Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge.

Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.

Dynamic contrast-enhanced breast MRI features correlate with invasive breast cancer angiogenesis.

Angiogenesis is a critical component of breast cancer development, and identification of imaging-based angiogenesis assays has prognostic and treatment implications. We evaluated the association of semi-quantitative kinetic and radiomic breast cancer features on dynamic contrast-enhanced (DCE)-MRI with microvessel density (MVD), a marker for angiogenesis. Invasive breast cancer kinetic features (initial peak percent enhancement [PE], signal enhancement ratio [SER], functional tumor volume [FTV], and washout fraction [WF]), radiomics features (108 total features reflecting tumor morphology, signal intensity, and texture), and MVD (by histologic CD31 immunostaining) were measured in 27 patients (1/2016-7/2017). Lesions with high MVD levels demonstrated higher peak SER than lesions with low MVD (mean: 1.94 vs. 1.61, area under the receiver operating characteristic curve [AUC] = 0.79, p = 0.009) and higher WF (mean: 50.6% vs. 22.5%, AUC = 0.87, p = 0.001). Several radiomics texture features were also promising for predicting increased MVD (maximum AUC = 0.84, p = 0.002). Our study suggests DCE-MRI can non-invasively assess breast cancer angiogenesis, which could stratify biology and optimize treatments.