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

Abstract

and Data System (BI-RADS) category 4A lesions can be distinguished from BI-RADS 3 lesions with main ultrasound (US) findings such as a well-defined contour, round/oval shape, and parallel orientation with a homogeneous echo pattern. Breast Imaging Reporting and Data System 4A solid masses might be diagnosed as simple fibroadenomas (SFAs), complex fibroadenomas (CFAs), or benign phyllodes tumors (BPTs). Complex fibroadenomas have an increased risk of invasive cancer development than SFAs, and BPTs have a risk of borderlinemalignant phyllodes tumor transformation; both of them are surgically treated, whereas follow-up procedures are applied in SFAs. It is essential to differentiate SFAs from CFAs and BPTs. Grayscale features of these lesions include a prominent overlap. Texture analyses in breast lesions have contributions in benignmalignant lesion differentiation. In this study, we aimed to use texture analysis of US images to differentiate these benign lesions. Methods-Grayscale US features of lesions (32 SFAs, 31 CFAs, and 32 BPTs) were classified according to the BI-RADS. Texture analysis of US images with LIFEx software (http://www.lifexsoft.org) was performed retrospectively. Firstand second-order histogram parameters were evaluated. Results-In grayscale US, the shape, orientation, and posterior acoustic characteristics had statistical significance (P < .05). In the statistical analysis, skewness, kurtosis, excess kurtosis, gray-level co-occurrence matrix (GLCM)-energy, GLCMentropy log 2, and GLCM-entropy log 10 revealed significant differences among all 3 groups (P < .05). Conclusions-As grayscale US features show prominent intersections, and treatment options differ, correct diagnosis is essential in SFAs, CFAs, and BPTs. In this study, we concluded that texture analysis of US images can discriminate SFAs from CFAs and BPTs. Texture analyses of US images is a potential candidate diagnostic tool for these lesions, and accurate diagnoses will preclude patients from undergoing unnecessary biopsies.

Evaluation of Elastographic Features in Complex Fibroadenomas With Radiologic‐Pathologic Correlation

“…1 In another study, a complex-cystic echo pattern was also reported in most CFAs. 7 Despite these, an irregular shape, a noncircumscribed contour, microcalcification content, and posterior acoustic enhancement are more common in CFAs than SFAs. In some cases, the imaging findings are challenging.…”

“…22 The mean size of SFAs is smaller than that of CFAs. 1,7,23 In the literature, there is a hypothesis that larger fibroadenomas have stiffer structures than smaller ones because of prominent compression of Figure 5. Routine US evaluation of a 32-year-old female patient.…”

“…As the patient gets older, fibroadenomas regress; hyaline degenerations occur; and the structures develop complex histopathologic features. 7,23 In fibroadenomas in elderly women and those including calcification, the stiffness increases. 26,27 B-mode US images of SFAs and CFAs may show an overlap, and as a result, the lesions might be classified as BI-RADS 3 and 4A.…”

“…2 In the literature, B-mode ultrasound (US) findings and even US texture features of CFAs were reported. 1,2,7 To our knowledge, there is no study that has specifically described the elastographic findings of CFAs. This article aims to illustrate the elastographic features in CFAs and correlate radiologic and histopathologic findings of different cases.…”

Differential diagnosis between small breast phyllodes tumors and fibroadenomas using artificial intelligence and ultrasound data

“…Deep learning and other technologies of artificial intelligence (AI) can accurately identify lesions in images, calculate and obtain quantitative information on the morphology and textural features of lesions, and thus provide a basis for differentiating breast diseases (10,11). Presently, studies on AI for differentiating PTs from FAs are limited and have mainly focused on MRI and ultrasonic images (12)(13)(14). Research has shown that the textural features on MRI T2-weighted short-tau inversion recovery (T2W-STIR) have higher diagnostic performance compared with clinical and conventional MRI features for distinguishing between PTs and FAs (12).…”