0.77/0.84). Deep learning-based HCC risk assessment according to LI-RADS can be implemented as automatically generated tumor risk maps using out-of-the-box image segmentation tools with high detection performance for LR-5 lesions. Before translation into clinical practice, further improvements in automatic LI-RADS classification, for example through large multi-center studies, would be desirable.

Hybrid manganite-graphene sensor for magnetic field magnitude and direction measurement

In this work, we present a novel hybrid magnetic sensor that combines the unique properties of manganite and graphene to measure both the magnitude and direction of a magnetic field. The sensor consists of a nanostructured manganite film to detect the magnetic field strength and a graphene layer to determine the angle between the magnetic field and the sensor plane. This dual sensor approach increases sensitivity over a wide range of magnetic field strengths and provides directional information, making it ideal for applications such as object positioning and navigation. The sensor design, based on a voltage divider configuration, has been optimized to achieve high sensitivity. Experimental results in pulsed magnetic fields up to 21 T demonstrated the increased sensitivity offered by the graphene-manganite combination. In addition, a measurement system for recording and processing data was developed that enables real-time measurement of the magnetic field magnitude and its orientation.

Enterprise API &amp;amp; Platform Strategy in the era of Agentic AI

This research paper investigates the critical importance of robust API and platform strategies for enterprises adapting to the proliferation of agentic AI, wherein AI systems autonomously execute tasks with limited human intervention. It addresses the imperative of facilitating seamless communication among AI agents, enterprise data systems, and external applications. The research examines the architectural and performance considerations essential for organizations to maintain competitiveness in this rapidly growing technological landscape of agentic AI projected to expand from $5.1 billion in 2024 to $47.1 billion by 2030. Key elements explored include unified data layer APIs, zero-trust authorization models, event-driven orchestration, and latency-sensitive design. Furthermore, the study considers emerging trends such as AI-powered SDKs, self-optimizing API gateways, autonomous API discovery, and ethical AI governance APIs. The findings emphasize that the adoption of modern API and platform architectures, optimization of performance metrics, and adherence to regulatory mandates are paramount for organizations to fully capitalize on the transformative potential of agentic AI. It is posited that enterprises embracing this paradigm shift will achieve a demonstrable competitive advantage, fostering innovation and operational excellence in the AI-driven future.

Multi-Modal Learning: Combining Text, Image, and Audio Data

A significant research field is multi-modal learning, which facilitates the integration of multiple data modalities (text, image), audio or video to enhance model performance across different contexts. The use of complementary information from multiple sources in multi- modal systems can improve decision-making accuracy and robustness, unlike unimodal data systems that rely on a single data type. In this paper, we explore the methodologies and challenges of multi-modal learning, highlighting its potential as a transformative tool for various fields such as health care, education, and autonomous systems. The presentation includes a comprehensive examination of feature extraction techniques, fusion strategies, and modern architectures, along with an exploration of data alignment challenges, missing modalities, computational complexity, etc. By addressing these challenges, multi-modal learning is poised to become a major player in the future of artificial intelligence and data science.