Magnetic Resonance Imaging–Based Grading of Cartilaginous Bone Tumors: Added Value of Quantitative Texture Analysis

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Abstract

Objectives

The aim of this study was to assess the interreader agreement and diagnostic accuracy of morphologic magnetic resonance imaging (MRI) analysis and quantitative MRI-based texture analysis (TA) for grading of cartilaginous bone tumors.

Materials and Methods

This retrospective study was approved by our local ethics committee. Magnetic resonance imaging scans of 116 cartilaginous bone neoplasms were included (53 chondromas, 26 low-grade chondrosarcomas, 37 high-grade chondrosarcomas). Two musculoskeletal radiologists blinded to patient data separately analyzed 14 morphologic MRI features consisting of tumor and peritumoral characteristics. In addition, 2 different musculoskeletal radiologists separately performed TA including 19 quantitative TA parameters in a similar fashion. Interreader reliability, univariate, multivariate, and receiver operating characteristics analyses were performed for MRI and TA parameters separately and for combined models to determine independent predictors and diagnostic accuracy for grading of cartilaginous neoplasms. P values of 0.05 and less were considered statistically significant.

Results

Between both readers, MRI and TA features showed a mean kappa value of 0.49 (range, 0.08–0.82) and a mean intraclass correlation coefficient of 0.79 (range, 0.43–0.99), respectively. Independent morphological MRI predictors for grading of cartilaginous neoplasms were bone marrow edema, soft tissue mass, maximum tumor extent, and active periostitis, whereas TA predictors consisted of short-run high gray-level emphasis, skewness, and gray-level and run-length nonuniformity. Diagnostic accuracies for differentiation of benign from malignant as well as for benign from low-grade cartilaginous lesions were 87.0% and 77.4% using MRI predictors exclusively, 89.8% and 89.5% using TA predictors exclusively, and 92.9% and 91.2% using a combined model of MRI and TA predictors, respectively. For differentiation of low-grade from high-grade chondrosarcoma, no statistically significant independent TA predictors existed, whereas a model containing MRI predictors exclusively had a diagnostic accuracy of 84.8%.

Conclusions

Texture analysis improves diagnostic accuracy for differentiation of benign and malignant as well as for benign and low-grade cartilaginous lesions when compared with morphologic MRI analysis.

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