OBJECTIVES: To objectively identify possible differences in the signal characteristics of benign and malignant soft tissue masses (STM) on magnetic resonance (MR) images by means of texture analysis and to determine the value of these differences for computer-assisted lesion classification. METHOD: Fifty-eight patients with histologically proven STM (benign, n=30; malignant, n=28) were included. STM texture was analyzed on routine T1-weighted, T2-weighted and short tau inversion recovery (STIR) images obtained with heterogeneous acquisition protocols. Fisher coefficients (F) and the probability of classification error and average correlation coefficients (POE+ACC) were calculated to identify the most discriminative texture features for separation of benign and malignant STM. F>1 indicated adequate discriminative power of texture features. Based on the texture features, computer-assisted classification of the STM by means of k-nearest-neighbor (k-NN) and artificial neural network (ANN) classification was performed, and accuracy, sensitivity and specificity were calculated. RESULTS: Discriminative power was only adequate for two texture features, derived from the gray-level histogram of the STIR images (first and 10th gray-level percentiles). Accordingly, the best results of STM classification were achieved using texture information from STIR images, with an accuracy of 75.0% (sensitivity, 71.4%; specificity, 78.3%) for the k-NN classifier, and an accuracy of 90.5% (sensitivity, 91.1%; specificity, 90.0%) for the ANN classifier. CONCLUSION: Texture analysis revealed only small differences in the signal characteristics of benign and malignant STM on routine MR images. Computer-assisted pattern recognition algorithms may aid in the characterization of STM, but more data is necessary to confirm their clinical value.
OBJECTIVES: To objectively identify possible differences in the signal characteristics of benign and malignant soft tissue masses (STM) on magnetic resonance (MR) images by means of texture analysis and to determine the value of these differences for computer-assisted lesion classification. METHOD: Fifty-eight patients with histologically proven STM (benign, n=30; malignant, n=28) were included. STM texture was analyzed on routine T1-weighted, T2-weighted and short tau inversion recovery (STIR) images obtained with heterogeneous acquisition protocols. Fisher coefficients (F) and the probability of classification error and average correlation coefficients (POE+ACC) were calculated to identify the most discriminative texture features for separation of benign and malignant STM. F>1 indicated adequate discriminative power of texture features. Based on the texture features, computer-assisted classification of the STM by means of k-nearest-neighbor (k-NN) and artificial neural network (ANN) classification was performed, and accuracy, sensitivity and specificity were calculated. RESULTS: Discriminative power was only adequate for two texture features, derived from the gray-level histogram of the STIR images (first and 10th gray-level percentiles). Accordingly, the best results of STM classification were achieved using texture information from STIR images, with an accuracy of 75.0% (sensitivity, 71.4%; specificity, 78.3%) for the k-NN classifier, and an accuracy of 90.5% (sensitivity, 91.1%; specificity, 90.0%) for the ANN classifier. CONCLUSION: Texture analysis revealed only small differences in the signal characteristics of benign and malignant STM on routine MR images. Computer-assisted pattern recognition algorithms may aid in the characterization of STM, but more data is necessary to confirm their clinical value.
Authors: Ieva Malinauskaite; Jeremy Hofmeister; Simon Burgermeister; Angeliki Neroladaki; Marion Hamard; Xavier Montet; Sana Boudabbous Journal: Sarcoma Date: 2020-01-07
Authors: Roberta Fusco; Mario Sansone; Salvatore Filice; Guglielmo Carone; Daniela Maria Amato; Carlo Sansone; Antonella Petrillo Journal: J Med Biol Eng Date: 2016-08-31 Impact factor: 1.553