Preoperative MRI Improves Prediction of Extensive Occult Axillary Lymph Node Metastases in Breast Cancer Patients with a Positive Sentinel Lymph Node Biopsy

Abstract

Rationale and Objectives To test the ability of quantitative measures from preoperative Dynamic Contrast Enhanced MRI (DCE-MRI) to predict, independently and/or with the Katz pathologic nomogram, which breast cancer patients with a positive sentinel lymph node biopsy will have ≥ 4 positive axillary lymph nodes upon completion axillary dissection. Methods and Materials A retrospective review was conducted to identify clinically node-negative invasive breast cancer patients who underwent preoperative DCE-MRI, followed by sentinel node biopsy with positive findings and complete axillary dissection (6/2005 – 1/2010). Clinical/pathologic factors, primary lesion size and quantitative DCE-MRI kinetics were collected from clinical records and prospective databases. DCE-MRI parameters with univariate significance (p < 0.05) to predict ≥ 4 positive axillary nodes were modeled with stepwise regression and compared to the Katz nomogram alone and to a combined MRI-Katz nomogram model. Results Ninety-eight patients with 99 positive sentinel biopsies met study criteria. Stepwise regression identified DCE-MRI total persistent enhancement and volume adjusted peak enhancement as significant predictors of ≥4 metastatic nodes. Receiver operating characteristic (ROC) curves demonstrated an area under the curve (AUC) of 0.78 for the Katz nomogram, 0.79 for the DCE-MRI multivariate model, and 0.87 for the combined MRI-Katz model. The combined model was significantly more predictive than the Katz nomogram alone (p = 0.003). Conclusion Integration of DCE-MRI primary lesion kinetics significantly improved the Katz pathologic nomogram accuracy to predict presence of metastases in ≥ 4 nodes. DCE-MRI may help identify sentinel node positive patients requiring further localregional therapy.

Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage

“…For example, the decision to proceed with neoadjuvant chemotherapy is necessarily a judgment based on clinical stage without knowledge of pathologic stage and, thus, is based primarily on imaging, with MRI often playing a key role. It has been demonstrated that breast MRI is an accurate method for predicting extent of breast cancer as well as demonstrating axillary lymph node involvement through direct evaluation of the axilla . However, MRI may overestimate tumor size and underestimate axillary lymph node involvement; therefore, imaging has not yet obviated the need for surgical staging, including sentinel biopsy or axillary lymph node dissection …”

“…Current research indicates that quantitative MRI tumor biomarkers, ie, phenotypes, rather than anatomic evaluation, may hold promise in predicting malignancy, breast cancer subtypes, molecular pathways, gene expression, and lymph node status . However, not enough literature is available to demonstrate whether computer‐extracted MRI phenotypes can augment the prediction of pathologic stage.…”

Usefulness of Breast MRI for Safe Omission of Axillary Lymph Nodes Dissection in Sentinel Node-Positive Breast Cancer Patients

“…Though MRI is not recommended as a preoperative axillary evaluation method according to guidelines such as NCCN 2017 [4], integrating the information already gathered with regard to primary lesions and occult axillary lymph node risk will come at minimal additional cost. The capacity of breast MRI to predict additional axillary lymph node status is highly relevant for care of patients with SN+ findings [15]. Although AUS has been reported to have a similar specificity but better sensitivity than MRI [14], AUS is a subjective examination and it has some problems.…”

Multiparametric evaluation of preoperative MRI in early stage breast cancer: prognostic impact of peri-tumoral fat

“…In particular, tumor morphology, density and enhancement characteristics, as determined on MRI, have been shown as significant in differentiating breast cancers subtypes [16,17]. Similarly, prior investigations have associated certain breast tumor features to axillary nodal status using dynamic contrast-enhanced (DCE) MRI [18,19]. However, little is known about the role of the fat environment in predicting such adverse pathologic factors.…”