Torres-Velázquez Maribel, Wille Christa M, Hurley Samuel A, Kijowski Richard, Heiderscheit Bryan C, McMillan Alan B
Department of Biomedical Engineering, College of Engineering, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Department of Orthopedics and Rehabilitation, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA.
Skeletal Radiol. 2024 Apr;53(4):637-648. doi: 10.1007/s00256-023-04449-7. Epub 2023 Sep 20.
To determine if MRI-based radiomics from hamstring muscles are related to injury and if the features could be used to perform a time to return to sport (RTS) classification. We hypothesize that radiomics from hamstring muscles, especially T2-weighted and diffusion tensor imaging-based features, are related to injury and can be used for RTS classification.
MRI data from 32 athletes at the University of Wisconsin-Madison that sustained a hamstring strain injury were collected. Diffusion tensor imaging and T1- and T2-weighted images were processed, and diffusion maps were calculated. Radiomics features were extracted from the four hamstring muscles in each limb and for each MRI modality, individually. Feature selection was performed and multiple support vector classifiers were cross-validated to differentiate between involved and uninvolved limbs and perform binary (≤ or > 25 days) and multiclass (< 14 vs. 14-42 vs. > 42 days) classification of RTS.
The combination of radiomics features from all diffusion tensor imaging and T2-weighted images provided the most accurate differentiation between involved and uninvolved limbs (AUC ≈ 0.84 ± 0.16). For the binary RTS classification, the combination of all extracted radiomics offered the most accurate classification (AUC ≈ 0.95 ± 0.15). While for the multiclass RTS classification, the combination of features from all the diffusion tensor imaging maps provided the most accurate classification (weighted one vs. rest AUC ≈ 0.81 ± 0.16).
This pilot study demonstrated that radiomics features from hamstring muscles are related to injury and have the potential to predict RTS.
确定基于MRI的腘绳肌放射组学特征是否与损伤相关，以及这些特征是否可用于进行恢复运动时间（RTS）分类。我们假设，腘绳肌的放射组学特征，尤其是基于T2加权和扩散张量成像的特征，与损伤相关，并且可用于RTS分类。
收集了威斯康星大学麦迪逊分校32名发生腘绳肌拉伤的运动员的MRI数据。对扩散张量成像以及T1加权和T2加权图像进行处理，并计算扩散图。分别从每个肢体的四块腘绳肌以及每种MRI模态中提取放射组学特征。进行特征选择，并对多个支持向量分类器进行交叉验证，以区分受伤侧和未受伤侧肢体，并对RTS进行二元（≤或>25天）和多类别（<14天vs.14 - 42天vs.>42天）分类。
来自所有扩散张量成像和T2加权图像的放射组学特征组合，在区分受伤侧和未受伤侧肢体方面提供了最准确的结果（AUC≈0.84±0.16）。对于二元RTS分类，所有提取的放射组学特征组合提供了最准确的分类（AUC≈0.95±0.15）。而对于多类别RTS分类，来自所有扩散张量成像图的特征组合提供了最准确的分类（加权一对其余类别的AUC≈0.81±0.16）。
这项初步研究表明，腘绳肌的放射组学特征与损伤相关，并且有预测RTS的潜力。
