The following represents disclosure information provided by authors of this abstract. The program committee has reviewed all presenting author disclosure reports, identified potential conflicts of interest, and implemented strategies to manage those areas of conflict, where appropriate. All relationships are considered compensated. Relationships are self-held unless otherwise noted. I = Immediate Family Member, Inst = My Institution
 
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Deep learning algorithm to predict pathologic complete response to neoadjuvant chemotherapy for breast cancer prior to treatment.
 
Rachel Choi
No Relationships to Disclose
 
Marina Joel
No Relationships to Disclose
 
Miles Hui
No Relationships to Disclose
 
Sanjay Aneja
Consulting or Advisory Role - Prophet Consulting (I)
Research Funding - AHRQ; American Cancer Society; American Society of Clinical Oncology; National Cancer Institute; National Science Foundation; theMednet
Patents, Royalties, Other Intellectual Property - Provisional patent of deep learning optimization algorithm
Travel, Accommodations, Expenses - Prophet Consulting (I); The Hope Foundation
Other Relationship - ASCO; JCO Clinical Cancer Informatics; NRG Oncology; SWOG