Purpose: To assess the feasibility of grading soft tissue sarcomas (STSs) using MRI features (radiomics). Materials and Methods: MRI (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. Results: Intermediate-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*105versus 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). Conclusion: Good accuracy and AUC could be obtained using only few Radiomic features, belonging to the FOS class. Level of Evidence: 4. Technical Efficacy: Stage 2. J. Magn. Reson. Imaging 2018;47:829–840.

Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions

Corino, Valentina D. A.;Montin, Eros;Mainardi, Luca
2018-01-01

Abstract

Purpose: To assess the feasibility of grading soft tissue sarcomas (STSs) using MRI features (radiomics). Materials and Methods: MRI (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. Results: Intermediate-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*105versus 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). Conclusion: Good accuracy and AUC could be obtained using only few Radiomic features, belonging to the FOS class. Level of Evidence: 4. Technical Efficacy: Stage 2. J. Magn. Reson. Imaging 2018;47:829–840.
2018
intensity-based features; radiomics; sarcoma grading; soft tissue sarcomas; texture features; Radiology, Nuclear Medicine and Imaging
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1052456
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