• Corino Valentina D A VDA Department of Electronic, Information, and Bioengineering, Politecnico di Milano, Milan, Italy., Eros Montin, Antonella Messina, Paolo G Casali, Alessandro Gronchi, Alfonso Marchianò, and Luca T Mainardi.
• Department of Electronic, Information, and Bioengineering, Politecnico di Milano, Milan, Italy.
• J Magn Reson Imaging. 2018 Mar 1; 47 (3): 829-840.

PurposeTo assess the feasibility of grading soft tissue sarcomas (STSs) using MRI features (radiomics).Materials And MethodsMRI (echo planar SE, 1.5T) from 19 patients with STSs and a known histological grading, were retrospectively analyzed. The apparent diffusion coefficient (ADC) maps, obtained by diffusion-weighted imaging acquisitions, were analyzed through 65 radiomic features, intensity-based (first order statistics, FOS) and texture (gray level co-occurrence matrix, GLCM; and gray level run length matrix, GLRLM) features. Feature selection (sequential forward floating search) and classification (k-nearest neighbor classifier) were performed to distinguish intermediate- from high-grade STSs. Classification was performed using the three different sub-groups of features separately as well as all the features together. The entire dataset was divided in three subsets: the training, validation and test set, containing, respectively, 60, 30, and 10% of the data.ResultsIntermediate-grade lesions had a higher and less disperse ADC values compared with high-grade ones: most of FOS related to intensity are higher for the intermediate-grade STSs, while FOS related to signal variability were higher in the high grade (e.g., the feature variance is 2.6*105  ± 0.9*105 versus 3.3*105  ± 1.6*105 , P = 0.3). The GLCM features related to entropy and dissimilarity were higher in the high-grade. When performing classification, the best accuracy is obtained with a maximum of three features for each subgroup, FOS features being those leading to the best classification (validation set: FOS accuracy 0.90 ± 0.11, area under the curve [AUC] 0.85 ± 0.16; test set: FOS accuracy 0.88 ± 0.25, AUC 0.87 ± 0.34).ConclusionGood accuracy and AUC could be obtained using only few Radiomic features, belonging to the FOS class.Level Of Evidence4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:829-840.© 2017 International Society for Magnetic Resonance in Medicine.

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