Original Research ARTICLE
Differentiation between G1 and G2/G3 phyllodes tumors of breast using mammography and mammographic texture analysis
- 1Jiangsu Provincial Hospital of Traditional Chinese Medicine, China
- 2Bengbu Medical College, China
- 3Nanjing Medical University, China
- 4GE Healthcare (China), China
Purpose To determined the potential of mammography (MG) and mammographic texture analysis in differentiation between Grade 1 (G1) and Grade 2/ Grade 3 (G2/G3) phyllodes tumors (PTs) of breast.
Materials and methods A total of 80 female patients with histologically proven PTs were included in this study. 45 subjects who underwent pretreatment MG from 2010 to 2017 were retrospectively analyzed, including 14 PTs G1 and 31 PTs G2/G3. Tumor size, shape, margin, density, homogeneity, presence of fat or calcifications, halo-sign as well as some indirect manifestations were evaluated. Texture analysis features were performed using commercial software. Receiver operating characteristic curve (ROC) was used to determine the sensitivity and specificity of prediction.
Results G2/G3 PTs showed larger size (> 4.0 cm) compared with PTs G1 (64.52% vs. 28.57%, p = 0.025). Strong lobulation or multinodular confluent was more common in G2/G3 PTs compared to PTs G1 (64.52% vs. 14.29%, p = 0.004) . Significant differences were also observed in tumours’ growth speed and clinical manifestations (p = 0.007, 0.022, respectively) . 10 texture features showed significant differences between the two groups (p＜0.05) , Correlation_AllDirection_offset7_SD and ClusterProminence_AllDirection_offset7_SD were independent risk factors. The area under the curve (AUC) of imaging-based diagnosis, texture-based diagnosis and the combination of the two approaches were 0.805, 0.730 and 0.843 (90.3% sensitivity and 85.7% specificity) .
Conclusions Texture analysis has great potential to improve the diagnostic efficacy of MG in differentiating PTs G1 from PTs G2/G3.
Keywords: Phyllodes tumors, Classification, Mammography, artificial intelligence, machine learning
Received: 11 Mar 2019;
Accepted: 07 May 2019.
Edited by:Aleix Prat, Hospital Clínic de Barcelona, Spain
Reviewed by:Masahiko Tanabe, The University of Tokyo, Japan
Yoichi Naito, National Cancer Center Hospital East, Japan
Copyright: © 2019 Cui, Wang, Jia, Ren, Duan, Cui, Chen and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Prof. Xiao Chen, Jiangsu Provincial Hospital of Traditional Chinese Medicine, Nanjing, Jiangsu Province, China, firstname.lastname@example.org
Prof. Zhongqiu Wang, Jiangsu Provincial Hospital of Traditional Chinese Medicine, Nanjing, Jiangsu Province, China, email@example.com