Dual-Phase ADC Modelling of Breast Masses in Diffusion-Weighted Imaging: Comparison with Histopathologic Findings

Abstract

Objective: To investigate the diagnostic value of dual-phase apparent diffusion coefficient (ADC) compared to traditional ADC values in quantitative diffusion-weighted imaging (DWI) for differentiating between benign and malignant breast masses. Materials and Methods: Diffusion-weighted images of pathologically confirmed 88 benign and 85 malignant lesions acquired using a 3.0T MR scanner were analyzed. Small region-of-interests focusing on the highest signal intensity of lesions were used. Lesion ADC estimates were obtained separately from all b-value images (ADC; b=50, 400 and 800s/mm(2)), lower b-value images (ADC low; b=50 and 400s/mm2) and higher b-value images (ADC(high); b=400 and 800s/mm(2)). A set of dual-phase ADC (dpADC) models were constructed using ADC low, ADC high and a perfusion influence factor ranging from 0 to 1. Results: Strong positive correlation is observable between ADC and all dpADCs (rho=0.80-1.00). Differences in ADC and dpADCs between the benign and the malignant lesions are all significant (p<0.05). In detecting malignancy, traditional lesion ADC provides a good performance (AUC=89.9%) however dpADC(0.5) (dpADC with a factor of 0.5) accomplishes a better performance (AUC=90.8%). At optimal thresholds, ADC achieves 94.1% sensitivity, 72.7% specificity and 83.2% accuracy while dpADC(0.5) leads to 92.9% sensitivity, 79.5% specificity and 86.1% accuracy. Conclusion: Dual-phase ADC modelling may improve the accuracy in breast cancer diagnosis using DWI. Further prospective studies are needed to justify its benefit in clinical setting.