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

Abstract

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.

Texture Analysis of Ultrasound Images to Differentiate Simple Fibroadenomas From Complex Fibroadenomas and Benign Phyllodes Tumors

“…This difference was consistent with the literature . However, the mean size of the BPTs (25.1 ± 11.2 mm) in our study was smaller than that reported in the literature (>30 mm) . Mai et al evaluated 42 patients with phyllodes tumors, and Duman et al evaluated 70 patients with phyllodes tumors.…”

“…However, the mean size of the BPTs (25.1 ± 11.2 mm) in our study was smaller than that reported in the literature (>30 mm) . Mai et al evaluated 42 patients with phyllodes tumors, and Duman et al evaluated 70 patients with phyllodes tumors. As mentioned above, patients with suspicious breast lesions are referred to our institution, a tertiary hospital, where BI‐RADS 4A lesions are diagnosed according to grayscale US features, and the lesions are excised as soon as possible.…”

“…Complex fibroadenomas and BPTs should be excised with clean surgical margins, whereas SFAs can be followed by US using an appropriate protocol. Recently, texture analysis using magnetic resonance imaging of fibroadenomas and phyllodes tumors was reported, and the authors concluded that T2‐weighted texture analyses were useful in discriminating between these lesion types . In another study, discrimination between triple‐negative breast cancer and fibroadenomas was achieved by using TAUI .…”

“…Texture features and analyses of these features derived from different imaging modalities can enable better diagnostic prediction of radiologic gray‐zone lesions. Several studies have used texture analyses to evaluate the contributions of texture features in malignant and benign breast diseases . In this study, we used texture analysis of ultrasound images (TAUI) to discriminate SFAs from CFAs and BPTs.…”

Additive Value of Texture Analysis Based on Breast MRI for Distinguishing Between Benign and Malignant Non-mass Enhancement in Premenopausal Women

“…The results of subsequent studies were similar, and our TA results were no exception, with the AUC, sensitivity, and speci city all lower than those with CRMC, indicating that the diagnostic e ciency of TA alone in NME diagnosis was not high. Some investigators had used TA combined with breast MRI morphology features to distinguish between phyllodes and broadenomas tumors, while others had combined TA with DWI parameters to predict the response to neoadjuvant chemotherapy for breast cancer, and their results demonstrated that combined TA could improve the diagnostic performance [30,31]. On the basis of previous studies, we tried to use the combination of TA and CRMC in NME diagnosis.…”