Related Experiment Video
Updated: Aug 1, 2026
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Value of virtual touch tissue imaging quantification in diagnosing breast mass-type lesions and nonmass lesions: a
Si-Yi Li1, Liang-Ling Cheng1,2, Wei-Min Li1
1Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, China.
Background:
Breast cancer is the most common malignancy in women worldwide. Breast lesions can be divided into mass-type lesions and nonmass lesions (NMLs) on ultrasound, with NMLs being difficult to diagnose clinically. With the development of ultrasound technology, virtual touch tissue imaging quantification (VTIQ) is being gradually applied to evaluate breast lesions in clinical practice. This study evaluated the diagnostic performance of VTIQ in mass-type lesions and NMLs in a cohort of patients with Breast Imaging Reporting and Data System (BI-RADS) 4-5 breast cancer.
Methods:
A prospective cohort of patients with breast lesions classified as BI-RADS 4-5 based on ultrasonographic evaluation conducted between January 2024 and February 2025 was enrolled in this study. Lesions were categorized as mass-type lesions and NMLs according to their ultrasonographic characteristics. Shear wave velocity (SWV) was measured via VTIQ. Postoperative histopathological results served as the gold standard. Receiver operating characteristic (ROC) curves were plotted to assess diagnostic performance, and the area under the curve (AUC) was calculated. The efficacy of SWV for diagnosing mass-type lesions and NMLs was compared.
Results:
This study included 761 patients with breast lesions, comprising 433 mass-type lesions (190 malignant and 243 benign) and 328 NMLs (131 malignant and 197 benign). The SWV of malignant lesions was significantly greater than that of their benign counterparts in both the mass-type and nonmass groups (P<0.001 for both). The AUC for differentiating malignant lesions from benign lesions was 0.807 [95% confidence interval (CI): 0.767-0.843] for mass-type lesions and 0.886 (95% CI: 0.846-0.918) for NMLs, with optimal cutoff values of 3.39 and 3.52 m/s, respectively. In diagnosing mass-type lesions and NMLs, respectively, VTIQ had a sensitivity of 68.42% and 79.39%, a specificity 85.19% of 86.80%, a positive predictive value of 78.31% and 80.00%, a negative predictive value of 77.53% and 86.40%, and an accuracy of 77.83% and 83.80%. The sensitivity (P=0.030), negative predictive value (P=0.016), and accuracy (P=0.038) were significantly lower for mass-type lesions than for NMLs.
Conclusions:
VTIQ demonstrated diagnostic value for mass-type lesions and NMLs of the breast. VTIQ may exhibit superior diagnostic performance for NMLs than for mass-type lesions.
