TY - JOUR
T1 - MRI Volume Changes of Axillary Lymph Nodes as Predictor of Pathologic Complete Responses to Neoadjuvant Chemotherapy in Breast Cancer
AU - Cattell, Renee F.
AU - Kang, James J.
AU - Ren, Thomas
AU - Huang, Pauline B.
AU - Muttreja, Ashima
AU - Dacosta, Sarah
AU - Li, Haifang
AU - Baer, Lea
AU - Clouston, Sean
AU - Palermo, Roxanne
AU - Fisher, Paul
AU - Bernstein, Cliff
AU - Cohen, Jules A.
AU - Duong, Tim Q.
N1 - Funding Information:
The authors thank Drs Nola Hylton, Gillian Hirst, Christina Yau, and David Newitt for providing additional data from ISPY-1 (ie, detailed pathology, treatment, and survival data) that were not available on the website. The authors also acknowledge the students who have contributed to this project, including Jason Ha, Karamoko Soumahoro, Ankita Katukota, and Nikita Katukota. This work was supported in part by pilot grants from the Stony Brook Cancer Center and a Carol Baldwin pilot grant through the Stony Brook University School of Medicine. The authors also would like to acknowledge the resources of the Advanced Imaging Shared Resource of the Stony Brook Cancer Center and the Biomedical Imaging Research Center (Radiology).
Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2020/2
Y1 - 2020/2
N2 - 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). Materials and Methods: 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. A subset with visible aLNs on MRI was identified (N = 132). 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. Results: 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.
AB - 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). Materials and Methods: 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. A subset with visible aLNs on MRI was identified (N = 132). 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. Results: 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.
KW - Breast tumor volume
KW - Dynamic contrast-enhanced MRI
KW - Magnetic resonance imaging
KW - Molecular subtypes
KW - Sentinel lymph node biopsy
UR - http://www.scopus.com/inward/record.url?scp=85070733110&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070733110&partnerID=8YFLogxK
U2 - 10.1016/j.clbc.2019.06.006
DO - 10.1016/j.clbc.2019.06.006
M3 - Article
C2 - 31327729
AN - SCOPUS:85070733110
SN - 1526-8209
VL - 20
SP - 68-79.e1
JO - Clinical Breast Cancer
JF - Clinical Breast Cancer
IS - 1
ER -