Faculty of Medicine, University of Zurich.
Department of Orthopedic Surgery, Balgrist University Hospital.
Invest Radiol. 2018 Nov;53(11):663-672. doi: 10.1097/RLI.0000000000000486.
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.
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.
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%.
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.
本研究旨在评估形态磁共振成像（MRI）分析和基于定量 MRI 的纹理分析（TA）在软骨性骨肿瘤分级中的读者间一致性和诊断准确性。
本回顾性研究经我院伦理委员会批准。纳入 116 例软骨性骨肿瘤的 MRI 扫描（53 例软骨瘤、26 例低度软骨肉瘤、37 例高级别软骨肉瘤）。两位对患者数据不知情的肌肉骨骼放射科医生分别分析了 14 种形态 MRI 特征，包括肿瘤和肿瘤周围特征。此外，两位不同的肌肉骨骼放射科医生以类似的方式分别进行 TA，包括 19 个定量 TA 参数。分别对 MRI 和 TA 参数以及联合模型进行读者间可靠性、单变量、多变量和受试者工作特征分析，以确定软骨性肿瘤分级的独立预测因素和诊断准确性。P 值小于 0.05 被认为具有统计学意义。
两位读者之间，MRI 和 TA 特征的平均kappa 值分别为 0.49（范围，0.08-0.82）和 0.79（范围，0.43-0.99），平均组内相关系数。分级软骨肿瘤的独立形态 MRI 预测因素包括骨髓水肿、软组织肿块、最大肿瘤范围和活跃性骨膜炎，而 TA 预测因素包括短运行高灰度强调、偏度和灰度和运行长度不均匀性。仅使用 MRI 预测因素，良性与恶性、良性与低度软骨病变的鉴别诊断准确率分别为 87.0%和 77.4%，仅使用 TA 预测因素，分别为 89.8%和 89.5%，而使用 MRI 和 TA 预测因素的联合模型，分别为 92.9%和 91.2%。在区分低级别和高级别软骨肉瘤时，没有统计学意义的独立 TA 预测因素，而仅包含 MRI 预测因素的模型诊断准确率为 84.8%。
与形态 MRI 分析相比，纹理分析可提高良性和恶性以及良性和低度软骨病变的鉴别诊断准确性。
