Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer

Abstract

Objective: To predict the response of breast cancer patients to neoadjuvant chemotherapy (NAC) using features derived from dynamic contrast-enhanced (DCE) MRI.

Materials and methods: 60 patients with triple-negative early-stage breast cancer receiving NAC were evaluated. Features assessed included clinical data, patterns of tumor response to treatment determined by DCE-MRI, MRI breast imaging-reporting and data system descriptors, and quantitative lesion kinetic texture derived from the gray-level co-occurrence matrix (GLCM). All features except for patterns of response were derived before chemotherapy; GLCM features were determined before and after chemotherapy. Treatment response was defined by the presence of residual invasive tumor and/or positive lymph nodes after chemotherapy. Statistical modeling was performed using Lasso logistic regression.

Results: Pre-chemotherapy imaging features predicted all measures of response except for residual tumor. Feature sets varied in effectiveness at predicting different definitions of treatment response, but in general, pre-chemotherapy imaging features were able to predict pathological complete response with area under the curve (AUC)=0.68, residual lymph node metastases with AUC=0.84 and residual tumor with lymph node metastases with AUC=0.83. Imaging features assessed after chemotherapy yielded significantly improved model performance over those assessed before chemotherapy for predicting residual tumor, but no other outcomes.

Conclusions: DCE-MRI features can be used to predict whether triple-negative breast cancer patients will respond to NAC. Models such as the ones presented could help to identify patients not likely to respond to treatment and to direct them towards alternative therapies.

Keywords: BI-RADS; Biomedical imaging Informatics; Heterogeneity; Pharmacokinetics; Treatment response; Triple-Negative breast cancer.

Figure 1

Example patterns of response; arrows indicate primary lesions. (A) 41-year-old woman with triple-negative (TN) invasive ductal carcinoma (IDC). Following therapy, tumor reduced in size but was still visible by MRI. Final pathology showed residual invasive carcinoma and lymph node metastases. (B) 65-year-old woman with TN IDC. Tumor progressed during therapy as shown by MRI, and the patient was taken off the trial early. Final pathology was not available for this patient. (C) 46-year-old woman with TN IDC. Following therapy, tumor was no longer visible by MRI. Final pathology showed pathological complete response. (D) 42-year-old woman with TN IDC. Following therapy, tumor showed no major changes in size or morphology by MRI. Final pathology showed residual invasive carcinoma and lymph node metastases. Access the article online to view this figure in colour.

Figure 2

(A) Dynamic sequence showing lesion-averaged voxel intensity versus time, along with images at three time point time points. The window level does not change between images. (B) A three time point empirical kinetic map derived from the lesion dynamic sequence, colored by wash-in (value) and wash-out (hue). The lesion shown is the pre-therapy lesion from figure 1A. Access the article online to view this figure in colour.

Figure 3

Pharmacokinetic maps for a single lesion, and estimated pharmacokinetic curves for three pixels. The lesion shown is the pre-therapy lesion from figure 1A. Note that values of v_e>1 are unphysical, but they probably appear due to uncertainties in model inputs, particularly T₁₀. Access the article online to view this figure in colour.

Figure 4

Proportion of patients with residual invasive breast tumor, residual lymph nodes metastases, or both, after therapy. Access the article online to view this figure in colour.

Figure 5

Results of modeling to predict treatment response for various feature sets. If area under the curve is less than 0.6, the result is labeled as ‘junk.’ 95% CI are shown based on the 20 Monte Carlo repetitions of each model. Access the article online to view this figure in colour.

Figure 6

Fraction of MRI ‘pattern of response’ categories for patients with residual tumor (left) or no residual tumor (right) by pathology following therapy. The ‘resolution’ pattern of response indicates no visible tumor-like enhancement by MRI, and all other patterns indicate visible tumor-like enhancement. Access the article online to view this figure in colour.