This study was carried out to investigate the differentiation of mucinous borderline ovarian tumor from mucinous ovarian carcinoma using magnetic resonance imaging.

Background: With the increasing integration of multiparametric MRI (mpMRI) and 68Ga‐PSMA PET/CT into the diagnostic and staging algorithms of prostate cancer (PCa), digital rectal examination (DRE)—a once foundational and cost‐effective physical examination—has gradually become marginalized in daily clinical practice. However, the continued relevance of DRE in the modern imaging era remains uncertain. This study aimed to reassess the diagnostic value of DRE in the context of contemporary imaging tools, particularly mpMRI and PSMA PET/CT. Methods: This retrospective analysis included 164 patients diagnosed with prostate adenocarcinoma via transrectal ultrasound‐guided biopsy. All patients underwent DRE before biopsy, and 151 received mpMRI while 62 underwent PSMA PET/CT. mpMRI findings were reported using PI‐RADS v2.1, and SUVmax values were recorded from PET/CT reports. Radical prostatectomy was performed in 156 patients. The relationship between DRE findings and imaging parameters, biopsy results, and pathological staging was analyzed using multivariate statistics and ROC curve analyses. Results: DRE findings were benign in 100 patients and suspicious for malignancy in 64. Suspicious DRE was significantly associated with higher PI‐RADS scores, greater lesion size, elevated SUVmax, and clinically significant PCa ( p < 0.001 for all). Each one‐point increase in PI‐RADS was associated with a twofold rise in the likelihood of a positive DRE. ROC analyses identified a 13.5 mm lesion size and an SUVmax of 15.1 as predictive thresholds for DRE positivity. Suspicious DRE also correlated with advanced pathological stage (≥ pT3) ( p < 0.001). Conclusion: Despite the rise of advanced imaging techniques, DRE remains a valuable adjunctive tool in identifying high‐risk and advanced PCa. When interpreted alongside mpMRI and PSMA PET/CT findings, DRE provides additional clinical insight, supporting its continued role in risk stratification rather than screening in the modern diagnostic workflow.

Background: Cholelithiasis can lead to significant complications if not diagnosed and treated promptly. Recent advances in deep learning and the improved ability of computer systems to detect clinically significant textural and morphological patterns in magnetic resonance imaging (MRI) can help reduce the time and resources required for the radiological evaluation of the gallbladder and cholelithiasis. Objective: To detect cholelithiasis, a support system with a graphical user interface for magnetic resonance (MR) images of the gallbladder was implemented to reduce the manual effort and time required to identify gallstones. Method: A commonly used deep learning model for pixel-level mask generation and instance segmentation, Mask Region Based Convolutional Neural Network (Mask R-CNN), was modified, trained, and evaluated to provide a robust pipeline for automated analysis. The primary aim was to automatically locate and label the gallbladder in T2-weighted axial MR images to detect gallstones and highlight the visual characteristics of the target region, thereby supporting radiologists. All automation was designed to operate on a single optimal slice instead of the entire volume. While this approach limits generalisability, it offers a practical starting point for method development. This setup reflects a feasibility-oriented design, rather than a comprehensive diagnostic capability. The dataset included 788 axial MR images from different patients. Each image was labeled and segmented by an experienced radiologist to train and test the models at the image level. Results: The proposed model with squeeze and excitation (SE) modification improved classification accuracy, and at the image level, stone detection improved in terms of accuracy, precision, and specificity, although recall and F1 scores slightly decreased. Conclusions: The results show that the modified Mask R-CNN model can detect gallstones with up to 0.89 accuracy, supporting the clinical applicability of the proposed method.

Objectives: To evaluate the magnetic resonance imaging (MRI) features that may help differentiate usual-type endocervical adenocarcinoma (UEA) from cervical squamous cell carcinoma (SCC), with particular emphasis on diffusion-weighted imaging–derived apparent diffusion coefficient (ADC) values and tumor growth patterns. Methods: This retrospective study included 26 patients with histopathologically confirmed UEA and 50 patients with SCC who underwent preoperative pelvic MRI. Quantitative MRI parameters—including tumor size, tumor-to-muscle signal intensity ratios (SIRs) on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and mean ADC values—were analyzed. Qualitative imaging features such as tumor growth pattern, location, uterine corpus invasion, intratumoral cyst formation, hydrometra/hematometra, and lymphadenopathy were also assessed. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic performance of ADC values. Multivariable logistic regression analysis was used to identify independent predictors of UEA. Interobserver agreement was assessed using intraclass correlation coefficients (ICC) and Cohen’s kappa statistics. Results: Mean ADC values were significantly higher in the UEA group than in the SCC group (0.901 × 10 ⁻ ³ mm²/s vs. 0.825 × 10 ⁻ ³ mm²/s, p = 0.008). ROC analysis showed a statistically significant but moderate discriminatory performance of mean ADC for differentiating UEA from SCC (AUC = 0.659; 95% CI: 0.527–0.790; p = 0.024). Using the Youden index, an optimal ADC cut-off value of 0.837 × 10 ⁻ ³ mm²/s yielded a sensitivity of 65.4% and a specificity of 62.0% for identifying UEA. Endophytic growth was more frequently observed in UEA, whereas mixed growth was more common in SCC (p = 0.023). Intratumoral cyst formation was observed exclusively in UEA (p = 0.004). In multivariable analysis, mean ADC value and tumor growth pattern were independent predictors of UEA. Interobserver agreement for quantitative and qualitative MRI assessments was excellent. Conclusion: Higher ADC values, an endophytic growth pattern, and the presence of intratumoral cysts are useful MRI features for differentiating usual-type endocervical adenocarcinoma from cervical squamous cell carcinoma. These imaging characteristics may contribute to improved preoperative diagnostic accuracy and treatment planning.

Objectives: To investigate the control preferences of clinicians working in a regional hospital to prevent contrast agent reactions. Methods: This study investigated through a questionnaire the role preferred by 75 participant specialist physicians working in two local hospitals regarding prevention of contrast agent reactions. The questionnaire form included five different items consisting of modified forms of preferences found in control preferences scale (Degner et al., 1997). These items were as follows: A, I prefer to make a decision as a clinician; B, I prefer to make a decision as a clinician but should get radiologist’s opinion; C, Radiologist and I should make a decision together. The ideas of the two of us are equally important; D, The radiologist should make a decision but after getting my opinion; E, I prefer the radiologist to make a decision. Participants were asked to specify their primary (the most preferred) and secondary (subsequent) preferences. Results: The results of the statistical analysis indicated that females showed more equalitarian approach when compared to males (43.3% versus 15.6%, P < 0.005). Also, the physicians in internal medicine branches showed more equalitarian approach when compared to the physicians in surgical branches (37.8% versus 15.8%, P < 0.01) Conclusions: When we attempt to establish a clinical guideline committee towards preventing reactions to contrast agents, involvement of physicians exhibiting cooperative attitude in the committee will facilitate communication, and thereby improve group performance. Keywords: Control Preferences Scale; Contrast Agent Reactions; Contrast Agent