TITLE:
A Predictive Model for Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy Using Machine Learning
AUTHORS:
Isaac Kim, Kwanbum Lee, Seung Ah Lee, Yeon Hee Park, Seung Ki Kim
KEYWORDS:
Breast Neoplasm, Neoadjuvant Therapy, Chemotherapy, Response, Prediction
JOURNAL NAME:
Advances in Breast Cancer Research,
Vol.10 No.4,
August
26,
2021
ABSTRACT: Background: In patients with breast cancer after Neoadjuvant Chemotherapy (NAC), pathological
Complete Response (pCR) was associated with better long-term outcomes. We here attempted to predict pCR using machine learning. Patients and Methods: From 2008 to 2017, 1308 breast
cancer patients underwent NAC before surgery, of whom 377 patients underwent
Cancer SCANTM for gene data. Of 377, 238 were analyzed here, with 139 excluded due to
incomplete medical data. Results: The pCR (-) vs. (+) group had 200 vs. 38 patients. In our predictive model with
gene data, the Area Under the Curve (AUC) of
the Receiver Operating Characteristic (ROC) curve was 0.909 and accuracy
was 0.875. In another model without gene data, the AUC of ROC curve was 0.743
and accuracy was 0.800. We also conducted internal validation with 72 patients
undergoing NAC and Cancer SCANTM during July 2017 and April 2018. When we applied a 0.4
threshold value, accuracy was 0.806 and
0.778 in the predictive model with vs. without gene profiles, respectively. Conclusion: The
present predictive model may be a useful and easy-to-access tool for pCR-prediction in breast cancer
patients treated with NAC.