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BACKGROUND/AIM: Craniocervical junction (CCJ) can be involved in inflammatory arthritis. We aimed to define types of CCJ involvement in rheumatoid arthritis (RA), spondyloarthritis (SpA), and psoriatic arthritis (PsA) and compare them with patients without inflammatory arthritides. MATERIALS AND METHODS: In this retrospective analysis, cervical CT or MRIs of patients with RA, SpA, or PsA, taken for any reason between 2010 and 2020, according to ICD-10 codes, were scanned. Demographic data of the patients were recorded. CCJ involvements (atlantoaxial, vertical, or subaxial subluxation, odontoid process involvement) were reevaluated by an experienced radiologist. The control group consisted of consecutive patients without inflammatory arthritis. RESULTS: Exactly 459 patients (204 RA, 200 SpA, and 55 PsA) and 78 patients in the control group were included in the study. CCJ involvement was detected in 101 (49.5%) RA, 53 (26.5%) SpA, 10 (18.2%) PsA, and 4 patients (5.1%) in the control group (p < 0.001). The odontoid process was one of the main targets, especially in RA patients (69 (33.8%)), which was significantly higher than in the SpA, PsA, and control groups. Although vertical subluxation (VS) was numerically higher in the RA and SpA groups compared to the control group, VS-related brainstem compression was relatively uncommon: 6 (2.9%) in RA, 1 (0.5%) in AS, and none in the PsA and control groups. CONCLUSION: CCJ involvement can often be detected in patients with inflammatory arthritis, especially in RA and SpA patients. The odontoid process is the main target of inflammation.

Background and Purpose: To develop and test a decision tree for predicting contrast enhancement quality and shape using precontrast magnetic resonance imaging (MRI) sequences in a large adult‐type diffuse glioma cohort. Methods: Preoperative MRI scans (development/optimization/test sets: n = 31/38/303, male = 17/22/189, mean age = 52/59/56.7 years, high‐grade glioma = 22/33/249) were retrospectively evaluated, including pre‐ and postcontrast T1‐weighted, T2‐weighted, fluid‐attenuated inversion recovery, and diffusion‐weighted imaging sequences. Enhancement prediction decision tree (EPDT) was developed using development and optimization sets, incorporating four imaging features: necrosis, diffusion restriction, T2 inhomogeneity, and nonenhancing tumor margins. EPDT accuracy was assessed on a test set by three raters of variable experience. True enhancement features (gold standard) were evaluated using pre‐ and postcontrast T1‐weighted images. Statistical analysis used confusion matrices, Cohen's/Fleiss' kappa, and Kendall's W. Significance threshold was p <.05. Results: Raters 1, 2, and 3 achieved overall accuracies of.86 (95% confidence interval [CI]:.81‐.90),.89 (95% CI:.85‐.92), and.92 (95% CI:.89‐.95), respectively, in predicting enhancement quality (marked, mild, or no enhancement). Regarding shape, defined as the thickness of enhancing margin (solid, rim, or no enhancement), accuracies were.84 (95% CI:.79‐.88),.88 (95% CI:.84‐.92), and.89 (95% CI:.85‐.92). Intrarater intergroup agreement comparing predicted and true enhancement features consistently reached substantial levels (≥.68 [95% CI:.61‐.75]). Interrater comparison showed at least moderate agreement (group: ≥.42 [95% CI:.36‐.48], pairwise: ≥.61 [95% CI:.50‐.72]). Among the imaging features in the EPDT, necrosis assessment displayed the highest intra‐ and interrater consistency (≥.80 [95% CI:.73‐.88]). Conclusion: The proposed EPDT has high accuracy in predicting enhancement patterns of gliomas irrespective of rater experience. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to evaluate the clinical, imaging and histopathological features of breast cancer in patients aged under 40 years of age. The relationship between radiological characteristics and histopathological features was also investigated. Materials and Methods: The study included 131 patients aged under 40 years, diagnosed pathologically with breast cancer. A retrospective evaluation was made of the imaging and clinicopathological findings and the relationship between pathological and imaging findings was investigated. Results: Most of the cancers were detected from clinical symptoms, especially a palpable mass (76.3%). The most common histological type of tumor was invasive ductal carcinoma and 64.8% of the tumors were high grade tumors. The predominant features were irregular borders (92.4%), microlobulated-angulated contours (43.5%), hypo-homogeneous internal echogenicity (80.9%) on ultrasonography, and the presence of a mass (41.2%) and suspicious microcalcifications (40.2%) on mammography. Magnetic resonance imaging commonly showed mass enhancement (66.7%) with type 2 or 3 dynamic curve (92.6%). High-grade tumors were associated with posterior acoustic enhancement (p: 0.03) while low-grade tumors presented with spiculated margins more than high grade tumors (p: 0.04). Conclusion: Breast cancer in women aged under 40 years usually presents with a self-detected palpable mass and can show different imaging findings according to the histological grade. Ultrasonography is the main modality for the diagnosis of breast cancer in young women, but mammography and magnetic resonance imaging can help in both diagnosis and evaluation of the extent of disease. [ABSTRACT FROM AUTHOR]

Objective: Benign fibroepithelial lesions (BFL) lesions of the breast are various and predominantly benign, although a few can be locally aggressive. Definitive diagnosis of some BFL can be challenging from core needle biopsy (CNB). Radiological findings can help guide the management of the lesions. The aim of this study was to investigate the accuracy rate of CNB results and evaluate the radiological findings of the most common BFL according to the final excision pathology results. The secondary aim was to assess the contribution of the imaging findings to CNB results. Materials and Methods: A retrospective review was made of 266 patients diagnosed with suspicious BFL, conventional fibroadenoma, complex fibroadenoma, cellular fibroadenoma and benign phyllodes tumor (PT). The study included 132 patients who underwent surgical excision. The radiological and histopathological findings were evaluated. Results: While 66 patients were diagnosed with more descriptive results on CNB, the other 66 patients were diagnosed with suspicious BFL. Agreement between CNB and excisional pathology was good, when CNB provided a definite diagnosis. While conventional and complex fibroadenoma were observed to have hypo or normal vascularity, cellular fibroadenoma and PT showed hypervascularity. Oval shaped and homogeneous internal echo pattern were significantly associated with conventional fibroadenoma. A heterogeneous internal echo pattern was seen in complex fibroadenomas and PT. Conclusion: CNB often reaches the correct diagnosis alone when it gives a definite diagnosis. The radiological findings which help in the differentiation of BFL are hypervascularity, oval shape and internal heterogeneity. More accurate results can be obtained when histopathological and radiological findings are evaluated together. [ABSTRACT FROM AUTHOR]

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment., Competing Interests: Conflicts of Interest No conflicts of interest to disclose.

Background: Visually Accessible Rembrandt (Repository for Molecular Brain Neoplasia Data) Images (VASARI) features, a vocabulary to establish reproducible terminology for glioma reporting, have been applied for a decade, but a systematic performance evaluation is lacking., Purpose: Our aim was to conduct a systematic review and meta-analysis of the performance of the VASARI features set for glioma assessment., Data Sources: MEDLINE, Web of Science, EMBASE, and the Cochrane Library were systematically searched until September 26, 2023., Study Selection: Original articles predicting diagnosis, progression, and survival in patients with glioma were included., Data Analysis: The modified Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to evaluate the risk-of-bias. The meta-analysis used a random effects model and forest plot visualizations, if ≥5 comparable studies with a low or medium risk of bias were provided., Data Synthesis: Thirty-five studies (3304 patients) were included. Risk-of-bias scores were medium ( n  = 33) and low ( n  = 2). Recurring objectives were overall survival ( n  = 18) and isocitrate dehydrogenase mutation ( IDH ; n  = 12) prediction. Progression-free survival was examined in 7 studies. In 4 studies (glioblastoma n  = 2, grade 2/3 glioma n  = 1, grade 3 glioma n  = 1), a significant association was found between progression-free survival and single VASARI features. The single features predicting overall survival with the highest pooled hazard ratios were multifocality (hazard ratio = 1.80; 95%-CI, 1.21-2.67; I 2 = 53%), ependymal invasion (hazard ratio = 1.73; 95% CI, 1.45-2.05; I 2 = 0%), and enhancing tumor crossing the midline (hazard ratio = 2.08; 95% CI, 1.35-3.18; I 2 = 52%). IDH mutation-predicting models combining VASARI features rendered a pooled area under the receiver operating characteristic curve of 0.82 (95% CI, 0.76-0.88) at considerable heterogeneity (I 2 = 100%). Combined input models using VASARI plus clinical and/or radiomics features outperformed single data-type models in all relevant studies ( n  = 17)., Limitations: Studies were heterogeneously designed and often with a small sample size. Several studies used The Cancer Imaging Archive database, with likely overlapping cohorts. The meta-analysis for IDH was limited due to a high study heterogeneity., Conclusions: Some VASARI features perform well in predicting overall survival and IDH mutation status, but combined models outperform single features. More studies with less heterogeneity are needed to increase the evidence level., (© 2024 by American Journal of Neuroradiology.)