MRI Volume Changes of Axillary Lymph Nodes as Predictor of Pathologic Complete Responses to Neoadjuvant Chemotherapy in Breast Cancer.INTRODUCTION
 This study aims to determine whether axillary lymph node (aLN) volume by magnetic resonance imaging (MRI) could augment the prediction accuracy of treatment response to neoadjuvant chemotherapy (NAC).


 MRI was acquired pre-, during, and post-NAC.  A subset with visible aLNs on MRI was identified (N = 132).  aLN MRI offers clinically relevant information and has the potential to predict treatment response to NAC in patients with breast cancer.
MRI
Level-2a curated data from the I-SPY-1 TRIAL (2002-2006) were used. 
I-SPY-1 TRIAL
MRI Volume Changes of Axillary Lymph Nodes as Predictor of Pathologic Complete Responses to Neoadjuvant Chemotherapy in Breast Cancer.INTRODUCTION
 Longitudinal monitoring of breast tumor volume over the course of chemotherapy is informative of pathologic response.  This study aims to determine whether axillary lymph node (aLN) volume by magnetic resonance imaging (MRI) could augment the prediction accuracy of treatment response to neoadjuvant chemotherapy (NAC).


 Level-2a curated data from the I-SPY-1 TRIAL (2002-2006) were used.  Patients had stage 2 or 3 breast cancer.  MRI was acquired pre-, during, and post-NAC.  Prediction of pathologic complete response (PCR) was made using breast tumor volume changes, nodal volume changes, and combined breast tumor and nodal volume changes with sub-stratification with and without large lymph nodes (3 mL or ∼1.79 cm diameter cutoff).  Receiver operating characteristic curve analysis was used to quantify prediction performance.


 The rate of change of aLN and breast tumor volume were informative of pathologic response, with prediction being most informative early in treatment (area under the curve (AUC), 0.57-0.87) compared with later in treatment (AUC, 0.50-0.75).  Larger aLN volume was associated with hormone receptor negativity, with the largest nodal volume for triple negative subtypes.  Sub-stratification by node size improved predictive performance, with the best predictive model for large nodes having AUC of 0.87.


 CONCLUSION
 aLN MRI offers clinically relevant information and has the potential to predict treatment response to NAC in patients with breast cancer.
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