Search Results (18,584)

Objective: We evaluated the feasibility of a machine-learning (ML) model based on clinical features and radiomics from [¹⁸F]FDG PET/CT images to differentiate between infected and non-infected intracavitary vascular grafts and endografts (iVGEI). Methods: Three ML models were developed: one based on pre-treatment criteria to diagnose a vascular graft infection (“MAGIC-light features”), another using radiomics features from diagnostic [¹⁸F]FDG-PET scans, and a third combining both datasets. The training set included 92 patients (72 iVGEI-positive, 20 iVGEI-negative), and the external test set included 20 iVGEI-positive and 12 iVGEI-negative patients. The abdominal aorta and iliac arteries in the PET/CT scans were automatically segmented using SEQUOIA and TotalSegmentator and manually adjusted, extracting 96 radiomics features. The best-performing models for the MAGIC-light features and PET-radiomics features were selected from 343 unique models. Most relevant features were combined to test three final models using ROC analysis, accuracy, sensitivity, and specificity. Results: The combined model achieved the highest AUC in the test set (mean ± SD: 0.91 ± 0.02) compared with the MAGIC-light-only model (0.85 ± 0.06) and the PET-radiomics model (0.73 ± 0.03). The combined model also achieved a higher accuracy (0.91 vs. 0.82) than the diagnosis based on all the MAGIC criteria and a comparable sensitivity and specificity (0.70 and 1.00 vs. 0.76 and 0.92, respectively) while providing diagnostic information at the initial presentation. The AUC for the combined model was significantly higher than the PET-radiomics model (p = 0.02 in the bootstrap test), while other comparisons were not statistically significant. Conclusions: This study demonstrated the potential of ML models in supporting diagnostic decision making for iVGEI. A combined model using pre-treatment clinical features and PET-radiomics features showed high diagnostic performance and specificity, potentially reducing overtreatment and enhancing patient outcomes. Full article

In high geothermal tunnels (>28 °C), curing temperature critically affects early-age concrete mechanics and durability. Uniaxial compression tests under six curing conditions, combined with CT scanning and machine learning-based crack analysis, were used to evaluate the impacts of curing age, temperature, and fiber content. The test results indicate that concrete exhibits optimal development of mechanical properties under ambient temperature conditions. Specifically, the elastic modulus increased by 33.85% with age in the room-temperature group (RT), by 23.35% in the fiber group (F), and decreased by 26.75% in the varying-temperature group (VT). A Weibull statistical damage-based constitutive model aligned strongly with the experimental data (R² > 0.99). Fractal analysis of CT-derived cracks revealed clear fractal characteristics in the log(Nr)–log(r) curves, demonstrating internal damage mechanisms under different thermal histories. Full article

Computational Thinking (CT) has been an important concept for the computer science education community in the last 20 years. In this work we performed a systematic review of the literature regarding the computational thinking of children from kindergarten to primary school. We compiled a large dataset of one hundred and twenty (120) studies from the literature. Through analysis of these studies, we tried to reveal important insights and draw interesting and valid conclusions. We analyzed various qualitative and quantitative aspects of the studies, including the sample size, the year of publication, the country of origin, the studies’ design and duration, the computational tools used, and so on. An important aspect of the work is to highlight differences between different study designs. We identified a total of 120 studies, with more than half of them (>50%) originating from Asian countries. Most studies (82.5%) conducted some form of intervention, aiming to improve their computational thinking in students. A smaller proportion (17.5%) were assessment studies in which the authors conducted assessments regarding the children’s computational thinking. On average, intervention studies had a smaller number of participants, but differences in duration could not be identified. There was also a lack of large-scale longitudinal studies. Block-based coding (i.e., Scratch) and Plugged and Unplugged activities were observed in high numbers in both categories of studies. CT assessment tools showed great variability. Efforts for standardization and reaching a consensus are needed in this regard. Finally, robotic systems have been found to play a major role in interventions over the last years. Full article

Museums play an essential role in preserving both cultural and natural heritage, safeguarding samples that offer invaluable insights into our history and scientific understanding. The integration of micro-computed tomography (micro-CT) has significantly advanced the study, restoration, and conservation of these priceless objects. This work explores the application of micro-CT across three critical areas of museum practice: sample virtualization, restoration assessment, and the analysis of fossil specimens. Specifically, micro-CT scanning was applied to fossils stored in the G.A.M.P.S. collection (Scandicci, Italy), enabling the creation of highly detailed non-invasive 3D models for digital archiving and virtual exhibitions. At the Opificio delle Pietre Dure in Florence, micro-CT was employed to evaluate fossil bone restoration treatments, focusing on the internal impact of menthol as a consolidant and its effects on the structural integrity of the material. Furthermore, micro-CT was utilized to investigate a sealed bee preserved in its cocoon within a paleosol in Costa Vicentina (Portugal), providing unprecedented insights into its internal anatomy and state of preservation, all while maintaining the integrity of the specimen. The results of this study underscore the versatility of micro-CT as a powerful non-destructive tool for advancing the fields of conservation, restoration, and scientific analysis of cultural and natural heritage. By integrating high-resolution imaging with both virtual and hands-on conservation strategies, micro-CT empowers museums to enhance research capabilities, improve preservation methodologies, and foster greater public engagement with their collections. Full article

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

Large soil pores critically influence water and solute transport in soils. The presence of preferential flow paths created by soil macropores can profoundly impact water quality, underscoring the necessity of accurately assessing the characteristics of these macropores. However, it remains unclear whether variations in macropore structure exist between different altitudes and positions of terraced paddy fields. The primary objective of this research was to utilize X-ray computed tomography (CT) and image analysis techniques to characterize the soil pore structure at both the inner field and ridge positions across different altitude levels (high, medium, and low altitude) within terraced paddy fields. The results indicate that there are significant differences in the distribution of large soil pores at different altitudes, with large pores concentrated in the surface layer (0–10 cm) in low-altitude areas, while in high-altitude areas, the distribution of large pores is more uniform. Additionally, as altitude increases, the porosity of large pores shows an increasing trend. The three-dimensional equivalent diameter and large pore volume are primarily characterized by large pores ranging from 1 to 2 mm and 0 to 5 mm³, respectively, with their morphology predominantly appearing spherical or ellipsoidal. The connectivity of large pores in the surface layer of paddy soil is stronger than that in the bunds. However, this connectivity gradually weakens with increasing soil depth. The findings from this study provide valuable quantitative insights into the unique characteristics of soil macropores that vary according to the altitude and position in terraced paddy fields. Moreover, this study emphasizes the necessity for future research that encompasses a broader range of soil types, altitudes, and terraced paddy locations to validate and further explore the identified relationships between altitude and macropore characteristics. Full article

The gut microbiota of macaques, highly homologous to humans in biological characteristics and metabolic functions, serves as an ideal model for studying the mechanisms of human intestinal diseases and therapeutic approaches. A comprehensive characterization of the macaque gut microbiota provides unique insights into human health and disease. This study employs metagenomic sequencing to assess the gut microbiota of wild M. mulatta brevicaudus across various ages, sexes, and physiological states. The results revealed that the dominant bacterial species in various age groups included Segatella copri and Bifidobacterium adolescentis. The predominant bacterial species in various sexes included Alistipes senegalensis and Parabacteroides (specifically Parabacteroides merdae, Parabacteroides johnsonii, and Parabacteroides sp. CT06). The dominant species during lactation and non-lactation periods were identified as Alistipes indistinctus and Capnocytophaga haemolytica. Functional analysis revealed significant enrichment in pathways such as global and overview maps, carbohydrate metabolism and amino acid metabolism. This study enhances our understanding of how age, sex, and physiological states shape the gut microbiota in M. mulatta brevicaudus, offering a foundation for future research on (1) host–microbiome interactions in primate evolution, and (2) translational applications in human health, such as microbiome-based therapies for metabolic or immune-related disorders. Full article

Background: Computed tomography urography (CTU) is routinely used to evaluate the upper urinary tract in patients with hematuria. CTU may detect incidental findings outside the urinary tract, but it remains unclear if this adds value. This study aimed to develop a deep learning algorithm that automatically segments and selectively visualizes the urinary tract on CTU. Methods: The urinary tract (kidneys, ureters, and urinary bladder) was manually segmented on 2 mm dual-phase CTU slices of 111 subjects. With this dataset, a deep learning-based AI was trained to automatically segment and selectively visualize the urinary tract on CTU scans (including accompanying unenhanced CT scans), which we dub “focused view CTU”. Focused view CTU was technically optimized and tested in 39 subjects with hematuria. Results: The technically optimized focused view CTU algorithm provided complete visualization of 97.4% of kidneys, 80.8% of ureters, and 94.9% of urinary bladders. All urinary tract organs were completely visualized in 66.6% of cases. In these cases (excluding 33.3% of cases with incomplete visualization), focused view CTU intrinsically achieved a sensitivity, specificity, positive predictive value, and negative predictive value of 100.0%, 92.3%, 92.9%, and 100.0% for lesions in the urinary tract compared to unmodified CT, although interrater agreement was moderate (κ = 0.528). All incidental findings were successfully hidden by focused view CTU. Conclusions: Focused view CTU provides adequate urinary tract segmentation in most cases, but further research is needed to optimize the technique (segmentation does not succeed in about one-third of cases). It offers selective urinary tract visualization, potentially aiding in assessing relevance and cost-effectiveness of detecting incidental findings in hematuria patients through a prospective randomized trial. Full article

Background/Objectives: Rib fracture detection holds critical importance in the field of medical image processing. Methods: In this study, two different data augmentation methods, traditional data augmentation (Albumentations) and focused data augmentation (focused augmentation), were compared using computed tomography (CT) images for the detection of rib fractures on YOLOv8n, YOLOv8s, and YOLOv8m models. While the traditional data augmentation method applies general transformations to the entire image, the focused data augmentation method performs specific transformations by targeting only the fracture regions. Results: The model performance was evaluated using the Precision, Recall, mAP@50, and mAP@50–95 metrics. The findings revealed that the focused data augmentation method achieved superior performance in certain metrics. Specifically, analysis on the YOLOv8s model showed that the focused data augmentation method increased the mAP@50 value by 2.18%, reaching 0.9412, and improved the recall value for fracture detection by 5.70%, reaching 0.8766. On the other hand, the traditional data augmentation method achieved better results in overall precision metrics with the YOLOv8m model and provided a slight advantage in the mAP@50 value. Conclusions: The study indicates that focused data augmentation can contribute to achieving more reliable and accurate results in medical imaging applications. Full article

Background/Objectives: To evaluate the importance of image processing in a previously validated model for detecting pancreatic neuroendocrine tumors (PanNETs) and to introduce Image2Radiomics, a new framework that ensures reproducibility of the image processing pipeline and facilitates the deployment of radiomics models. Methods: A previously validated model for identifying PanNETs from CT images served as the reference. Radiomics features were re-extracted using Image2Radiomics and compared to those from the original model using performance metrics. The impact of nine alterations to the image processing pipeline was evaluated. Prediction discrepancies were quantified using the mean ± SD of absolute differences in PanNET probability and the percentage of classification disagreement. Results: The reference model was successfully replicated with Image2Radiomics, achieving a Cohen’s kappa coefficient of 1. Alterations to the image processing pipeline led to reductions in model performance, with AUC dropping from 0.87 to 0.71 when image windowing was removed. Prediction disagreements were observed in up to 45% of patients. Even minor changes, such as switching the library used for spatial resampling, resulted in up to 21% disagreement. Conclusions: Reproducing image processing pipelines remains challenging and limits the clinical deployment of radiomics models. While this study is limited to one model and imaging modality, the findings underscore a common risk in radiomics reproducibility. The Image2Radiomics framework addresses this issue by allowing researchers to define and share complete processing pipelines in a standardized way, improving reproducibility and facilitating model deployment in clinical and multicenter settings. Full article

Background: Transcatheter aortic valve implantation (TAVI) has revolutionized the treatment of severe aortic valve stenosis (AS). Balloon post-dilatation (PD) remains an important procedural step to optimize valve function by resolving incomplete valve expansion, which may lead to paravalvular regurgitation and other potentially adverse effects. There are only limited data on the predictors, incidence, and clinical impact of PD during TAVI. Methods: This retrospective, single-center study analyzed 585 patients who underwent TAVI (2016–2022). Pre-procedural evaluations included transthoracic echocardiography and CT angiography to assess key parameters, including the aortic valve calcium score (AVCS); aortic valve calcium density (AVCd); aortic valve maximal systolic transvalvular flow velocity (AV Vmax); and aortic valve mean systolic pressure gradient (AV MPG). We identified imaging predictors of PD and evaluated associated clinical outcomes by analyzing procedural endpoints (according to VARC-3 criteria) and long-term survival. Results: PD was performed on 67 out of 585 patients, with elevated AV Vmax (OR: 1.424, 95% CI: 1.039–1.950; p = 0.028) and AVCd (OR: 1.618, 95% CI: 1.227–2.132; p = 0.001) emerging as a significant independent predictor for PD in TAVI. Kaplan–Meier survival analysis revealed no significant differences in short- and mid-term survival between patients who underwent PD and those who did not. Interestingly, patients requiring PD exhibited a lower incidence of adverse events regarding major vascular complications, permanent pacemaker implantations and stroke. Conclusions: The study highlights AV Vmax and AVCd as key predictors of PD. Importantly, PD was not associated with increased procedural adverse events and did not predict adverse events in this contemporary cohort. Full article

Lung cancer is one of the most common cancers and the leading cause of cancer-related death worldwide. Despite advancements, the overall survival rate for lung cancer remains between 10% and 20% in most countries. However, recent progress in diagnostic tools and therapeutic strategies has led to meaningful improvements in survival outcomes, highlighting the growing importance of personalized management based on accurate disease assessment. ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) has become essential in the management of lung cancer, serving as a key imaging modality for initial diagnosis, staging, treatment response assessment, and follow-up evaluation. Recent developments in radiomics and artificial intelligence (AI), including machine learning and deep learning, have revolutionized the analysis of complex imaging data, enhancing the diagnostic and predictive capabilities of FDG PET/CT in lung cancer. However, the limitations of FDG, including its low specificity for malignancy, have driven the development of novel oncologic radiotracers. One such target is fibroblast activation protein (FAP), a type II transmembrane glycoprotein that is overexpressed in activated cancer-associated fibroblasts within the tumor microenvironment of various epithelial cancers. As a result, FAP-targeted radiopharmaceuticals represent a novel theranostic approach, offering the potential to integrate PET imaging with radioligand therapy (RLT). In this review, we provide a comprehensive overview of FDG PET/CT in lung cancer, along with recent advances in AI. Additionally, we discuss FAP-targeted radiopharmaceuticals for PET imaging and their potential application in RLT for the personalized management of lung cancer. Full article

Background/objectives: This was a post hoc analysis of safety data across the bivalent respiratory syncytial virus prefusion F (RSVpreF) vaccine clinical trial development program. Methods: Data from eight clinical trials in 46,913 immunocompetent adults who received RSVpreF or placebo were analyzed. Local reactions and systemic events were assessed among non-pregnant ≥18-year-olds (n = 9517); adverse events (AEs) among pregnant and non-pregnant 18–59-year-olds (n = 9238); and vaccine-related AEs among non-pregnant ≥18-year-olds (n = 39,314). Post-marketing data in non-pregnant adults were considered. Results: Local reactions and systemic events were reported more frequently in RSVpreF versus placebo recipients; injection site pain was the most common local reaction (RSVpreF, 18.9%; placebo, 7.4%), and fatigue (23.5%; 18.4%) and headache (19.5%; 15.0%) were the most common systemic events. Percentages of AEs within 1 month after vaccination were similar across groups (RSVpreF, 12.8%; placebo, 13.1%); severe AEs were reported in ≤1.5% of participants. Differences in percentages of individuals reporting vaccine-related AEs between the RSVpreF and placebo groups were <0.2% for all related AEs. Serious AEs throughout the study were reported in ≤14.0% (RSVpreF, 12.6%; placebo, 14.0%). No atrial fibrillation, Guillain-Barré syndrome, or acute polyneuropathy cases were reported. The AE data from post-marketing data sources were consistent with the safety profile from the clinical trial program, with no new safety concerns. Conclusions: Integrated data demonstrated that RSVpreF was well tolerated with a favorable safety profile in non-pregnant and pregnant adults. Ongoing surveillance through real-world use and clinical trial experience continue to support the safety profile of RSVpreF. ClinicalTrials.gov: CT03529773/NCT04071158/NCT04785612/NCT05035212/NCT05096208/NCT05842967/NCT04032093/NCT04424316. Full article

Background/Objectives: Low folate intake before and during pregnancy increases the risk of neural tube defects and other adverse outcomes. Gene variants such as MTHFR 677C>T (rs1801133) may increase risks associated with suboptimal folate intake. Our objective was to use BALB/cJ Mthfr^677C>T mice to evaluate the effects of the TT genotype and low folate diets on embryonic development and MTHFR protein expression in pregnant mice. Methods: Female 677CC (mCC) and 677TT (mTT) mice were fed control (2 mg folic acid/kg (2D)), 1 mg folic acid/kg (1D) and 0.3 mg folic acid/kg (0.3D) diets before and during pregnancy. Embryos and maternal tissues were collected at embryonic day 10.5. Embryos were examined for developmental delays and defects. Methyltetrahydrofolate (methylTHF) and total homocysteine (tHcy) were measured in maternal plasma, and MTHFR protein expression was evaluated in maternal liver. Results: MethylTHF decreased due to the experimental diets and mTT genotype. tHcy increased due to 0.3D and mTT genotype; mTT 0.3D mice had significantly higher tHcy than the other groups. MTHFR expression was lower in mTT liver than mCC. MTHFR protein expression increased due to low folate diets in mCC mice, whereas in mTT mice, MTHFR expression increased only due to 1D. Developmental delays were increased in the litters of mTT mice fed 1D and 0.3D. Conclusions: The Mthfr^677C>T mouse models the effects of the MTHFR 677TT genotype in humans and provides a folate-responsive model for examination of the effects of folate intake and the MTHFR 677C>T variant during gestation. Full article

Background: The majority of parotid gland tumors are benign, e.g., pleomorphic adenoma (PA) and Warthin’s tumor (WT). From a biomedical point of view, oxidative stress is of significant importance due to its established association with the initiation and progression of various types of cancer, including parotid gland cancers. This study aimed to assess whether blood prooxidant–antioxidant markers could aid in diagnosing and guiding surgery for recurrent malignancies after parotid tumor treatment. Methods: We examined patients (n = 20) diagnosed with WT (n = 14) and PA (n = 6) using histopathological verification and computed tomography (CT) who qualified for surgical treatment. Blood samples were taken before the surgery and again 10 days later for biochemical analysis. The activities of the antioxidant enzymes (SOD, CAT and GPx), the non-enzymatic antioxidants (GSH and UA) and oxidative stress markers (MDA and TOS) were determined in the blood. The activities of CK and LDH and the concentrations of Cor and TAS were measured in the serum. Hb and Ht were determined in whole blood. Results: The patients’ SOD, CAT, and GPx activities after surgery did not differ significantly from their preoperative levels. However, following surgery, their serum TOS levels were significantly elevated in all the patients compared to baseline. In contrast, the plasma MDA concentrations were markedly reduced after surgery. Similarly, the GSH concentrations showed a significant decrease postoperatively. No significant changes were observed in the CK and LDH activities, TAS concentrations, or levels of Hb, Ht and Cor following surgery. Conclusions: The surgical removal of salivary gland tumors did not result in a reduction in oxidative stress at 10 days after surgery. Therefore, further studies are needed to determine the effectiveness of endogenous defense mechanisms in counteracting the oxidative stress induced by salivary gland tumors. Full article