Preoperative predicting malignancy in breast mass-like lesions: value of adding histogram analysis of apparent diffusion coefficient maps to dynamic contrast-enhanced magnetic resonance imaging for improving confidence level

Abstract

Objective: This study aims to find out the benefits of adding histogram analysis of apparent diffusion coefficient (ADC) maps onto dynamic contrast-enhanced MRI (DCE-MRI) in predicting breast malignancy.

Methods: This study included 95 patients who were found with breast mass-like lesions from January 2014 to March 2016 (47 benign and 48 malignant). These patients were estimated by both DCE-MRI and diffusion-weighted imaging (DWI) and classified into two groups, namely, the benign and the malignant. Between these groups, the DCE-MRI parameters, including morphology, enhancement homogeneity, maximum slope of increase (MSI) and time-signal intensity curve (TIC) type, as well as histogram parameters generated from ADC maps were compared. Then, univariate and multivariate logistic regression analyses were conducted to determine the most valuable variables in predicting malignancy. Receiver operating characteristic curve analyses were taken to assess their clinical values.

Results: The lesion morphology, MSI and TIC Type (p < 0.05) were significantly different between the two groups. Multivariate logistic regression analyses revealed that irregular morphology, TIC Type II/III and ADC₁₀ were important predictors for breast malignancy. Increased area under curve (AUC) and specificity can be achieved with Model 2 (irregular morphology + TIC Type II/III + ADC₁₀ < 1.047 ×10^-3 mm² s^-1) as the criterion than Model 1 (irregular morphology + TIC Type II/III) only (Model 2 vs Model 1; AUC, 0.822 vs 0.705; sensitivity, 68.8 vs 75.0%; specificity, 95.7 vs 66.0%).

Conclusion: Irregular morphology, TIC Type II/III and ADC₁₀ are indicators for predicting breast malignancy. Histogram analysis of ADC maps can provide additional value in predicting breast malignancy. Advances in knowledge: The morphology, MSI and TIC types in DCE-MRI examination have significant difference between the benign and malignant groups. A higher AUC can be achieved by using ADC₁₀ as the diagnostic index than other ADC parameters, and the difference in AUC based on ADC₁₀ and ADC_mean was statistically significant. The irregular morphology, TIC Type II/III and ADC₁₀ were significant predictors for malignant lesions.