Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics. Guangdong Province), 183 Zhongshan Da Dao Xi, Guangzhou, 510630, Guangdong, China.
Zhuhai Precision Medical Center, Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, 519000, Guangdong, China.
Eur Radiol. 2021 Oct;31(10):7913-7924. doi: 10.1007/s00330-021-07748-6. Epub 2021 Mar 30.
To develop and validate a radiomics signature based on magnetic resonance imaging (MRI) from multicenter datasets for preoperative prediction of pathologic response to neoadjuvant chemotherapy (NAC) in patients with osteosarcoma.
We retrospectively enrolled 102 patients with histologically confirmed osteosarcoma who received chemotherapy before treatment from 4 hospitals (68 in the primary cohort and 34 in the external validation cohort). Quantitative imaging features were extracted from contrast-enhanced fat-suppressed T1-weighted images (CE FS T1WI). Four classification methods, i.e., the least absolute shrinkage and selection operator logistic regression (LASSO-LR), support vector machine (SVM), Gaussian process (GP), and Naive Bayes (NB) algorithm, were compared for feature selection and radiomics signature construction. The predictive performance of the radiomics signatures was assessed with the area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis (DCA).
Thirteen radiomics features selected based on the LASSO-LR classifier were adopted to construct the radiomics signature, which was significantly associated with the pathologic response. The prediction model achieved the best performance between good and poor responders with an AUC of 0.882 (95% CI, 0.837-0.918) in the primary cohort. Calibration curves showed good agreement. Similarly, findings were validated in the external validation cohort with good performance (AUC, 0.842 [95% CI, 0.793-0.883]) and good calibration. DCA analysis confirmed the clinical utility of the selected radiomics signature.
The constructed CE FS T1WI-radiomics signature with excellent performance could provide a potential tool to predict pathologic response to NAC in patients with osteosarcoma.
• The radiomics signature based on multicenter contrast-enhanced MRI was useful to predict response to NAC. • The prediction model obtained with the LASSO-LR classifier achieved the best performance. • The baseline clinical characteristics were not associated with response to NAC.
基于多中心数据集的磁共振成像（MRI）开发和验证一种影像组学特征，用于预测骨肉瘤患者新辅助化疗（NAC）前后的病理反应。
我们回顾性纳入了来自 4 家医院（主要队列 68 例，外部验证队列 34 例）的经组织学证实患有骨肉瘤且接受化疗的 102 例患者。从增强脂肪抑制 T1 加权成像（CE FS T1WI）中提取定量影像学特征。比较了 4 种分类方法，即最小绝对收缩和选择算子逻辑回归（LASSO-LR）、支持向量机（SVM）、高斯过程（GP）和朴素贝叶斯（NB）算法，用于特征选择和影像组学特征构建。采用受试者工作特征曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）评估影像组学特征的预测性能。
基于 LASSO-LR 分类器选择的 13 个影像组学特征被用于构建影像组学特征，其与病理反应显著相关。在主要队列中，该预测模型在良好和不良反应者之间的表现最佳，AUC 为 0.882（95%CI，0.837-0.918）。校准曲线显示出良好的一致性。同样，在外部验证队列中也得到了良好的验证结果（AUC，0.842[95%CI，0.793-0.883]）和良好的校准。DCA 分析证实了所选择的影像组学特征的临床实用性。
基于多中心增强 MRI 的影像组学特征构建的特征具有优异的性能，可以为骨肉瘤患者预测 NAC 的病理反应提供一种潜在的工具。
基于多中心对比增强 MRI 的影像组学特征有助于预测 NAC 反应。
使用 LASSO-LR 分类器获得的预测模型表现最佳。
基线临床特征与 NAC 反应无关。
