Department of Orthopaedic Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Aviation College, Liming Vocational University, Quanzhou, China.
Front Endocrinol (Lausanne). 2024 Jun 4;15:1296047. doi: 10.3389/fendo.2024.1296047. eCollection 2024.
The main objective of this study is to assess the possibility of using radiomics, deep learning, and transfer learning methods for the analysis of chest CT scans. An additional aim is to combine these techniques with bone turnover markers to identify and screen for osteoporosis in patients.
A total of 488 patients who had undergone chest CT and bone turnover marker testing, and had known bone mineral density, were included in this study. ITK-SNAP software was used to delineate regions of interest, while radiomics features were extracted using Python. Multiple 2D and 3D deep learning models were trained to identify these regions of interest. The effectiveness of these techniques in screening for osteoporosis in patients was compared.
Clinical models based on gender, age, and β-cross achieved an accuracy of 0.698 and an AUC of 0.665. Radiomics models, which utilized 14 selected radiomics features, achieved a maximum accuracy of 0.750 and an AUC of 0.739. The test group yielded promising results: the 2D Deep Learning model achieved an accuracy of 0.812 and an AUC of 0.855, while the 3D Deep Learning model performed even better with an accuracy of 0.854 and an AUC of 0.906. Similarly, the 2D Transfer Learning model achieved an accuracy of 0.854 and an AUC of 0.880, whereas the 3D Transfer Learning model exhibited an accuracy of 0.740 and an AUC of 0.737. Overall, the application of 3D deep learning and 2D transfer learning techniques on chest CT scans showed excellent screening performance in the context of osteoporosis.
Bone turnover markers may not be necessary for osteoporosis screening, as 3D deep learning and 2D transfer learning techniques utilizing chest CT scans proved to be equally effective alternatives.
本研究的主要目的是评估放射组学、深度学习和迁移学习方法在分析胸部 CT 扫描中的可能性。另一个目的是结合这些技术与骨转换标志物来识别和筛选骨质疏松症患者。
本研究共纳入了 488 例接受过胸部 CT 和骨转换标志物检测且已知骨密度的患者。使用 ITK-SNAP 软件勾画感兴趣区,使用 Python 提取放射组学特征。训练多个 2D 和 3D 深度学习模型来识别这些感兴趣区。比较这些技术在筛选骨质疏松症患者中的有效性。
基于性别、年龄和β-cross 的临床模型的准确性为 0.698,AUC 为 0.665。利用 14 个选定的放射组学特征的放射组学模型的最大准确性为 0.750,AUC 为 0.739。测试组的结果很有前景:2D 深度学习模型的准确性为 0.812,AUC 为 0.855,而 3D 深度学习模型的表现更好,准确性为 0.854,AUC 为 0.906。同样,2D 迁移学习模型的准确性为 0.854,AUC 为 0.880,而 3D 迁移学习模型的准确性为 0.740,AUC 为 0.737。总体而言,3D 深度学习和 2D 迁移学习技术在胸部 CT 扫描中的应用在骨质疏松症的筛查中表现出了极好的性能。
对于骨质疏松症的筛查来说,骨转换标志物可能不是必需的,因为利用胸部 CT 扫描的 3D 深度学习和 2D 迁移学习技术同样有效。