Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea.
Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea.
Eur Radiol. 2021 Sep;31(9):6825-6834. doi: 10.1007/s00330-021-07832-x. Epub 2021 Mar 19.
To develop and validate a combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT.
One hundred sixty-five patients with vertebral compression fractures were allocated to training (n = 110 [62 acute benign and 48 malignant fractures]) and validation (n = 55 [30 acute benign and 25 malignant fractures]) cohorts. Radiomics features (n = 144) were extracted from non-contrast-enhanced CT images. Radiomics score was constructed by applying least absolute shrinkage and selection operator regression to reproducible features. A combined radiomics-clinical model was constructed by integrating significant clinical parameters with radiomics score using multivariate logistic regression analysis. Model performance was quantified in terms of discrimination and calibration. The model was internally validated on the independent data set.
The combined radiomics-clinical model, composed of two significant clinical predictors (age and history of malignancy) and the radiomics score, showed good calibration (Hosmer-Lemeshow test, p > 0.05) and discrimination in both training (AUC, 0.970) and validation (AUC, 0.948) cohorts. Discrimination performance of the combined model was higher than that of either the radiomics score (AUC, 0.941 in training cohort and 0.852 in validation cohort) or the clinical predictor model (AUC, 0.924 in training cohort and 0.849 in validation cohort). The model stratified patients into groups with low and high risk of malignant fracture with an accuracy of 98.2% in the training cohort and 90.9% in the validation cohort.
The combined radiomics-clinical model integrating clinical parameters with radiomics score could predict malignancy in vertebral compression fractures on CT with high discriminatory ability.
• A combined radiomics-clinical model was constructed to predict malignancy of vertebral compression fractures on CT by combining clinical parameters and radiomics features. • The model showed good calibration and discrimination in both training and validation cohorts. • The model showed high accuracy in the stratification of patients into groups with low and high risk of malignant vertebral compression fractures.
开发并验证一种联合放射组学和临床模型，以预测 CT 上椎体压缩性骨折的恶性程度。
将 165 例椎体压缩性骨折患者分配到训练队列（n=110[62 例急性良性和 48 例恶性骨折]）和验证队列（n=55[30 例急性良性和 25 例恶性骨折]）。从非增强 CT 图像中提取放射组学特征（n=144）。通过应用最小绝对收缩和选择算子回归对可重复的特征构建放射组学评分。通过多元逻辑回归分析将显著的临床参数与放射组学评分相结合构建联合放射组学-临床模型。通过内部验证来评估模型的性能。
由两个显著的临床预测因子（年龄和恶性肿瘤病史）和放射组学评分组成的联合放射组学-临床模型在训练组（AUC，0.970）和验证组（AUC，0.948）中均表现出良好的校准（Hosmer-Lemeshow 检验，p>0.05）和区分度。与单独的放射组学评分（AUC，训练队列为 0.941，验证队列为 0.852）或临床预测因子模型（AUC，训练队列为 0.924，验证队列为 0.849）相比，该联合模型的判别性能更高。该模型可将患者分为恶性骨折低风险和高风险组，在训练队列中的准确率为 98.2%，在验证队列中的准确率为 90.9%。
该联合放射组学-临床模型将临床参数与放射组学评分相结合，可对 CT 上椎体压缩性骨折的恶性程度进行高判别能力的预测。
· 构建了一种联合放射组学和临床模型，通过结合临床参数和放射组学特征来预测 CT 上椎体压缩性骨折的恶性程度。
· 该模型在训练组和验证组中均表现出良好的校准和判别能力。
· 该模型在将患者分为恶性和非恶性骨折的低风险和高风险组方面具有较高的准确性。
