The Predictive Value of Magnetic Resonance Imaging-based Texture Analysis in Evaluating Histopathological Grades of Breast Phyllodes Tumor

Yifei Mao; Zhongtang Xiong; Songxin Wu; Zhiqing Huang; Ruoxian Zhang; Yuqin He; Yuling Peng; Yang Ye; Tianfa Dong; Hui Mai

doi:10.4048/jbc.2022.25.e14

The Predictive Value of Magnetic Resonance Imaging-based Texture Analysis in Evaluating Histopathological Grades of Breast Phyllodes Tumor

J Breast Cancer. 2022 Apr;25(2):117-130. doi: 10.4048/jbc.2022.25.e14.

Authors

Yifei Mao^#¹, Zhongtang Xiong^#², Songxin Wu³, Zhiqing Huang³, Ruoxian Zhang³, Yuqin He³, Yuling Peng¹, Yang Ye¹, Tianfa Dong⁴, Hui Mai⁵

Affiliations

¹ Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
² Department of Pathology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
³ Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, China.
⁴ Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. 2019683078@gzhmu.edu.cn.
⁵ Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. 2014683059@gzhmu.edu.cn.

^# Contributed equally.

Abstract

Purpose: Knowing the distinction between benign and borderline/malignant phyllodes tumors (PTs) can help in the surgical treatment course. Herein, we investigated the value of magnetic resonance imaging-based texture analysis (MRI-TA) in differentiating between benign and borderline/malignant PTs.

Methods: Forty-three women with 44 histologically proven PTs underwent breast MRI before surgery and were classified into benign (n = 26) and borderline/malignant groups (n = 18 [15 borderline, 3 malignant]). Clinical and routine MRI parameters (CRMP) and MRI-TA were used to distinguish benign from borderline/malignant PT. In total, 298 texture parameters were extracted from fat-suppression (FS) T2-weighted, FS unenhanced T1-weighted, and FS first-enhanced T1-weighted sequences. To evaluate the diagnostic performance, receiver operating characteristic curve analysis was performed for the K-nearest neighbor classifier trained with significantly different parameters of CRMP, MRI sequence-based TA, and the combination strategy.

Results: Compared with benign PTs, borderline/malignant ones presented a higher local recurrence (p = 0.045); larger size (p < 0.001); different time-intensity curve pattern (p = 0.010); and higher frequency of strong lobulation (p = 0.024), septation enhancement (p = 0.048), cystic component (p = 0.023), and irregular cystic wall (p = 0.045). TA of FS T2-weighted images (0.86) showed a significantly higher area under the curve (AUC) than that of FS unenhanced T1-weighted (0.65, p = 0.010) or first-enhanced phase (0.72, p = 0.049) images. The texture parameters of FS T2-weighted sequences tended to have a higher AUC than CRMP (0.79, p = 0.404). Additionally, the combination strategy exhibited a similar AUC (0.89, p = 0.622) in comparison with the texture parameters of FS T2-weighted sequences.

Conclusion: MRI-TA demonstrated good predictive performance for breast PT pathological grading and could provide surgical planning guidance. Clinical data and routine MRI features were also valuable for grading PTs.

Keywords: Breast; Classification; Magnetic Resonance Imaging; Phyllodes Tumor.