Koch Kevin M, Potter Hollis G, Koff Matthew F
Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Department of Radiology and Imaging, Hospital for Special Surgery, New York, New York, USA.
J Orthop Res. 2025 Jan;43(1):183-191. doi: 10.1002/jor.25970. Epub 2024 Sep 13.
This study applied radiomics to MRI data for automated classification of soft tissue abnormalities near total hip arthroplasty (THA). A total of 126 subjects with 1.5 T MRI of symptomatic THA were included in the analysis. Peri-prosthetic soft tissue regions of interest were manually segmented and classified by an expert radiologist. An established radiomics library was used to extract 96 features from 2D image patches across segmented regions. Logistic regression was employed as the primary radiomic classifier, achieving an average area under curve (AUC) of 0.71 in differentiating tissue classifications spanning normal, infected, and several inflammatory, noninfectious categories. Notably, infection cases were identified with the highest accuracy, attaining an AUC of 0.79. Statement of Clinical Significance: This study demonstrates that radiomics applied to MRI data can effectively automate the classification of soft tissue abnormalities in symptomatic total hip arthroplasty, particularly in differentiating periprosthetic infections.
本研究将放射组学应用于MRI数据，以对全髋关节置换术（THA）附近的软组织异常进行自动分类。分析纳入了126例有症状THA的1.5T MRI检查的受试者。假体周围软组织感兴趣区域由放射科专家手动分割并分类。使用一个已建立的放射组学库从分割区域的二维图像块中提取96个特征。逻辑回归被用作主要的放射组学分类器，在区分正常、感染以及几种炎症性、非感染性组织分类时，平均曲线下面积（AUC）为0.71。值得注意的是，感染病例的识别准确率最高，AUC达到0.79。临床意义声明：本研究表明，将放射组学应用于MRI数据可有效实现有症状全髋关节置换术中软组织异常的自动分类，尤其是在鉴别假体周围感染方面。
