医学
无线电技术
放射科
接收机工作特性
核医学
压缩(物理)
曲线下面积
临床实习
曲线下面积
人工智能
内科学
家庭医学
计算机科学
药代动力学
复合材料
材料科学
作者
Shuo Duan,Yichun Hua,Guanmei Cao,Jun‐nan Hu,Wei Cui,Duo Zhang,Shuai Xu,Tianhua Rong,Baoge Liu
标识
DOI:10.1016/j.ejrad.2023.110899
摘要
Differentiating benign from malignant vertebral compression fractures (VCFs) is a diagnostic dilemma in clinical practice. To improve the accuracy and efficiency of diagnosis, we evaluated the performance of deep learning and radiomics methods based on computed tomography (CT) and clinical characteristics in differentiating between Osteoporosis VCFs (OVCFs) and malignant VCFs (MVCFs).We enrolled a total of 280 patients (155 with OVCFs and 125 with MVCFs) and randomly divided them into a training set (80%, n = 224) and a validation set (20%, n = 56). We developed three predictive models: a deep learning (DL) model, a radiomics (Rad) model, and a combined DL_Rad model, using CT and clinical characteristics data. The Inception_V3 served as the backbone of the DL model. The input data for the DL_Rad model consisted of the combined features of Rad and DCNN features. We calculated the receiver operating characteristic curve, area under the curve (AUC), and accuracy (ACC) to assess the performance of the models. Additionally, we calculated the correlation between Rad features and DCNN features.For the training set, the DL_Rad model achieved the best results, with an AUC of 0.99 and ACC of 0.99, followed by the Rad model (AUC: 0.99, ACC: 0.97) and DL model (AUC: 0.99, ACC: 0.94). For the validation set, the DL_Rad model (with an AUC of 0.97 and ACC of 0.93) outperformed the Rad model (with an AUC: 0.93 and ACC: 0.91) and the DL model (with an AUC: 0.89 and ACC: 0.88). Rad features achieved better classifier performance than the DCNN features, and their general correlations were weak.The Deep learnig model, Radiomics model, and Deep learning Radiomics model achieved promising results in discriminating MVCFs from OVCFs, and the DL_Rad model performed the best.
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