Quantitative MRI mapping parameters to differentiate HER2-positive and HER2-low breast cancer

Breast cancer (BC) is the most common malignant tumor among women worldwide, and early detection and precise molecular subtyping are crucial for improving survival rates. Quantitative magnetic resonance imaging (MRI) mapping techniques have demonstrated potential in evaluating tumor tissue characteristics. The aim of this study was to evaluate the value of quantitative MRI mapping parameters in determining human epidermal growth factor receptor 2 (HER2) status in BC.

In this retrospective study, 178 women with histologically confirmed BC underwent preoperative MRI, including longitudinal relaxation time (T1), transverse relaxation time (T2), and effective transverse relaxation time (T2*) mapping sequences. Following triplicate region of interest (ROI) measurements by two independent radiologists per lesion, the median value for each radiologist was calculated and then averaged between them to derive the final quantitative parameter. Quantitative mapping parameters were compared across HER2 groups with Bonferroni-adjusted tests. Multivariable regression was performed to identify independent predictors for differentiating HER2 statuses.

Significant differences in estrogen receptor (ER) and progesterone receptor (PR) status were observed between the HER2-low and HER2-positive groups (P<0.05). Pairwise comparisons showed that HER2-low BCs had significantly higher ΔT1 [ΔT1 = T1_pre (T1_pre is the native T1 relaxation time measured before contrast agent administration) - T1_post (T1_post is the T1 relaxation time measured after contrast injection)] and ΔT1_pre [ΔT1_pre = (T1_pre - T1_post)/T1_pre] values, lower T1_post and T2* values (all P<0.05) compared to HER2-positive BCs, while overall differences across all the three HER2 groups were not observed for all mapping parameters. Multivariate logistic regression analysis showed that T2* and T1_post were the most discriminative indexes [the area under the curve (AUC) =0.800 and 0.762, respectively] and ER was optimal pathological differentiation index (AUC =0.617). The multivariable model incorporating ER state, values of T2* and T1_post demonstrated favorable performance with AUC of 0.854.

A combined model incorporating ER state, T2* and T1_post value showed good discriminative ability in non-invasively differentiating HER2-low from HER2-positive BCs.