Wenjuan Ma1,2,3,4, Xinpeng Guo1,2,3, Liangsheng Liu1,2,3, Lisha Qi1,2, Peifang Liu1,2,3, Ying Zhu1,2,3, Xiqi Jian4, Guijun Xu5, Xin Wang6, Hong Lu1,2,3, Chao Zhang5. 1. Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer Huanhuxi Road, Hexi District, Tianjin 300060, P. R. China. 2. Key Laboratory of Breast Cancer Prevention and Therapy Huanhuxi Road, Hexi District, Tianjin 300060, P. R. China. 3. Tianjin's Clinical Research Center for Cancer Huanhuxi Road, Hexi District, Tianjin 300060, P. R. China. 4. Department of Biomedical and Engineering, Tianjin Medical University 22 Qixiangtai Road, Heping District, Tianjin 300070, P. R. China. 5. Department of Bone and Soft Tissue Tumors, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer Tianjin, P. R. China. 6. Department of Epidemiology and Biostatistics, First Affiliated Hospital, Army Medical University 30 Gaotanyan Street Shapingba District, Chongqing 400038, P. R. China.
Abstract
OBJECTIVE: This study aimed to differentiate benign and non-benign (borderline/malignant) phyllodes tumors of the breast by the semantic and quantitative features in magnetic resonance imaging (MRI). METHODS: The female patients, diagnosed with phyllodes tumors by MRI and pathological test, were retrospectively selected from December, 2006 to April, 2019. The MRI of benign, borderline and malignant phyllodes tumors was analyzed using 8 semantic features and 20 computed quantitative features from diffuse contrast-enhanced magnetic resonance imaging (DCE-MRI). The semantic features were analyzed by univariate analysis. The least absolute shrinkage and selection operator (LASSO) method was used to identify the optimal subset of MRI quantitative features. According to the results from multivariate logistic regression for the semantic and quantitative features, the model was constructed to differentiate benign and non-benign (borderline/malignant) phyllodes tumors. RESULTS: Thirty-two benign (58.18%), 13 borderline (23.64%) and 10 malignant (18.18%) phyllodes tumors were identified in 54 patients. Five semantic features were proved to be significantly correlated with pathologic grade, including size, the T1 weighted image signal intensity, fat-saturated T2-weighted image signal intensity, enhanced signal intensity, and kinetic curve pattern. With the analysis of LASSO method, three quantitative texture features with significant predictive ability were selected. The model combining both the semantic and quantitative features was proved to have good performance in differentiation on phyllodes tumors, yielding an area under receiver operating characteristic curve, accuracy, sensitivity and specificity of 0.893, 0.933, 1.000, and 0.818, respectively. CONCLUSION: The constructed model based on the semantic and quantitative features of DCE-MRI can significantly improve the differential diagnosis of phyllodes tumors in breast. AJTR
OBJECTIVE: This study aimed to differentiate benign and non-benign (borderline/malignant) phyllodestumors of the breast by the semantic and quantitative features in magnetic resonance imaging (MRI). METHODS: The female patients, diagnosed with phyllodestumors by MRI and pathological test, were retrospectively selected from December, 2006 to April, 2019. The MRI of benign, borderline and malignant phyllodestumors was analyzed using 8 semantic features and 20 computed quantitative features from diffuse contrast-enhanced magnetic resonance imaging (DCE-MRI). The semantic features were analyzed by univariate analysis. The least absolute shrinkage and selection operator (LASSO) method was used to identify the optimal subset of MRI quantitative features. According to the results from multivariate logistic regression for the semantic and quantitative features, the model was constructed to differentiate benign and non-benign (borderline/malignant) phyllodestumors. RESULTS: Thirty-two benign (58.18%), 13 borderline (23.64%) and 10 malignant (18.18%) phyllodestumors were identified in 54 patients. Five semantic features were proved to be significantly correlated with pathologic grade, including size, the T1 weighted image signal intensity, fat-saturated T2-weighted image signal intensity, enhanced signal intensity, and kinetic curve pattern. With the analysis of LASSO method, three quantitative texture features with significant predictive ability were selected. The model combining both the semantic and quantitative features was proved to have good performance in differentiation on phyllodestumors, yielding an area under receiver operating characteristic curve, accuracy, sensitivity and specificity of 0.893, 0.933, 1.000, and 0.818, respectively. CONCLUSION: The constructed model based on the semantic and quantitative features of DCE-MRI can significantly improve the differential diagnosis of phyllodestumors in breast. AJTR