The Utility of Texture Analysis Based on Breast Magnetic Resonance Imaging in Differentiating Phyllodes Tumors From Fibroadenomas

Frontiers in Oncology

Liangping Luo¹  Xi Zhong¹  Yu Tan²  Kuiming Jiang²  Shuting Huang²  Songxin Wu²  Yifei Mao³  Yingwei Qiu⁴  Tianfa Dong⁴  Hui Mai⁴  Xiaowei Huang⁵ 

[1] Department of Medical Imaging, The First Affiliated Hospital of Jinan University, Guangzhou, China;Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, China;Department of Radiology, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen, China;Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China;Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;

关键词: texture analysis;    breast;    magnetic resonance imaging;    phyllodes tumor;    fibroadenoma;   

DOI  :  10.3389/fonc.2019.01021

来源: DOAJ

【 摘 要 】

Background: The preoperative diagnosis of phyllodes tumors (PTs) of the breast is critical to appropriate surgical treatment. However, reliable differentiation between PT and fibroadenoma (FA) remains difficult in daily clinical practice. The purpose of this study was to investigate the utility of breast MRI texture analysis for differentiating PTs from FAs.Materials and Methods: Forty-two PTs and 42 FAs were enrolled in this retrospective study. Clinical and conventional MRI features (CCMF) and MRI texture analysis were used to distinguish between PT and FA. Texture features were extracted from the axial short TI inversion recovery T2-weighted (T2W-STIR), T1-weighted pre-contrast, and two contrast-enhanced series (first contrast and third contrast). The Mann–Whitney U test was used to select statistically significant features of texture analysis and CCMF. Using a linear discriminant analysis, the most discriminative features were determined from statistically significant features. The K-nearest neighbor classifier and ROC curve were applied to evaluate the diagnostic performance.Results: With a higher classification accuracy (89.3%) and an AUC of 0.89, the texture features on T2W-STIR outperformed the texture features on other MRI sequences and CCMF. The AUC of the combination of CCMF with texture features on T2W-STIR was significantly higher than that of CCMF or texture features on T2W-STIR alone (p < 0.05). Based on the result of the classification accuracy (95.2%) and AUC (0.95), the diagnostic performance of the combination strategy performed better than texture features on T2W-STIR or CCMF separately.Conclusions: Texture features on T2W-STIR showed better diagnostic performance compared to CCMF for the distinction between PTs and FAs. After further validation of multi-institutional large datasets, MRI-based texture features may become a potential biomarker and be a useful medical decision tool in clinical trials having patients with breast fibroepithelial neoplasms.

【 授权许可】

Unknown