Breast Imaging Reporting and Data System evaluation of breast lesions improved with virtual touch tissue imaging average grayscale values

Abstract

BACKGROUND:

Early breast cancer diagnosis is of great clinical importance for selecting treatment options, improving prognosis, and enhancing the quality of patients’ survival.

OBJECTIVE:

We investigated the value of virtual touch tissue imaging average grayscale values (VAGV) helper Breast Imaging Reporting and Data System (BI-RADS) in diagnosing breast malignancy.

METHODS:

We retrospectively analyzed 141 breast tumors in 134 patients. All breast lesions were diagnosed pathologically by biopsy or surgical excision. All patients first underwent conventional ultrasound (US) followed by virtual touch tissue imaging (VTI). The measurement of the VAGV of the lesion was performed by Image J software. BI-RADS classification was performed for each lesion according to the US. We performed a two-by-two comparison of the diagnostic values of VAGV, BI-RADS, and BI-RADS $+$ VAGV.

RESULTS:

VAGV was lower in malignant tumors than in benign ones (35.82 $\pm$ 13.39 versus 73.58 $\pm$ 42.69, $P<$ 0.001). The area under the receiver operating characteristic curve (AUC) value, sensitivity, and specificity of VAGV was 0.834, 84.09%, and 69.07%, respectively. Among BI-RADS, VAGV, and BI-RADS $+$ VAGV, BI-RADS $+$ VAGV had the highest AUC (0.926 versus 0.882, $P=$ 0.0066; 0.926 versus 0.834, $P=$ 0.0012). There was perfect agreement between the two radiologists using VAGV (ICC $=$ 0.9796) and substantial agreement using BI-RADS (Kappa $=$ 0.725).

CONCLUSION:

Our study shows that VAGV can accurately diagnose breast cancer. VAGV effectively improves the diagnostic performance of BI-RADS.