Background/Aim. Breast cancer is a leading global health concern. Contrast-enhanced mammography (CEM) presents a promising advancement in early breast cancer detection, excelling in sensitivity, specificity, and cost-effectiveness. The aim of the study was to assess the overall diagnostic efficacy of CEM in breast cancer screening, particularly in distinguishing benign from malignant lesions in dense breasts. Methods. A two-year retrospective study was conducted at the Center for Radiology, including a total of 279 women undergoing CEM following standard mammography. Results. CEM demonstrated high sensitivity (92.4%), specificity (75.1%), and a noteworthy negative predictive value of 97.0%, significantly reducing unnecessary biopsies. Conclusion. CEM is a valuable screening tool, offering enhanced diagnostic capabilities and the potential to reduce the number of unnecessary biopsies. It is particularly beneficial for patients with dense breasts or inconclusive traditional mammography results.

Introduction. Breast cancer stands as the predominant form of cancer diagnosed in women worldwide. In the Republic of Serbia, breast cancer held the top position in 2020, accounting for 22.6% of all cancer cases. Early diagnosis of the disease can lead to favorable prognosis and high survival rates. National and opportunistic screening aim to conduct preventive mammography examinations in women aged 50-69, with vacuumassisted biopsy guided by tomosynthesis being performed when necessary as a diagnostic procedure. Material and Methods. The vacuum-assisted tomosynthesis guided breast procedure typically takes half as the time of a stereotaxic biopsy. The procedure begins with planning the approach to the lesion, contingent up on its localization within the breast. Results. The first vacuum-assisted biopsy guided by tomosynthesis in Serbia was conducted at the Radiology Center of the Clinical Center of Vojvodina in December 2020, utilizing Selenia Hologic mammography unit. Between December 2020 and June 2023, a total of 161 tomosynthesis-guided VABs were performed in 160 patients, with one patient undergoing the procedure on both breasts. Conclusion. Most authors report using the vacuum sampling technique during the vacuum-assisted biopsy guided by tomosynthesis. Given the potential of complete removal of the lesion with this method, it is standard practice to place a marker at the biopsy site. The increasing incidence of breast cancer is observed globally, and it is attributed to advancements in standards of living and lifestyle changes.

Background/Objectives: Crohn’s disease (CD) requires precise, noninvasive monitoring to guide therapy and support treat-to-target management. Magnetic resonance enterography (MRE), particularly diffusion-weighted imaging (DWI), is the preferred cross-sectional technique for assessing small-bowel inflammation. Indices such as the Magnetic Resonance Index of Activity (MaRIA) and its diffusion-weighted variant (DWI MaRIA) are widely used for grading disease activity. This study evaluated whether unsupervised clustering of MRI-derived features can complement these indices by providing more coherent and biologically grounded stratification of disease activity. Materials and Methods: Fifty patients with histologically confirmed CD underwent 1.5 T MRE. Of 349 bowel segments, 84 were pathological and classified using literature-based thresholds (MaRIA, DWI MaRIA) and unsupervised clustering. Differences between inactive, active, and severe disease were analyzed using multivariate analysis of variance (MANOVA), analysis of variance (ANOVA), and t-tests. Mahalanobis distances were calculated to quantify and compare separation between categories. Results: Using MaRIA thresholds, 5, 16, and 63 segments were classified as inactive, active, and severe (Mahalanobis distances 2.60, 4.95, 4.12). Clustering redistributed them into 22, 37, and 25 (9.26, 24.22, 15.27). For DWI MaRIA, 21, 14, and 49 segments were identified under thresholds (3.59, 5.72, 2.85) versus 21, 37, and 26 with clustering (7.40, 16.35, 9.41). Wall thickness dominated cluster-derived separation, supported by diffusion metrics and the apparent diffusion coefficient (ADC). Conclusions: Cluster-derived classification yielded clearer and more biologically consistent separation of disease-activity groups than fixed thresholds, emphasizing its potential to refine boundary definition, enhance MRI-based assessment, and inform future AI-driven diagnostic modeling.

Background and Objectives: The use of a standard chest X-ray has become a routine diagnostic method in daily clinical practice for the evaluation of a wide range of lung diseases. During the COVID-19 pandemic, significant challenges occurred in achieving accurate diagnostics and selecting appropriate therapies for patients with different symptoms of diseases. The aim was to cross-correlate radiological findings and clinical data and to develop models to predict hospitalization status, while evaluating the prognostic importance of the different variables. Materials and Methods: A set of variables including Brixia score, and clinical data: gender, age, hypertension, and diabetes was used to explore their association with patient hospitalization. Four different machine learning (ML) methods (Decision Tree—DT, Logistic Regression—LR, Random Forest—RF and Support Vector Machine—SVM) were used for hospitalization outcome prediction. Results: SVM appeared to be with the highest AUC (0.851), with low sensitivity, while DT was the most balanced in the context of AUC, accuracy, sensitivity, and specificity. Brixia score appeared to be the most important predictor for hospitalization within the group of predictors (gender, age, hypertension and diabetes). Conclusions: All four ML models that used in this study provided “good” prediction capabilities (AUC > 0.8), with the exception of SVM that had low sensitivity, emphasizing Brixia score as the strongest predictor of hospitalization. Application of ML methods have considerable potential in various aspects of medical clinical practice and future studies could potentially indicate the importance of applying the ML model in more precise diagnosis, therapy and prognosis of the patient’s clinical condition.

Background/Objectives: Clinical prediction models may support staged triage in acute pelvic pain, but simplified integer scores can lose information and destabilise point assignments. We evaluated a tiered clinical–ultrasound prediction pathway and the stability of its simplified score within an ultrasound-documented acute-pelvic-pain cohort. Methods: This single-centre retrospective cohort included 4800 encounters in 4320 women. Encounters from 2013 to 2019 formed the development cohort (n = 3360), and patient-separated encounters from 2020 to 2022 formed the temporal-evaluation cohort (n = 1440). M1 contained clinical and laboratory predictors without imaging variables, M2 added ultrasound descriptors, and M3 added cross-sectional-imaging descriptors. Multiple imputation, ridge logistic regression, patient-level cluster bootstrap, calibration, decision-curve analysis, reader reproducibility and score-stability analyses were used. Results: Urgent pathology occurred in 20.0% of development and 18.0% of temporal encounters. Temporal AUC was 0.841 for M1 and 0.845 for M2 (difference 0.004; 95% CI 0.001–0.009). Because inclusion required documented ultrasound, this difference estimates incremental information from the selected ultrasound descriptors within an already ultrasound-documented pathway and does not estimate the pathway-level benefit of performing ultrasound. The locked integer rule classified 2.9% as low risk, with 99.6% sensitivity and 3.5% specificity. Categorisation accounted for 0.057 of the total 0.060 AUC loss, and 798 distinct score cards arose in 800 bootstrap resamples. Conclusions: Continuous M2 retained good temporal discrimination, but the small M1-M2 difference does not establish whether ultrasound improves pathway-level triage or can safely alter subsequent management. The integer score had low rule-out yield and highly variable point assignments; independent evaluation should prioritise local calibration, clinical utility and score-card stability before implementation.