Objectives Our study aimed to develop and validate an efficient ultrasound image‐based radiomic model for determining the Achilles tendinopathy in skiers. Methods A total of 88 feet of skiers clinically diagnosed with unilateral chronic Achilles tendinopathy and 51 healthy feet were included in our study. According to the time order of enrollment, the data were divided into a training set (n = 89) and a test set (n = 50). The regions of interest (ROIs) were segmented manually, and 833 radiomic features were extracted from red, green, blue color channels and grayscale of ROIs using Pyradiomics, respectively. Three feature selection and three machine learning modeling algorithms were implemented respectively, for determining the optimal radiomics pipeline. Finally, the area under the receiver operating characteristic curve (AUC), consistency analysis, and decision analysis were used to evaluate the diagnostic performance. Results By comparing nine radiomics analysis strategies of three color channels and grayscale, the radiomic model under the green channel obtained the best diagnostic performance, using the Random Forest selection and Support Vector Machine modeling, which was selected as the final machine learning model. All the selected radiomic features were significantly associated with the Achilles tendinopathy (P < .05). The radiomic model had a training AUC of 0.98, a test AUC of 0.99, a sensitivity of 0.90, and a specificity of 1, which could bring sufficient clinical net benefits. Conclusions Ultrasound image‐based radiomics achieved high diagnostic performance, which could be used as an intelligent auxiliary tool for the diagnosis of Achilles tendinopathy.

This study aimed to determine promethazine (PMZ) and its metabolites, promethazine sulfoxide (PMZSO) and monodesmethyl-promethazine (Nor1PMZ), in swine muscle, liver, kidney, and fat. A sample preparation method and high-performance liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis were established and validated. The samples were extracted using 0.1% formic acid–acetonitrile and purified with acetonitrile-saturated n-hexane. After concentration by rotary evaporation, the extract was re-dissolved in a mixture of 0.1% formic acid-water and acetonitrile (80:20, v/v). Analysis was performed using a Waters Symmetry C18 column (100 mm × 2.1 mm i.d., 3.5 μm) with 0.1% formic acid–water and acetonitrile as the mobile phase. The target compounds were determined using positive ion scan and multiple reaction monitoring. PMZ and Nor1PMZ were quantified with deuterated promethazine (PMZ-d6) as the internal standard, while PMZSO was quantified using the external standard method. In spiked muscle, liver, and kidney samples, the limits of detection (LOD) and limits of quantification (LOQ) for PMZ and PMZSO were 0.05 μg/kg and 0.1 μg/kg, respectively, while for Nor1PMZ, these values were 0.1 μg/kg and 0.5 μg/kg, respectively. For spiked fat samples, the LOD and LOQ for all three analytes were found to be 0.05 μg/kg and 0.1 μg/kg, respectively. The sensitivity of this proposed method reaches or exceeds that presented in previous reports. The analytes PMZ and PMZSO exhibited good linearity within the range of 0.1 μg/kg to 50 μg/kg, while Nor1PMZ showed good linearity within the range of 0.5 μg/kg to 50 μg/kg, with correlation coefficients (r) greater than 0.99. The average recoveries of the target analytes in the samples varied from 77% to 111%, with the precision fluctuating between 1.8% and 11%. This study developed, for the first time, an HPLC–MS/MS method for the determination of PMZ, PMZSO, and Nor1PMZ in four swine edible tissues, comprehensively covering the target tissues of monitoring object. The method is applicable for monitoring veterinary drug residues in animal-derived foods, ensuring food safety.