PURPOSE: To study, from a machine learning perspective, the performance of several machine learning classifiers that use texture analysis features extracted from soft-tissue tumors in nonenhanced T1-MRI images to discriminate between malignant and benign tumors. MATERIALS AND METHODS: Texture analysis features were extracted from the tumor regions from T1-MRI images of clinically proven cases of 49 malignant and 86 benign soft-tissue tumors. Three conventional machine learning classifiers were trained and tested. The best classifier was compared to the radiologists by means of the McNemar's statistical test. RESULTS: The SVM classifier performs better than the neural network and the C4.5 decision tree based on the analysis of their receiver operating curves (ROC) and cost curves. The classification accuracy of the SVM, which was 93% (91% specificity; 94% sensitivity), was better than the radiologist classification accuracy of 90% (92% specificity; 81% sensitivity). CONCLUSION: Machine learning classifiers trained with texture analysis features are potentially valuable for detecting malignant tumors in T1-MRI images. Analysis of the learning curves of the classifiers showed that a training data size smaller than 100 T1-MRI images is sufficient to train a machine learning classifier that performs as well as expert radiologists.
PURPOSE: To study, from a machine learning perspective, the performance of several machine learning classifiers that use texture analysis features extracted from soft-tissue tumors in nonenhanced T1-MRI images to discriminate between malignant and benign tumors. MATERIALS AND METHODS: Texture analysis features were extracted from the tumor regions from T1-MRI images of clinically proven cases of 49 malignant and 86 benign soft-tissue tumors. Three conventional machine learning classifiers were trained and tested. The best classifier was compared to the radiologists by means of the McNemar's statistical test. RESULTS: The SVM classifier performs better than the neural network and the C4.5 decision tree based on the analysis of their receiver operating curves (ROC) and cost curves. The classification accuracy of the SVM, which was 93% (91% specificity; 94% sensitivity), was better than the radiologist classification accuracy of 90% (92% specificity; 81% sensitivity). CONCLUSION: Machine learning classifiers trained with texture analysis features are potentially valuable for detecting malignant tumors in T1-MRI images. Analysis of the learning curves of the classifiers showed that a training data size smaller than 100 T1-MRI images is sufficient to train a machine learning classifier that performs as well as expert radiologists.
Authors: Brandon K K Fields; Natalie L Demirjian; Darryl H Hwang; Bino A Varghese; Steven Y Cen; Xiaomeng Lei; Bhushan Desai; Vinay Duddalwar; George R Matcuk Journal: Eur Radiol Date: 2021-04-23 Impact factor: 5.315
Authors: Lorenzo Ugga; Renato Cuocolo; Domenico Solari; Elia Guadagno; Alessandra D'Amico; Teresa Somma; Paolo Cappabianca; Maria Laura Del Basso de Caro; Luigi Maria Cavallo; Arturo Brunetti Journal: Neuroradiology Date: 2019-08-02 Impact factor: 2.804