Background:

 Breast cancer may result in remodeling of adjacent normal appearing breast tissues. Magnetic resonance imaging (MRI) is increasingly used in the diagnosis and follow-up of breast cancer by means of diffusion weighted imaging, which is based on thermal motion of water molecules in the extracellular fluid.

Objectives:

 We investigated the correlation of visual assessment of peri-tumoral edema with peri-tumoral and tumoral apparent diffusion coefficient (ADC) values.

Patients and Methods:

 In this cross-sectional study, from 2016 to 2018, 78 patients with 89 malignant breast lesions (mean age, 47 years) were examined by 1.5-T breast MRI. The lesions were categorized based on the visual assessment of peri-tumoral edema on T2 weighted imaging (T2WI) into two groups: (A) with edema (36 lesions) and (B) without edema (53 lesions). Measuring ADC values in the contralateral normal breast tissue, peri-tumoral tissue and peri-tumoral-normal tissue ADC ratio were compared between the two groups for all lesions.

Results:

 The number of in situ lesions was higher in group B (7.5% vs 2.7%) with the p value of 0.01. The mean of ADC values in the normal breast tissue was 1.76 × 10-3mm2/s. Tumor ADCs were significantly lower in group A compared to group B (0.95 × 10-3mm2/s vs. 1.11 × 10-3mm2/s) with the P value of 0.003. However, peri-tumoral ADCs were significantly higher in group A (1.82 × 10-3mm2/s vs. 1.53 × 10-3mm2/s) with the p value of 0.005. The peri-tumoral-normal tissue ADC ratio was 0.87 in group B and about 1 in group A. However, the difference between normal tissue ADCs and peri-tumoral ADCs was only significant (P value of 0.005) in group B. The cut-off point value for differentiating normal tissue ADCs and peri-tumoral ADCs was 1.61 × 10-3mm2/s with the sensitivity of 65% and specificity of 70%.

Conclusion:

 Breast cancer with peri-tumoral edema has lower tumoral ADC values, higher peri-tumoral ADC values and lower prevalence of in situ lesions. Visual assessment of peri-tumoral edema on T2WI could predict the tumoral characteristic on diffusion-weighted imaging