MRI Predicts Recurrence-Free Survival in Breast Cancer

MRI can help physicians predict breast cancer recurrence in women who underwent chemotherapy and surgery.

MRI may help predict recurrence-free survival (RFS) in women with breast cancer, even in the presence of pathologic complete response (PCR) and residual cancer burden (RCB) class, according to a study published in Radiology.

Researchers from California, Rhode Island, Pennsylvania, Washington, Alabama, South Carolina, New York, Texas, Washington DC, and Illinois, performed a prospective multicenter study to evaluate volumetric MR imaging for predicting RFS after neoadjuvant chemotherapy (NACT) of breast cancer and to consider its predictive performance relative to PCR.

The study included 162 female patients with breast tumors that were 3 cm or larger. The women were scheduled to receive NACT. They underwent four examinations with dynamic contrast-enhanced MR imaging:

1. Before NACT treatment began

2. After one cycle

3. Midtherapy

4. Before surgery[[{"type":"media","view_mode":"media_crop","fid":"47312","attributes":{"alt":"MRI","class":"media-image media-image-right","id":"media_crop_8651781788049","media_crop_h":"0","media_crop_image_style":"-1","media_crop_instance":"5555","media_crop_rotate":"0","media_crop_scale_h":"0","media_crop_scale_w":"0","media_crop_w":"0","media_crop_x":"0","media_crop_y":"0","style":"height: 135px; width: 180px; border-width: 0px; border-style: solid; margin: 1px; float: right;","title":"©nav/","typeof":"foaf:Image"}}]]

The researchers determined 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.

The results showed that 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,” the authors wrote.

The FTV2 and RCB class had the strongest predictive performance, greater than for FTV4 and PCR. 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).

The researchers concluded that using MR imaging to measure breast tumor FTV is a strong predictor for RFS.