Ultrafast dynamic contrast-enhanced breast MRI with quantitative perfusion parameters in differentiating breast cancer: a study focusing on triple-negative and HER2 positive breast cancer.

Background: In the realm of breast cancer diagnosis and treatment, accurately discerning molecular subtypes is of paramount importance, especially when aiming to avoid invasive tests. The updated guidelines for diagnosing and treating HER2 positive advanced breast cancer, as presented at the 2021 National Breast Cancer Conference and the Annual Meeting of the Chinese Society of Clinical Oncology, highlight the significance of this approach. A new generation of drug-antibody combinations has emerged, expanding the array of treatment options for HER2 positive advanced breast cancer and significantly improving patient survival rates. Triple-negative breast cancer (TNBC), on the other hand, may indicate survival outcomes following multi-agent adjuvant chemotherapy. DISCO is a more recent DCE MRI technique that has achieved high spatial and temporal resolution and minimized image artifacts in cases like malignant focal liver lesions, enhanced focal breast lesions, and intracranial aneurysms.

Objective: To employ the method mentioned above to differentiate between triple-negative and non-triple-negative as well as HER2 positive and HER2 negative cancer lesions, and to assess the value of quantitative and semi-quantitative parameters in molecularly typing breast cancer.

Methods: All participants were scanned with a 3.0-T MR scanner (GE SIGNA™ Premier) using a 16-channel phased-array body coil. Each participant underwent a DISCO DCE-MRI with a scan time of approximately 1 minute and 40 seconds. The ROIs were outlined with the GenIQ software, avoiding regions with blood vessels, susceptibility artifacts, hemorrhage, and necrosis. We evaluated four quantitative parameters (K, k, v, v) and four semi-quantitative parameters (TTP, MAX Conc, AUC, MAX Slope). The carcinomas were segregated into respective subgroups (HER2+ vs HER2-, TNBC vs non-TNBC, HER2+ vs TNBC) and we compared the eight parameters across these groups. The AUC of the models was compared using DeLong's test as per the ROC analysis.

Results: We analyzed a total of 96 female patients, revealing significant differences in the semi-quantitative parameters such as TTP, MAX Conc, AUC, and MAX Slope among different groups. HER2-positive versus HER2-negative exhibited significant differences in quantitative parameters (Ktrans: 0.22 min-1 vs. 0.43 min-1, kep: 0.11 min-1 vs. 0.35 min-1, vp: 0.01 vs. 0.04, all P < 0.05). TNBC versus non-TNBC revealed statistical variations in quantitative parameters (Ktrans: 1.03 min-1 vs. 0.15 min-1, kep: 0.61 min-1 vs. 0.19 min-1, vp: 0.18 vs. 0.01, all P < 0.05). Additionally, HER2-positive compared to TNBC demonstrated significant differences in quantitative parameters (Ktrans: 0.22 min-1 vs. 1.03 min-1, kep: 0.11 min-1 vs. 0.61 min-1, vp: 0.01 vs. 0.18, all P < 0.05). As per ROC analysis, Ktrans, kep, vp, TTP, and MAX Conc effectively differentiated TNBC from non-TNBC, with TTP being the strongest determinant for TNBC. Furthermore, these parameters successfully distinguished between HER2 positive and HER2 negative, with kep being particularly effective in identifying HER2. Importantly, Ktrans, kep, vp, TTP, and MAX Conc were effective in discriminating HER2 positive from TNBC, with kep and TTP exhibiting notable efficacy in this context.

Conclusion: Our study suggests that DISCO DCE-MRI derived parameters could serve as reliable quantitative biomarkers for differentiating between TNBC and HER2 positive breast cancer.