Neoadjuvant Chemotherapy for Breast Cancer: Functional Tumor Volume by MR Imaging Predicts Recurrence-free Survival-Results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL

Abstract

Purpose: To evaluate volumetric magnetic resonance (MR) imaging for predicting recurrence-free survival (RFS) after neoadjuvant chemotherapy (NACT) of breast cancer and to consider its predictive performance relative to pathologic complete response (PCR).

Materials and methods: This HIPAA-compliant prospective multicenter study was approved by institutional review boards with written informed consent. Women with breast tumors 3 cm or larger scheduled for NACT underwent dynamic contrast-enhanced MR imaging before treatment (examination 1), after one cycle (examination 2), midtherapy (examination 3), and before surgery (examination 4). Functional tumor volume (FTV), computed from MR images by using enhancement thresholds, and change from baseline (ΔFTV) were measured after one cycle and before surgery. Association of RFS with FTV was assessed by Cox regression and compared with association of RFS with PCR and residual cancer burden (RCB), while controlling for age, race, and hormone receptor (HR)/ human epidermal growth factor receptor type 2 (HER2) status. Predictive performance of models was evaluated by C statistics.

Results: Female patients (n = 162) with FTV and RFS were included. At univariate analysis, FTV2, FTV4, and ΔFTV4 had significant association with RFS, as did HR/HER2 status and RCB class. PCR approached significance at univariate analysis and was not significant at multivariate analysis. At univariate analysis, FTV2 and RCB class had the strongest predictive performance (C statistic = 0.67; 95% confidence interval [CI]: 0.58, 0.76), greater than for FTV4 (0.64; 95% CI: 0.53, 0.74) and PCR (0.57; 95% CI: 0.39, 0.74). At multivariate analysis, a model with FTV2, ΔFTV2, RCB class, HR/HER2 status, age, and race had the highest C statistic (0.72; 95% CI: 0.60, 0.84).

Conclusion: Breast tumor FTV measured by MR imaging is a strong predictor of RFS, even in the presence of PCR and RCB class. Models combining MR imaging, histopathology, and breast cancer subtype demonstrated the strongest predictive performance in this study.

Figure 1:

Study flowchart shows MR imaging data used in the analysis set. Of 230 eligible patients, 162 patients with measurable FTV₄ data who did not receive any trastuzumab were included in the analysis. Reduced subsets at FTV₁, FTV₂, and between-regimen (FTV₃) time points were because of missing MR examinations or nonmeasurable FTV.

Figure 2:

Longitudinal MR images and FTV maps. Maximum intensity projection images (top row) and corresponding FTV maps (bottom row) for a patient with an excellent clinical response and disseminated residual disease. FTV measurements were 48.5 cm³, 35.4 cm³, 5.6 cm³, and 0 cm³ for baseline, early treatment, inter-regimen, and presurgery time points, respectively (shown from left to right).

Figure 3:

Graphs show Kaplan-Meier RFS estimates according to FTV quartile cut points. RFS stratified by FTV₂ (top) is compared with FTV₄ (bottom) cut points at the lowest quartile (Q1), median quartile (Q2), and highest quartile (Q3), respectively, left to right. The log-rank test P value is shown for each plot.

Figure 4:

Graphs show Kaplan-Meier plots with RFS estimates by time point and HR and HER2 subtype. RFS stratified by FTV₂ (top row) is compared with FTV₄ (bottom row) by using the highest quartile (Q3) cut point for HR-positive (HR+) and HER2-negative (HER2-), HER2-positive (HER2+), and HR-negative/HER2-negative (HR-/HER2-; triple negative) subtypes, respectively, left to right. The log-rank test P value is shown for each plot.