Eros Montin, Richard Kijowski, Thomas Youm, Riccardo Lattanzi
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The femur and acetabulum were segmented bilaterally and associated radiomics features were extracted from the four MRI contrasts of the Dixon sequence (water-only, fat-only, in-phase, and out-of-phase). A radiologist collected 21 radiological measurements typically used in FAI. The Gini importance was used to define 9 subsets with the most predictive radiomics features and one subset for the most diagnostically relevant radiological measurements. For each subset, 100 Random Forest machine learning models were trained with different data splits and fivefold cross-validation to classify healthy subjects versus FAI patients. The average performance among the 100 models was computed for each subset and compared against the performance of the radiological measurements. One model trained using the radiomics features datasets yielded 100% accuracy in the detection of FAI, whereas all other radiomics features exceeded 80% accuracy. 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引用次数: 0
摘要
股骨髋臼撞击症(FAI)是髋关节疼痛的原因之一,可导致髋关节骨性关节炎。从X光片或磁共振成像(MRI)中获得的放射学测量结果通常用于FAI诊断,但它们需要耗时的人工操作,从而限制了准确性和可重复性。本研究比较了标准放射学测量和从核磁共振成像中自动提取的放射组学特征,以鉴别 FAI 患者和健康受试者。研究人员回顾性收集了 10 名确诊 FAI 患者和 10 名健康受试者的骨盆三维 Dixon MRI 图像。对股骨和髋臼进行双侧分割,并从 Dixon 序列的四种 MRI 对比(纯水、纯脂肪、相位内和相位外)中提取相关的放射组学特征。一名放射科医生收集了 21 项 FAI 典型的放射学测量数据。利用 Gini 重要性定义了 9 个最具预测性的放射组学特征子集和一个最具诊断相关性的放射测量子集。针对每个子集,使用不同的数据分割和五倍交叉验证训练了 100 个随机森林机器学习模型,以对健康受试者和 FAI 患者进行分类。对每个子集计算 100 个模型的平均性能,并与放射学测量结果的性能进行比较。使用放射组学特征数据集训练的一个模型检测 FAI 的准确率为 100%,而所有其他放射组学特征的准确率均超过 80%。放射测量的准确率为 74%,与之前的工作结果一致。这项初步工作的结果首次凸显了放射组学在全自动 FAI 诊断方面的潜力。
Radiomics features outperform standard radiological measurements in detecting femoroacetabular impingement on three-dimensional magnetic resonance imaging
Femoroacetabular impingement (FAI) is a cause of hip pain and can lead to hip osteoarthritis. Radiological measurements obtained from radiographs or magnetic resonance imaging (MRI) are normally used for FAI diagnosis, but they require time-consuming manual interaction, which limits accuracy and reproducibility. This study compares standard radiologic measurements against radiomics features automatically extracted from MRI for the identification of FAI patients versus healthy subjects. Three-dimensional Dixon MRI of the pelvis were retrospectively collected for 10 patients with confirmed FAI and acquired for 10 healthy subjects. The femur and acetabulum were segmented bilaterally and associated radiomics features were extracted from the four MRI contrasts of the Dixon sequence (water-only, fat-only, in-phase, and out-of-phase). A radiologist collected 21 radiological measurements typically used in FAI. The Gini importance was used to define 9 subsets with the most predictive radiomics features and one subset for the most diagnostically relevant radiological measurements. For each subset, 100 Random Forest machine learning models were trained with different data splits and fivefold cross-validation to classify healthy subjects versus FAI patients. The average performance among the 100 models was computed for each subset and compared against the performance of the radiological measurements. One model trained using the radiomics features datasets yielded 100% accuracy in the detection of FAI, whereas all other radiomics features exceeded 80% accuracy. Radiological measurements yielded 74% accuracy, consistent with previous work. The results of this preliminary work highlight for the first time the potential of radiomics for fully automated FAI diagnosis.
期刊介绍:
The Journal of Orthopaedic Research is the forum for the rapid publication of high quality reports of new information on the full spectrum of orthopaedic research, including life sciences, engineering, translational, and clinical studies.