Deng Xue-Ying, Chen Hai-Yan, Yu Jie-Ni, Zhu Xiu-Liang, Chen Jie-Yu, Shao Guo-Liang, Yu Ri-Sheng
Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.
Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China.
Front Oncol. 2021 Oct 14;11:700204. doi: 10.3389/fonc.2021.700204. eCollection 2021.
To confirm the diagnostic performance of computed tomography (CT)-based texture analysis (CTTA) and magnetic resonance imaging (MRI)-based texture analysis for grading cartilaginous tumors in long bones and to compare these findings to radiological features.
Twenty-nine patients with enchondromas, 20 with low-grade chondrosarcomas and 16 with high-grade chondrosarcomas were included retrospectively. Clinical and radiological information and 9 histogram features extracted from CT, T1WI, and T2WI were evaluated. Binary logistic regression analysis was performed to determine predictive factors for grading cartilaginous tumors and to establish diagnostic models. Another 26 patients were included to validate each model. Receiver operating characteristic (ROC) curves were generated, and accuracy rate, sensitivity, specificity and positive/negative predictive values (PPV/NPV) were calculated.
On imaging, endosteal scalloping, cortical destruction and calcification shape were predictive for grading cartilaginous tumors. For texture analysis, variance, mean, perc.01%, perc.10%, perc.99% and kurtosis were extracted after multivariate analysis. To differentiate benign cartilaginous tumors from low-grade chondrosarcomas, the imaging features model reached the highest accuracy rate (83.7%) and AUC (0.841), with a sensitivity of 75% and specificity of 93.1%. The CTTA feature model best distinguished low-grade and high-grade chondrosarcomas, with accuracies of 71.9%, and 80% in the training and validation groups, respectively; T1-TA and T2-TA could not distinguish them well. We found that the imaging feature model best differentiated benign and malignant cartilaginous tumors, with an accuracy rate of 89.2%, followed by the T1-TA feature model (80.4%).
The imaging feature model and CTTA- or MRI-based texture analysis have the potential to differentiate cartilaginous tumors in long bones by grade. MRI-based texture analysis failed to grade chondrosarcomas.
确认基于计算机断层扫描（CT）的纹理分析（CTTA）和基于磁共振成像（MRI）的纹理分析对长骨软骨肿瘤分级的诊断性能，并将这些结果与放射学特征进行比较。
回顾性纳入29例内生软骨瘤患者、20例低级别软骨肉瘤患者和16例高级别软骨肉瘤患者。评估临床和放射学信息以及从CT、T1加权成像（T1WI）和T2加权成像（T2WI）中提取的9个直方图特征。进行二元逻辑回归分析以确定软骨肿瘤分级的预测因素并建立诊断模型。另外纳入26例患者以验证每个模型。生成受试者操作特征（ROC）曲线，并计算准确率、敏感性、特异性以及阳性/阴性预测值（PPV/NPV）。
在影像学上，骨内膜扇贝样改变、皮质破坏和钙化形态对软骨肿瘤分级具有预测性。对于纹理分析，多变量分析后提取了方差、均值、第1百分位数、第10百分位数、第99百分位数和峰度。为了区分良性软骨肿瘤和低级别软骨肉瘤，影像学特征模型达到了最高准确率（83.7%）和曲线下面积（AUC，0.841），敏感性为75%，特异性为93.1%。CTTA特征模型对低级别和高级别软骨肉瘤的区分效果最佳，在训练组和验证组中的准确率分别为71.9%和80%；T1纹理分析（T1-TA）和T2纹理分析（T2-TA）对它们的区分效果不佳。我们发现影像学特征模型对良性和恶性软骨肿瘤的区分效果最佳，准确率为89.2%，其次是T1-TA特征模型（80.4%）。
影像学特征模型以及基于CT或MRI的纹理分析有潜力对长骨软骨肿瘤进行分级鉴别。基于MRI的纹理分析未能对软骨肉瘤进行分级。
