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This study aimed to investigate which of the two frequently adopted perfusion models better describes the contrast enhanced ultrasound (CEUS) perfusion signal in order to produce meaningful imaging markers with the goal of developing a machine-learning model that can classify perfusion curves as benign or malignant in breast cancer data. Twenty-five patients with high suspicion of breast cancer were analyzed with exponentially modified Gaussian (EMG) and gamma variate functions (GVF). The adjusted R2 metric was the criterion for assessing model performance. Various classifiers were trained on the quantified perfusion curves in order to classify the curves as benign or malignant on a voxel basis. Sensitivity, specificity, geometric mean, and AUROC were the validation metrics. The best quantification model was EMG with an adjusted R2 of 0.60 ± 0.26 compared to 0.56 ± 0.25 for GVF. Logistic regression was the classifier with the highest performance (sensitivity, specificity, Gmean, and AUROC = 89.2 ± 10.7, 70.0 ± 18.5, 77.1 ± 8.6, and 91.0 ± 6.6, respectively). This classification method obtained similar results that are consistent with the current literature. Breast cancer patients can benefit from early detection and characterization prior to biopsy.

Step aerobics is one of the most popular exercises with established benefits to cardiovascular status. This activity is associated with injuries which include patellar or talar chondral lesions, quadriceps tendinopathy, Achilles tendinopathy or tear, shin splints and muscle soreness. The purpose of this case report is to present two step aerobics athletes, suffering from anterior knee pain. We observed that their knee MR examination disclosed only suprapatellar fat pad edema. No other findings were noticed. In addition, we discuss the possible pathogenetic mechanism of this entity which has not been previously reported in the literature.