Department of Radiology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
J Xray Sci Technol. 2023;31(3):611-626. doi: 10.3233/XST-221352.
This study aims to evaluate the value of applying X-ray and magnetic resonance imaging (MRI) models based on radiomics feature to predict response of extremity high-grade osteosarcoma to neoadjuvant chemotherapy (NAC).
A retrospective dataset was assembled involving 102 consecutive patients (training dataset, n = 72; validation dataset, n = 30) diagnosed with extremity high-grade osteosarcoma. The clinical features of age, gender, pathological type, lesion location, bone destruction type, size, alkaline phosphatase (ALP), and lactate dehydrogenase (LDH) were evaluated. Imaging features were extracted from X-ray and multi-parametric MRI (T1-weighted, T2-weighted, and contrast-enhanced T1-weighted) data. Features were selected using a two-stage process comprising minimal-redundancy-maximum-relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression. Logistic regression (LR) modelling was then applied to establish models based on clinical, X-ray, and multi-parametric MRI data, as well as combinations of these datasets. Each model was evaluated using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI).
AUCs of 5 models using clinical, X-ray radiomics, MRI radiomics, X-ray plus MRI radiomics, and combination of all were 0.760 (95% CI: 0.583-0.937), 0.706 (95% CI: 0.506-0.905), 0.751 (95% CI: 0.572-0.930), 0.796 (95% CI: 0.629-0.963), 0.828 (95% CI: 0.676-0.980), respectively. The DeLong test showed no significant difference between any pair of models (p > 0.05). The combined model yielded higher performance than the clinical and radiomics models as demonstrated by net reclassification improvement (NRI) and integrated difference improvement (IDI) values, respectively. This combined model was also found to be clinically useful in the decision curve analysis (DCA).
Modelling based on combination of clinical and radiomics data improves the ability to predict pathological responses to NAC in extremity high-grade osteosarcoma compared to the models based on either clinical or radiomics data.
本研究旨在评估基于放射组学特征的 X 射线和磁共振成像（MRI）模型在预测肢体高级骨肉瘤对新辅助化疗（NAC）反应中的应用价值。
回顾性收集了 102 例连续患者（训练数据集，n=72；验证数据集，n=30）的资料，这些患者均被诊断为肢体高级骨肉瘤。评估了年龄、性别、病理类型、病变部位、骨破坏类型、大小、碱性磷酸酶（ALP）和乳酸脱氢酶（LDH）等临床特征。从 X 射线和多参数 MRI（T1 加权、T2 加权和对比增强 T1 加权）数据中提取影像学特征。特征采用两阶段最小冗余最大相关性（mRMR）和最小绝对值收缩和选择算子（LASSO）回归进行选择。然后应用逻辑回归（LR）建模，根据临床、X 射线和多参数 MRI 数据以及这些数据集的组合建立模型。使用灵敏度、特异性和 95%置信区间（CI）的接收器操作特征曲线（ROC）下面积（AUC）评估每个模型。
使用临床、X 射线放射组学、MRI 放射组学、X 射线加 MRI 放射组学和所有组合建立的 5 个模型的 AUC 分别为 0.760（95%CI：0.583-0.937）、0.706（95%CI：0.506-0.905）、0.751（95%CI：0.572-0.930）、0.796（95%CI：0.629-0.963）、0.828（95%CI：0.676-0.980）。DeLong 检验显示任意两个模型之间无显著差异（p＞0.05）。联合模型的净重新分类改善（NRI）和综合差异改善（IDI）值均高于临床和放射组学模型，表明其具有更高的性能。在决策曲线分析（DCA）中，该联合模型也被证明具有临床应用价值。
与基于临床或放射组学数据的模型相比，基于临床和放射组学数据的联合模型在预测肢体高级骨肉瘤对 NAC 的病理反应方面具有更好的能力。
