Diagnostic Value of CT- and MRI-Based Texture Analysis and Imaging Findings for Grading Cartilaginous Tumors in Long Bones

Xue-Ying Deng; Hai-Yan Chen; Jie-Ni Yu; Xiu-Liang Zhu; Jie-Yu Chen; Guo-Liang Shao; Ri-Sheng Yu

doi:10.3389/fonc.2021.700204

Diagnostic Value of CT- and MRI-Based Texture Analysis and Imaging Findings for Grading Cartilaginous Tumors in Long Bones

Front Oncol. 2021 Oct 14:11:700204. doi: 10.3389/fonc.2021.700204. eCollection 2021.

Authors

Xue-Ying Deng^{1

2}, Hai-Yan Chen^{1

2}, Jie-Ni Yu³, Xiu-Liang Zhu³, Jie-Yu Chen^{1

2}, Guo-Liang Shao^{1

2}, Ri-Sheng Yu³

Affiliations

¹ Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.
² Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China.
³ Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Abstract

Objective: 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.

Materials and methods: 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.

Results: 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%).

Conclusions: 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.

Keywords: cartilaginous tumors; chondrosarcoma; enchondroma; radiology; texture analysis.