Department of Medicine, University of Cambridge, Cambridge, UK.
Department of Astronomy, University of Cambridge, Cambridge, UK.
Bone. 2024 Sep;186:117142. doi: 10.1016/j.bone.2024.117142. Epub 2024 Jun 2.
Gaucher disease is one of the most common lysosomal storage disorders. Osteonecrosis is a principal clinical manifestation of Gaucher disease and often leads to joint collapse and fractures. T1-weighted (T1w) modality in MRI is widely used to monitor bone involvement in Gaucher disease and to diagnose osteonecrosis. However, objective and quantitative methods for characterizing osteonecrosis are still limited. In this work, we present a deep learning-based quantification approach for the segmentation of osteonecrosis and the extraction of characteristic parameters. We first constructed two independent U-net models to segment the osteonecrosis and bone marrow unaffected by osteonecrosis (UBM) in spine and femur respectively, based on T1w images from patients in the UK national Gaucherite study database. We manually delineated parcellation maps including osteonecrosis and UBM from 364 T1w images (176 for spine, 188 for femur) as the training datasets, and the trained models were subsequently applied to all the 917 T1w images in the database. To quantify the segmentation, we calculated morphological parameters including the volume of osteonecrosis, the volume of UBM, and the fraction of total marrow occupied by osteonecrosis. Then, we examined the correlation between calculated features and the bone marrow burden score for marrow infiltration of the corresponding image, and no strong correlation was found. In addition, we analyzed the influence of splenectomy and the interval between the age at first symptom and the age of onset of treatment on the quantitative measurements of osteonecrosis. The results are consistent with previous studies, showing that prior splenectomy is closely associated with the fractional volume of osteonecrosis, and there is a positive relationship between the duration of untreated disease and the quantifications of osteonecrosis. We propose this technique as an efficient and reliable tool for assessing the extent of osteonecrosis in MR images of patients and improving prediction of clinically important adverse events.
戈谢病是最常见的溶酶体贮积症之一。骨坏死是戈谢病的主要临床表现，常导致关节塌陷和骨折。磁共振成像（MRI）中的 T1 加权（T1w）模态广泛用于监测戈谢病中的骨骼受累情况，并诊断骨坏死。然而，用于表征骨坏死的客观和定量方法仍然有限。在这项工作中，我们提出了一种基于深度学习的定量方法，用于分割骨坏死并提取特征参数。我们首先基于英国国家戈谢病研究数据库中患者的 T1w 图像，分别构建了两个独立的 U-net 模型，用于分割脊柱和股骨中的骨坏死和不受骨坏死影响的骨髓（UBM）。我们手动描绘了包括骨坏死和 UBM 的分割图，作为训练数据集，来自 364 个 T1w 图像（176 个用于脊柱，188 个用于股骨），然后将训练好的模型应用于数据库中的所有 917 个 T1w 图像。为了量化分割，我们计算了形态学参数，包括骨坏死的体积、UBM 的体积和骨坏死占据总骨髓的比例。然后，我们检查了计算出的特征与对应图像骨髓浸润的骨髓负担评分之间的相关性，没有发现很强的相关性。此外，我们分析了脾切除术和从首次症状出现到开始治疗的时间间隔对骨坏死定量测量的影响。结果与先前的研究一致，表明脾切除术之前与骨坏死的分数体积密切相关，并且未治疗疾病的持续时间与骨坏死的定量测量呈正相关。我们提出这项技术作为一种有效和可靠的工具，用于评估患者的 MRI 图像中骨坏死的程度，并提高对临床上重要不良事件的预测能力。
