Objective: To evaluate multiparametric MRI for differentiating benign and malignant soft tissue tumors. Methods: This retrospective study included 67 patients (mean age, 55 years; 18–82 years) with 35 benign and 32 malignant soft tissue tumors. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI)-derived parameters (D, D*, f), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE)-MRI parameters (K^trans, K_ep, V_e, iAUC) were calculated. Myxoid and non-myxoid soft tissue tumors were divided for subgroup analysis. The parameters were compared between benign and malignant tumors. Results: ADC and D were significantly lower in malignant than benign soft tissue tumors (1170 ± 488 vs 1472 ± 349 µm²/s; 1132 ± 500 vs 1415 ± 374 µm²/s; p < 0.05). K^trans, K_ep, V_e, and iAUC were significantly different between malignant and benign soft tissue tumors (0.209 ± 0.160 vs 0.092 ± 0.067 min⁻¹; 0.737 ± 0.488 vs 0.311 ± 0.230 min⁻¹; 0.32 ± 0.17 vs 0.44 ± 0.28; 0.23 ± 0.14 vs 0.12 ± 0.09, p < 0.05, respectively). ADC (0.752), D (0.742), and K_ep (0.817) had high AUCs. Subgroup analysis showed that only K^trans, and iAUC were significantly different in myxoid tumors, while, ADC, D, K^trans, K_ep, and iAUC were significantly different in non-myxoid tumor for differentiating benign and malignant tumors. D, K_ep, and iAUC were the most significant parameters predicting malignant soft tissue tumors. Conclusion: Multiparametric MRI can be useful to differentiate benign and malignant soft tissue tumors using IVIM-DWI and DCE-MRI. Advances in knowledge: 1. Pure tissue diffusion (D), transfer constant (K^trans), rate constant (K_ep), and initial area under time–signal intensity curve (iAUC) can be used to differentiate benign malignant soft tissue tumors. 2. K^trans and iAUC enable differentiation of benign and malignant myxoid soft tissue tumors.

Background: Even though there are several physical tests available for superior labrum anterior posterior lesions, there have been very few reports on their accuracy, and none can be regarded as completely predictive for the presence of a superior labrum anterior posterior lesion in the shoulder joint. Hypothesis: This new clinical test is a useful and accurate technique for detecting superior labral tears in the shoulder joint. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: This test was conducted independently by 2 physicians before any other diagnostic evaluation. In all cases, the glenohumeral joint was investigated first, and the appropriate treatments were performed on the lesion. A protocol was established to evaluate the sensitivity, specificity, and positive and negative predictive values of this new clinical test. The reproducibility of this test was evaluated with a κ coefficient. Results: Sixty-one patients (61 shoulders) were examined with the passive compression test, and all underwent arthroscopic surgery. In 31 patients with a positive passive compression test result, 27 had a superior labrum anterior posterior lesion, and in 30 patients with a negative passive compression test result, 6 had a superior labrum anterior posterior lesion. The sensitivity of the test was 81.8%, and the specificity was 85.7%. The positive predictive value was 87.1%, and the negative predictive value was 80.0%. The κ coefficient was 0.771 between the 2 independent examiners (P < .01). Conclusion: The passive compression test is a useful and accurate technique for predicting superior labral tears of the shoulder joint.

Extra-abdominal desmoid-type fibromatosis (EADTF) is a rare neoplastic condition of monoclonal fibroblastic proliferation characterized by local aggressiveness with a distinct tendency to recur. Although EADTF is a benign disease entity, these tumors have a tendency to infiltrate surrounding normal tissues, making it difficult to completely eliminate them without adjacent healthy tissue injury. Surgical excision of these locally aggressive tumors without clear resection margins often leads to local recurrence. The aim of this thorough review was to assess the current treatment concepts for these rare tumors. A comprehensive search of articles published in the Cochrane Library, MEDLINE (PubMed), and EMBASE databases between January 2008 and February 2023 was conducted. Surgical intervention is no longer the first-line approach for most cases; instead, strategies like active surveillance or systemic therapies are used as initial treatment options. With the exception of EADTFs situated near vital structures, a minimum of 6–12 months of active surveillance is currently advocated for, during which some disease progression may be considered acceptable. Non-surgical interventions such as radiation or cryoablation may be employed in certain patients to achieve local control. The currently preferred systemic treatment options include tyrosine kinase inhibitors, low-dose chemotherapy, and gamma-secretase inhibitors, while hormone therapy is not advised. Nonsteroidal anti-inflammatory drugs are utilized primarily for pain management.

Utilizing “You only look once” (YOLO) v4 AI offers valuable support in fracture detection and diagnostic decision-making. The purpose of this study was to help doctors to detect and diagnose fractures more accurately and intuitively, with fewer errors. The data accepted into the backbone are diversified through CSPDarkNet-53. Feature maps are extracted using Spatial Pyramid Pooling and a Path Aggregation Network in the neck part. The head part aggregates and generates the final output. All bounding boxes by the YOLO v4 are mapped onto the 3D reconstructed bone images after being resized to match the same region as shown in the 2D CT images. The YOLO v4-based AI model was evaluated through precision–recall (PR) curves and the intersection over union (IoU). Our proposed system facilitated an intuitive display of the fractured area through a distinctive red mask overlaid on the 3D reconstructed bone images. The high average precision values (>0.60) were reported as 0.71 and 0.81 from the PR curves of the tibia and elbow, respectively. The IoU values were calculated as 0.6327 (tibia) and 0.6638 (elbow). When utilized by orthopedic surgeons in real clinical scenarios, this AI-powered 3D diagnosis support system could enable a quick and accurate trauma diagnosis.