Can Radiologist and Pathologist Reach The Truth Together in The Diagnosis of Benign Fibroepithelial Lesions?

Use of shear-wave elastography to distinguish complex and complicated fibroadenomas from simple fibroadenomas

PURPOSE

Simple fibroadenomas (SFAs), complex fibroadenomas (CFAs), and cellular fibroadenomas (CeFAs) are variants of fibroadenomas. Additionally, some degenerative, hyperplastic, and metaplastic changes may occur in fibroadenomas, forming complicated fibroadenomas. Distinctive ultrasonography (US) features in variants of fibroadenomas and complicated fibroadenomas have not been reported. Shear-wave elastography (SWE) can be applied to effectively discriminate between these variants and complicated fibroadenomas. In this study, we aimed to evaluate SWE findings to discriminate between SFAs and other variants.

METHODS

In total, 48 patients (26 with SFAs, 16 with CFAs, 3 with CeFAs, and 3 with complicated fibroadenomas) participated in this study. The lesions were classified into two groups according to histopathologic diagnoses. The SWE evaluation and lesion elasticity scores (Emax, Emean, and Emin) were both assessed in m/s and k/Pa, respectively. Two observers measured Emax, Emean, and Emin. Brightness (B)-mode US findings based on the Breast Imaging Reporting and Data System categorization and elasticity scores were recorded. In the statistical analyses, the chi-square test and non-parametric tests were performed. Fisher's exact test was used to compare independent groups, and Spearman's correlation coefficients were used to correlate the SWE data between the two observers. Additionally, receiver operating characteristic curves were analyzed to evaluate the diagnostic performance of the elasticity values.

RESULTS

The B-mode US features in both groups showed no statistical significance. The set of SWE values of both observers demonstrated strong statistical significance in discriminating between group 1 (SFAs) and Group 2 (CFAs, CeFAs, and complicated fibroadenomas).

CONCLUSION

As the fibroadenoma variants and complicated fibroadenomas have similar US findings, SWE in addition to a conventional B-mode examination can increase the diagnostic performance to discriminate SFAs from other complex and complicated forms of fibroadenomas.

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

OBJECTIVES

American College of Radiology Breast Imaging Reporting 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 borderline-malignant 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 benign-malignant 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. First- and 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, GLCM-entropy 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.