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Background/Objectives: Pancreatic cancer is among the most aggressive malignancies, with poor survival rates. Emerging evidence suggests that body composition, including skeletal muscle mass and adiposity distribution, plays a crucial role in predicting patient outcomes. However, its impact on survival in pancreatic cancer remains incompletely understood. The aim of this systematic review was to assess the correlation between body composition parameters and survival outcomes in pancreatic cancer patients, focusing on overall survival. Methods: A comprehensive literature search was conducted, including three main components: pancreatic cancer, body composition, and survival outcomes. Results: 23 studies were included in this review. The findings indicate that body composition can serve as a predictor of survival in pancreatic cancer patients, with 21 studies reporting a significant correlation. The most frequently observed predictor, with 11 studies reporting, was not a baseline parameter but rather changes in parameters over time during treatment. However, discrepancies remain regarding the extent of predictive power and the relative importance of individual components. Conclusions: Specific body composition parameters hold potential as prognostic indicators of survival in pancreatic cancer patients. However, further research is necessary to establish consistent patterns and to clarify which parameters are most predictive and under what conditions.

Background/Objectives: Super-resolution deep-learning reconstruction (SR-DLR) is an advanced image reconstruction technique, but its effect on dynamic myocardial computed tomography perfusion (CTP) imaging has not been evaluated. This study aimed to examine the impact of SR-DLR on image quality and perfusion parameters in dynamic myocardial CTP. Methods: Thirty-five patients who underwent dynamic myocardial CTP for coronary artery disease assessment were retrospectively analyzed. Two CTP datasets were reconstructed using hybrid iterative reconstruction (HIR) and SR-DLR. Image quality was compared qualitatively and quantitatively, including image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge rise slope (ERS). Equivalence of CT-derived myocardial blood flow (CT-MBF) between two reconstructions was tested using a previously reported 15% equivalence margin. Intra-patient variability of CT-MBF was evaluated using the robust coefficient of variation (rCV). Results: In the qualitative assessment, SR-DLR had significantly higher scores in contrast (4.0 vs. 2.0) and sharpness (4.5 vs. 2.5) compared with HIR (p < 0.001), while contrast scores were similar. In the quantitative assessment, SR-DLR demonstrated significantly lower image noise (19.4 vs. 29.4 HU), and improved SNR (6.1 vs. 4.1), CNR (13.7 vs. 10.9), and ERS (171.0 vs. 135.1 HU/mm) (all p < 0.001). Mean global CT-MBF was comparable (3.15 ± 0.91 mL/g/min for HIR vs. 3.18 ± 0.97 mL/g/min for SR-DLR) and equivalence was confirmed (p = 0.022). SR-DLR significantly reduced rCV compared with HIR (36.0% vs. 41.0%, p < 0.001). Conclusions: SR-DLR enhances image quality in dynamic myocardial CTP while maintaining mean global CT-MBF and reducing intra-patient variability.

This correction addresses several errors identified in the original publication [...].

Background: In computed tomography (CT), automatic exposure control (AEC) determines the tube current and thus the radiation dose based on scout images. We investigated CT dose modulation using two versions of CARE Dose 4D, Siemens AEC software.

Methods: A cylindrical phantom and an anthropomorphic phantom with the upper extremities raised or down were imaged. The CT tube current was determined using two versions of CARE Dose 4D and different scout directions: the posteroanterior scout image alone (PA scout), the lateral scout image alone (Lat scout), and the combination of the PA and Lat scout images (PA + Lat scout). The new version is designed to utilize the Lat image solely for off-center correction when both PA and Lat images are available. Experiments were performed at various vertical positions and with various scout imaging parameters.

Results: The influence of the scout direction on CT dose was demonstrated, with variations depending on the imaging object and software version. The CT dose determined with the PA scout varied according to vertical positioning, presumably due to changes in image magnification. Such effects were small with the Lat scout or PA + Lat scout. Decreasing the tube voltage or tube current in scout imaging affected CT dose modulation with the Lat scout but not with the PA scout. With the PA + Lat scout, the effects of scout parameters were evident using the previous version but minimal using the new version.

Conclusions: Off-center correction in the new version functioned appropriately. Because the behavior of an AEC system is complicated, it is recommended to examine the characteristics of each AEC system under various imaging conditions.

Background/objectives: Alzheimer's disease is the leading cause of dementia, marked by progressive cognitive decline and a severe socioeconomic burden. Early and accurate diagnosis is crucial to enhancing patient outcomes, yet traditional clinical and imaging assessments are often limited in sensitivity, particularly at early stages. This study presents a dual-modal framework that integrates symptom-based clinical data with magnetic resonance imaging (MRI) using machine learning (ML) and deep learning (DL) models, enhanced by explainable AI (XAI).

Methods: Four ML classifiers-K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)-were trained on demographic and clinical features. For stage-wise classification, five DL models-CNN, EfficientNetB3, DenseNet-121, ResNet-50, and MobileNetV2-were applied to MRI scans. Interpretability was incorporated through SHAP and Grad-CAM visualizations.

Results: Random Forest achieves the highest accuracy of 97% on clinical data, while CNN achieves the best overall performance of 94% in MRI-based staging. SHAP and Grad-CAM were used to find clinically relevant characteristics and brain areas, including hippocampal atrophy and ventricular enlargement.

Conclusions: Integrating clinical and imaging data and interpretable AI improves the accuracy and reliability of AD staging. The proposed model offers a valid and clear diagnostic route, which can assist clinicians in making timely diagnoses and adjusting individual treatment.

Objective: Axillary lymph node changes are frequently observed in patients with HIV, yet their radiological characteristics and clinical significance remain underexplored. This study aimed to evaluate the association between axillary lymph node computed tomography (CT) features and clinical markers of immune function, including CD4 lymphocyte count and plasma viral load, in HIV-positive patients. Materials and Methods: In this retrospective study, 113 HIV-positive patients who underwent contrast-enhanced chest CT were included. Patients were stratified by CD4 count (<200, 200-500, >500 cells/μL) and plasma viral load (<100,000 or >100,000 copies/mL). Axillary lymph node parameters-including maximum and minimum diameters, cortical thickness, hilar width, and density (Hounsfield units, HU)-were measured on multiplanar reconstructed CT images. Group differences were assessed using the Kruskal-Wallis and Mann-Whitney U tests, and Spearman's correlation was used to evaluate associations between imaging and laboratory findings. Receiver operating characteristic (ROC) curve analysis identified optimal density thresholds. Results: Lymph node diameters, cortical thickness, and hilar width did not significantly differ between CD4 groups. However, mean lymph node density was higher in patients with CD4 < 200 cells/μL (p = 0.024). A density threshold of 84.5 HU distinguished impaired from preserved immune function (sensitivity 61.1%, specificity 71.2%). Patients with viral load >100,000 copies/mL showed increased lymph node density, minimal diameter, and cortical thickness. Conclusions: Elevated axillary lymph node density correlates with immune suppression and high viral load, suggesting its potential as a non-invasive prognostic imaging biomarker in HIV infection.

Objective: This study aimed to investigate the functional connectivity (FC) of three amygdala subregions-the laterobasal amygdala (LBA), centromedial amygdala (CMA), and superficial amygdala (SFA)-with large-scale brain networks in individuals with schizophrenia (SCZ) compared to healthy controls (HC). Methodology: Resting-state functional magnetic resonance imaging (rs-fMRI) data were obtained from 100 participants (50 SCZ, 50 HC) with balanced age and gender distributions. FC between amygdala subregions and target functional networks was assessed using a region-of-interest (ROI)-to-ROI approach implemented in the CONN toolbox. Result: Connectivity patterns of the LBA, CMA, and SFA differed between SCZ and HC groups. After false discovery rate (FDR) correction (p < 0.05), SCZ patients exhibited significantly increased FC between the left CMA and both the default mode network (DMN) and the visual network (VN). In contrast, decreased FC was observed between the right LBA and the sensorimotor network (SMN) in SCZ compared with HC. Conclusions: These findings reveal novel FC alterations linking amygdala subregions with large-scale networks in schizophrenia. The results underscore the importance of examining the amygdala as distinct functional subregions rather than as a single structure, offering new insights into the neural mechanisms underlying SCZ.

The scientific publishing crisis represents a complex problem, mainly stemming from the "publish or perish" culture that prioritizes quantity over quality, which leads to the proliferation of low-quality research manuscripts and research misconduct, including data fabrication (making up data or results), falsification (manipulating research materials, equipment, or processes, or changing or omitting data or results such that the research is not accurately represented in the research record), or even plagiarism (the appropriation of another person's ideas, processes, results, or words without giving appropriate credit) [...].

Cancer-associated fibroblasts (CAFs) are an abundant and diverse cell population within tumor microenvironments of solid tumors. Multiple subtypes of CAFs, defined by molecular and functional markers, have been described in the literature. CAFs contribute to tumor progression by remodeling the extracellular matrix, promoting immune evasion, and supporting angiogenesis and metastasis. Fibroblast activation protein (FAP) is a transmembrane serine protease minimally expressed in normal adult tissues but significantly upregulated in certain subtypes of CAFs across many solid tumors. High levels of FAP have been associated with poor prognosis in various cancers. FAP has increasingly emerged as a promising target for both imaging and therapy. Multiple FAP-targeting strategies, such as small molecules, monoclonal antibodies, drug conjugates, and radiolabeled ligands, are currently being investigated in preclinical and early clinical settings. This review provides a clinically focused overview of CAFs in the tumor microenvironment, highlighting key fibroblast markers, their associations with prognosis across various tumor types, and their utility in radiologic imaging and targeted therapy. We also discuss the potential of non-FAP fibroblast targeting molecules and the clinical rationale for more selective, subtype-specific strategies. By examining fibroblast biology through a radiologist's lens, we aim to explore the evolving role of stromal targeting in imaging and the treatment of solid tumors.

Background/Objectives: The breast is a highly radiosensitive organ that is directly exposed to ionizing radiation during chest computed tomography (CT) examinations. Excessive radiation exposure increases the risk of radiation-induced malignancies, highlighting the importance of accurate and patient-specific dose estimation. This study aims to estimate the effective radiation dose absorbed by the breast during chest CT examinations using a machine learning (ML)-based personalized prediction approach. Methods: In this retrospective study, a total of 653 female patients who underwent both mammography and chest CT between 2020 and 2024 were included. A structured database was created incorporating demographic and anatomical parameters, including body weight, height, body mass index (BMI), and breast thickness (mm) obtained from mammography, along with dose length product (DLP) values from chest CT scans. Five regression-based ML algorithms-CatBoost, Gradient Boosting, Extra Trees, AdaBoost, and Random Forest-were implemented to predict breast radiation dose. Model performance was evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R²). Results: Among the evaluated models, the CatBoost algorithm optimized with Particle Swarm Optimization (CatBoostPSO) achieved the best overall predictive performance, yielding the lowest MSE (0.3795), MAE (0.3846), and MAPE (4.37%), along with the highest R² value (0.9875). CatBoost and Gradient Boosting models demonstrated predictions most closely aligned with ground truth values, indicating that ensemble-based and dynamically optimized models are particularly effective for breast dose estimation. Conclusions: The proposed machine learning framework enables rapid, accurate, and clinically applicable estimation of breast radiation dose during chest CT examinations. This patient-specific approach has strong potential to support personalized radiation dose monitoring and optimization strategies, contributing to improved radiation safety in clinical practice.