The clinical management of high-risk lesions using image-guided biopsy is challenging. This study aimed to evaluate the rates at which such lesions were upgraded to malignancy and identify possible predictive factors for upgrading high-risk lesions. This retrospective multicenter analysis included 1.343 patients diagnosed with high-risk lesions using an image-guided core needle or vacuum-assisted biopsy (VAB). Only patients managed using an excisional biopsy or with at least one year of documented radiological follow-up were included. For each, the Breast Imaging Reporting and Data System (BI-RADS) category, number of samples, needle thickness, and lesion size were correlated with malignancy upgrade rates in different histologic subtypes. Pearson's chi-squared test, the Fisher-Freeman-Halton test, and Fisher's exact test were used for the statistical analyses. The overall upgrade rate was 20.6%, with the highest rates in the subtypes of intraductal papilloma (IP) with atypia (44.7%; 55/123), followed by atypical ductal hyperplasia (ADH) (38.4%; 144/375), lobular neoplasia (LN) (12.7%; 7/55), papilloma without atypia (9.4%; 58/611), flat epithelial atypia (FEA) (8.7%; 10/114), and radial scars (RSs) (4.6%; 3/65). There was a significant relationship between the upgrade rate and BI-RADS category, number of samples, and lesion size Lesion size was the most predictive factor for an upgrade in all subtypes. ADH and atypical IP showed considerable upgrade rates to malignancy, requiring surgical excision. The LN, IP without atypia, pure FEA, and RS subtypes showed lower malignancy rates when the BI-RADS category was lower and in smaller lesions that had been adequately sampled using VAB. After being discussed in a multidisciplinary meeting, these cases could be managed with follow-up instead of excision.

Objective The purpose of this study was to establish the diffusion-weighted magnetic resonance imaging (DW-MRI) findings of the caudate nucleus and putamen in patients with obsessive-compulsive disorder (OCD) and to obtain new information on the etiopathogenesis of OCD, which is still unclear. Methods The study comprised 20 patients with OCDs and 20 healthy volunteers. In these cases, DW-MRI and diffusion-weighted echo-planar images (DW-EPI) at b600 and b1000 gradient values were taken and the measurements were made using the apparent diffusion coefficient (ADC) maps of each group at b600 and b1000 values from the caudate nucleus and putamen. Results When the DW-MRI examination in patients with OCD was compared with the control group, the mean ADC values in the caudate nucleus and putamen were not found to have statistically significantly changed. In addition, there were no significant differences regarding the right and left caudate nuclei and putamen ADC values at the b600 and b1000 in the patients with OCD or the control group. Conclusion There are still many unknowns about the neurobiology of OCD. When the DW-MRI examination of the patients with OCD was compared with the control group in our study, no significant difference was found between the ADC values of the caudate nucleus and putamen. Further studies are required for this present study on DW-MRI in patients with OCD to be meaningful.

Background Multiple sclerosis (MS) is usually described as an autoimmune disease, although the exact mechanism of the disease remains unknown. There have been studies reporting that venous flow abnormalities may be involved in the pathogenesis of MS or many of the associated clinical manifestations.Objective The aim of this study was to evaluate flow volumes of the middle cerebral artery (MCA), transverse sinus (TS), and cerebral aqueduct using phase contrast magnetic resonance imaging (PC-MRI) in relapsing-remitting MS patients and a control group.Methods We included 34 patients diagnosed by the McDonald criteria, revised in 2017, as well as 15 healthy controls matched by age and sex. The MRI scans were performed using a 1.5-T superconducting scanner. Axial T1-weighted, T2-weighted, and PC-MRI sequences were performed for the quantitative investigation of flow volume measurements. Quantitative analyses of flows were performed using flow analyses program PC-MRI angiography software. A circular region of interest was placed manually into the cerebral aqueduct, bilateral MCA, and TS.Results Flow volumes of the cerebral aqueduct and MCA were not statistically significant between the MS and control groups. The flow volumes of the TS for the patient group were lower than those of the control group, and this difference was statistically significant.Conclusions A reduced TS flow volume in MS patients was noted in the present study when compared with the control group, suggesting a relation between venous pathologies and MS. Further studies are needed to understand whether this relation is causal or epiphenomenal.

Purpose: The present study assesses whether malignant and benign lesions can be distinguished through histogram analysis of non-fat-suppressed T1-weighted and fatsuppressed T2-weighted breast magnetic resonance images (MRIs). Materials and Methods: MRIs of 20 malignant and 20 benign breast lesions were reviewed retrospectively by histogram analysis performed using Osirix V.4.9 software. The regions of interest (ROIs) were drawn manually to include almost the entire lesion, and values from these ROIs were used to calculate gray-level intensity mean, standard deviation, entropy, uniformity, skewness, kurtosis, and percentile values. Results: In non-fat-suppressed T1-weighted images, the minimum, 1st, 3rd, 5th, 10th and 25th percentile values were significantly lower in the malignant lesions than in the benign lesions. The minimum value had sensitivity of 70% and specificity of 63.2%. On the fat-suppressed T2weighted images, skewness was significantly higher while uniformity was significantly lower in malignant lesions than benign lesions. Skewness had 68.4% sensitivity and 60% specificity, and uniformity had 65% sensitivity and 68.4% specificity. Conclusion: The results of this study demonstrated that histogram analysis of non-fat-suppressed T1-weighted and fat-suppressed T2-weighted images can be used to differentiate malignant and benign lesions in breast MRI.

Akademisyen Yayınevi yöneticileri, yaklaşık 30 yıllık yayın tecrübesini, kendi tüzel kişiliklerine aktararak uzun zamandan beri, ticarî faaliyetlerini sürdürmektedir. Anılan süre içinde, başta sağlık ve sosyal bilimler, kültürel ve sanatsal konular dahil 2000’i aşkın kitabı yayımlamanın gururu içindedir. Uluslararası yayınevi olmanın alt yapısını tamamlayan Akademisyen, Türkçe ve yabancı dillerde yayın yapmanın yanında, küresel bir marka yaratmanın peşindedir. Bilimsel ve düşünsel çalışmaların kalıcı belgeleri sayılan kitaplar, bilgi kayıt ortamı olarak yüzlerce yılın tanıklarıdır. Matbaanın icadıyla varoluşunu sağlam temellere oturtan kitabın geleceği, her ne kadar yeni buluşların yörüngesine taşınmış olsa da, daha uzun süre hayatımızda yer edineceği muhakkaktır.

Background: In breast cancer diagnosis, while radiological imaging modalities provide important insights into the structural characteristics of tumors, pathological examinations remain essential for establishing a definitive diagnosis. Methods: This study proposes a Vision Transformer (ViT)-based approach developed by fusing magnetic resonance imaging (MRI) and pathology images from the same patient in breast cancer diagnosis. In the study, models combined with different classifiers were trained using ViT-16 and ViT-32 architectures. Performance of the models was evaluated using accuracy, F1 score, sensitivity, precision, ROC, AUC, and Precision–Recall (PR) curves. Results: The findings show that models multimodal image fusion models outperform single-modality models in accuracy, precision, and sensitivity, demonstrating that the fusion approach is an effective method for breast cancer diagnosis. Specifically, the ViT-B/32 (Fusion) + SVM model proved to be the most successful, achieving 92.53% accuracy and a PR-AP value of 0.9708. Conclusions: These results demonstrate that evaluating radiological and pathological images improves diagnostic accuracy and reliability, and that multimodal image fusion is effective in distinguishing malignant lesions from benign ones.