OBJECTIVE: To evaluate the feasibility of using radiomic features for differential diagnosis of hepatocellular carcinoma (HCC) and hepatic cavernous hemangioma (HHE). METHODS: Gadoxetate disodium-enhanced magnetic resonance imaging data were collected from a total of 135 HCC and HHE lesions. The radiomic texture features of each lesion were extracted on the hepatobiliary phase images, and the performance of each feature was assessed in differentiation and classification of HCC and HHE. In multivariate analysis, the performance of 3 feature selection algorithms (namely minimum redundancy-maximum relevance, mRmR; neighborhood component analysis, NCA; and sequence forward selection, SFS) was compared. The optimal feature subset was determined according to the optimal feature selection algorithm and used for testing the 3 classifier algorithms (namely the support vector machine, RBF-SVM; linear discriminant analysis, LDA; and logistic regression). All the tests were repeated 5 times with 10-fold cross validation experiments. RESULTS: More than 50% of the radiomic features exhibited strong distinguishing ability, among which gray level co-occurrence matrix feature S (3, -3) SumEntrp showed a good classification performance with an AUC of 0.72 (P<0.01), a sensitivity of 0.83 and a specificity of 0.57. For the multivariate analysis, 15 features were selected based on the SFS algorithm, which produced better results than the other two algorithms. Testing of these 15 selected features for their average cross-validation performance with RBF-SVM classifier yielded a test accuracy of 0.82∓0.09, an AUC of 0.86∓0.12, a sensitivity of 0.88∓0.11, and a specificity of 0.76∓0.18. CONCLUSION: The radiomic features based on gadoxetate disodium-enhanced magnetic resonance images allow efficient differential diagnosis of HCC and HHE, and can potentially provide important assistance in clinical diagnosis of the two diseases.
OBJECTIVE: To evaluate the feasibility of using radiomic features for differential diagnosis of hepatocellular carcinoma (HCC) and hepatic cavernous hemangioma (HHE). METHODS:Gadoxetate disodium-enhanced magnetic resonance imaging data were collected from a total of 135 HCC and HHE lesions. The radiomic texture features of each lesion were extracted on the hepatobiliary phase images, and the performance of each feature was assessed in differentiation and classification of HCC and HHE. In multivariate analysis, the performance of 3 feature selection algorithms (namely minimum redundancy-maximum relevance, mRmR; neighborhood component analysis, NCA; and sequence forward selection, SFS) was compared. The optimal feature subset was determined according to the optimal feature selection algorithm and used for testing the 3 classifier algorithms (namely the support vector machine, RBF-SVM; linear discriminant analysis, LDA; and logistic regression). All the tests were repeated 5 times with 10-fold cross validation experiments. RESULTS: More than 50% of the radiomic features exhibited strong distinguishing ability, among which gray level co-occurrence matrix feature S (3, -3) SumEntrp showed a good classification performance with an AUC of 0.72 (P<0.01), a sensitivity of 0.83 and a specificity of 0.57. For the multivariate analysis, 15 features were selected based on the SFS algorithm, which produced better results than the other two algorithms. Testing of these 15 selected features for their average cross-validation performance with RBF-SVM classifier yielded a test accuracy of 0.82∓0.09, an AUC of 0.86∓0.12, a sensitivity of 0.88∓0.11, and a specificity of 0.76∓0.18. CONCLUSION: The radiomic features based on gadoxetate disodium-enhanced magnetic resonance images allow efficient differential diagnosis of HCC and HHE, and can potentially provide important assistance in clinical diagnosis of the two diseases.
Authors: Sheela Agarwal; Joseph R Grajo; Jorge M Fuentes-Orrego; Seyed Mahdi Abtahi; Mukesh G Harisinghani; Sanjay Saini; Peter F Hahn Journal: Eur J Radiol Date: 2016-01-04 Impact factor: 3.528
Authors: Philippe Lambin; Emmanuel Rios-Velazquez; Ralph Leijenaar; Sara Carvalho; Ruud G P M van Stiphout; Patrick Granton; Catharina M L Zegers; Robert Gillies; Ronald Boellard; André Dekker; Hugo J W L Aerts Journal: Eur J Cancer Date: 2012-01-16 Impact factor: 9.162
Authors: Fergus Davnall; Connie S P Yip; Gunnar Ljungqvist; Mariyah Selmi; Francesca Ng; Bal Sanghera; Balaji Ganeshan; Kenneth A Miles; Gary J Cook; Vicky Goh Journal: Insights Imaging Date: 2012-10-24