Purpose: Given that early identification of breast cancer type allows for less-invasive therapies, we aimed to develop a machine learning model to discriminate between ductal carcinoma in situ (DCIS) and minimally invasive breast cancer (MIBC). Methods: In this retrospective study, the health records of 420 women who underwent biopsies between 2010 and 2020 to confirm breast cancer were collected. A trained XGBoost algorithm was used to classify cancers as either DCIS or MIBC using clinical characteristics, mammographic findings, ultrasonographic findings, and histopathological features. Its performance was measured against other methods using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, precision, and F1 score. Results: The model was trained using 357 women and tested using 63 women with an overall 420 patients (mean [standard deviation] age, 57.1 [12.0] years). The model performed well when feature importance was determined, reaching an accuracy of 0.84 (95% confidence interval [CI], 0.76–0.91), an AUC of 0.93 (95% CI, 0.87–0.95), a specificity of 0.75 (95% CI, 0.67–0.83), and a sensitivity of 0.91 (95% CI, 0.76–0.94). Conclusion: The XGBoost model, combining clinical, mammographic, ultrasonographic, and histopathologic findings, can be used to discriminate DCIS from MIBC with an accuracy equivalent to that of experienced radiologists, thereby giving patients the widest range of therapeutic options.

In recent years, several types of platelet concentrates have been investigated and applied in many fields, particularly in the musculoskeletal system. Platelet-rich fibrin (PRF) is an autologous biomaterial, a second-generation platelet concentrate containing platelets and growth factors in the form of fibrin membranes prepared from the blood of patients without additives. During tissue regeneration, platelet concentrates contain a higher percentage of leukocytes and a flexible fibrin net as a scaffold to improve cell migration in angiogenic, osteogenic, and antibacterial capacities during tissue regeneration. PRF enables the release of molecules over a longer period, which promotes tissue healing and regeneration. The potential of PRF to simulate the physiology and immunology of wound healing is also due to the high concentrations of released growth factors and anti-inflammatory cytokines that stimulate vessel formation, cell proliferation, and differentiation. These products have been used safely in clinical applications because of their autologous origin and minimally invasive nature. We focused on a narrative review of PRF therapy and its effects on musculoskeletal, oral, and maxillofacial surgeries and dermatology. We explored the components leading to the biological activity and the published preclinical and clinical research that supports its application in musculoskeletal therapy. The research generally supports the use of PRF as an adjuvant for various chronic muscle, cartilage, and tendon injuries. Further clinical trials are needed to prove the benefits of utilizing the potential of PRF.

Background Timely diagnosis of meniscus injuries is key for preventing knee joint dysfunction and improving patient outcomes because it decreases morbidity and facilitates treatment planning. Purpose To train and evaluate a deep learning model for automated detection of meniscus tears on knee magnetic resonance imaging (MRI). Study type Bicentric retrospective study. Subjects In total, 584 knee MRI studies, divided among training (n = 234), testing (n = 200), and external validation (n = 150) data sets, were used in this study. The public data set MRNet was used as a second external validation data set to evaluate the performance of the model. Sequence A 3 T, coronal, and sagittal images from T1‐weighted proton density (PD) fast spin‐echo (FSE) with fat saturation and T2‐weighted FSE with fat saturation sequences. Assessment The detection system for meniscus tear was based on the improved YOLOv4 model with Darknet‐53 as the backbone. The performance of the model was also compared with that of three radiologists of varying levels of experience. The determination of the presence of a meniscus tear from surgery reports was used as the ground truth for the images. Statistical Tests Sensitivity, specificity, prevalence, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic curve were used to evaluate the performance of the detection model. Two‐way analysis of variance, Wilcoxon signed‐rank test, and Tukey's multiple tests were used to evaluate differences in performance between the model and radiologists. Results The overall accuracies for detecting meniscus tears using our model on the internal testing, internal validation, and external validation data sets were 95.4%, 95.8%, and 78.8%, respectively. One radiologist had significantly lower performance than our model in detecting meniscal tears (accuracy: 0.9025 ± 0.093 vs. 0.9580 ± 0.025). Data Conclusion The proposed model had high sensitivity, specificity, and accuracy for detecting meniscus tears on knee MRIs. Evidence Level 3 Technical Efficacy Stage 2