Objectives
To study the added value of mean and entropy of apparent diffusion coefficient (ADC) values at standard (800 s/mm2) and high (1500 s/mm2) b-values obtained with diffusion-weighted imaging in identifying histologic phenotypes of invasive ductal breast cancer (IDC) with MR imaging.
Methods
One hundred thirty-four IDC patients underwent diffusion-weighted imaging with b-values of 800 and 1500 s/mm2, and corresponding ADC800 and ADC1500 maps were generated. Mean and entropy of volumetric ADC values were compared with molecular markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67). Associations among morphologic features, ADC metrics, and phenotypes (luminal A, luminal B [HER2 negative], luminal B [HER2 positive], HER2 positive, and triple negative) were evaluated.
Results
Mean ADC values were significantly decreased in ER-positive, PR-positive, and HER2-negative tumors (p < 0.01). Ki-67 ≥ 20% tumors demonstrated significantly higher ADC entropy values compared with Ki-67 < 20% tumors (p < 0.001). Luminal A subtype tended to display lower ADC entropy values compared with other subtypes, while HER2-positive subtype tended to display higher mean ADC values. ADC1500 entropy provided superior diagnostic performance over ADC800 entropy (p = 0.04). Independent risk factors were ADC1500 entropy (p = 0.002) associated with luminal A, irregular mass shape (p = 0.018) and ADC1500 entropy (p = 0.022) with luminal B (HER2 positive), mean ADC1500 (p = 0.018) with HER2 positive, and smooth mass margin (p = 0.012) and rim enhancement (p = 0.003) with triple negative.
Conclusions
Mean and entropy of ADC values provided complementary information and added value for evaluating IDC histologic phenotypes. High-b-value ADC1500 may facilitate better phenotype discrimination.
Key Points
• ADC metrics are associated with molecular marker status in IDC.
• ADC1500improves differentiation of histologic phenotypes compared with ADC800.
• ADC metrics add value to morphologic features in IDC phenotyping.