■开发和验证深度学习影像组学(DLR)模型,该模型使用X射线图像来预测骨质疏松性椎体骨折(OVF)的分类。
■该研究包括942名患者,包括通过X射线检查1076个椎骨,CT,以及三家不同医院的核磁共振成像。根据胸腰椎骨质疏松性骨折评估系统,OVF分为0级、1级或2级。将数据集随机分为四个不同的子集:包含712个样本的训练集,具有178个样本的内部验证集,包含111个样本的外部验证集,和一个由75个样本组成的前瞻性验证集。ResNet-50架构模型用于实现深度迁移学习(DTL),分别在RadImageNet和ImageNet数据集上进行预训练。使用X射线图像提取并整合来自DTL和影像组学的特征。通过最小绝对收缩和选择算子逻辑回归确定最佳融合特征模型。OVF分类的预测能力评估涉及八个机器学习模型,通过采用“一对一休息”策略的受试者工作特性曲线进行评估。Delong测试用于比较优越的RadImageNet模型与ImageNet模型的预测性能。
■在分别对RadImageNet和ImageNet数据集进行预训练之后,特征选择和融合产生了17个和12个融合特征,分别。Logistic回归成为两种DLR模型的最佳机器学习算法。在整个训练组中,内部验证集,外部验证集,和前瞻性验证集,基于RadImageNet数据集的宏观平均曲线下面积(AUC)超过了基于ImageNet数据集的曲线下面积,差异有统计学意义(P<0.05)。利用二进制“一个与一个休息”策略,基于RadImageNet数据集的模型在预测0级方面表现出优异的功效,AUC为0.969,准确度为0.863.预测类别1产生0.945的AUC和0.875的准确度,而对于类别2,AUC和准确度分别为0.809和0.692。
■DLR模型,基于RadImageNet数据集,在预测OVF的分类方面优于ImageNet模型,具有在前瞻性验证集中确认的泛化性。
UNASSIGNED: To develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs).
UNASSIGNED: The study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the \"One-vs-Rest\" strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model.
UNASSIGNED: Following pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P<0.05). Utilizing the binary \"One-vs-Rest\" strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. Predicting Class 1 yielded an AUC of 0.945 and accuracy of 0.875, while for Class 2, the AUC and accuracy were 0.809 and 0.692, respectively.
UNASSIGNED: The DLR model, based on the RadImageNet dataset, outperformed the ImageNet model in predicting the classification of OVFs, with generalizability confirmed in the prospective validation set.