A preliminary attempt to use radiomic features in the diagnosis of extra-articular long head biceps tendinitis

作者全名："Yin, Lifeng; Kong, Yanggang; Guo, Mingkang; Zhang, Xingyu; Yan, Wenlong; Zhang, Hua"

作者地址："[Yin, Lifeng; Kong, Yanggang; Guo, Mingkang; Zhang, Xingyu; Yan, Wenlong; Zhang, Hua] Chongqing Med Univ, Affiliated Hosp 1, Chongqing, Peoples R China"

通信作者："Zhang, H (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Chongqing, Peoples R China."

来源：MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE

ESI学科分类：　

WOS号：WOS:000912874900001

JCR分区：Q3

影响因子：2

年份：2023

卷号：　

期号：　

开始页：　

结束页：　

文献类型：Article; Early Access

关键词：Machine learning; Radiomic; Long head of biceps tendon; Tendinitis; MRI

摘要："Background This study aims to present a radiomic application in diagnosing the long head of biceps (LHB) tendinitis. Moreover, we evaluated whether machine learning-derived radiomic features recognize LHB tendinitis.Patients and methods A total of 170 patients were reviewed. All LHB tendinitis patients were diagnosed under arthroscopy. Radiomic features were extracted from preoperative magnetic resonance imaging (MRI), and the input dataset was divided into a training set and a test set. For feature selection, the t test and least absolute shrinkage and selection operator (LASSO) methods were used, and random forest (RF) and support vector machine (SVM) were used as machine learning classifiers. The sensitivity, specificity, accuracy, and area under the curve (AUC) of each model's receiver operating characteristic (ROC) curves were calculated to evaluate model performance.Results In total, 851 radiomic features were extracted, with 109 radiomic features extracted using a t test and 20 radiomic features extracted using the LASSO method. The random forest classifier shows the highest sensitivity, specificity, accuracy, and AUC (0.52, 0.92, 0.73, and 0.72).Conclusion The classifier contract by 20 radiomic features demonstrated a good ability to predict extra-articular LHB tendinitis.However because of poor segmentation reliability, the value of Radiomic in LHB tendinitis still needs to be further explored."

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