Deep learning predicts breast cancer recurrence in analysis of consecutive MRIs acquired during the course of neoadjuvant chemotherapy

Karen Drukker; Alexandra Edwards; John Papaioannou; Maryellen Giger

doi:10.1117/12.2549044

16 March 2020 Deep learning predicts breast cancer recurrence in analysis of consecutive MRIs acquired during the course of neoadjuvant chemotherapy

Karen Drukker, Alexandra Edwards, John Papaioannou, Maryellen Giger

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 1131410 (2020) https://doi.org/10.1117/12.2549044
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

The purpose of this study was to assess long short-term memory networks in the prediction of recurrence-free survival in breast cancer patients using features extracted from MRIs acquired during the course of neoadjuvant chemotherapy. In the I-SPY1 dataset, up to 4 MRI exams were available per patient acquired at pre-treatment, early-treatment, interregimen, and pre-surgery time points. Breast cancers were automatically segmented and 8 features describing kinetic curve characteristics were extracted. We assessed performance of long short-term memory networks in the prediction of recurrence-free survival status at 2 years and at 5 years post-surgery. For these predictions, we analyzed MRIs from women who had at least 2 (or 5) years of recurrence-free follow-up or experienced recurrence or death within that timeframe: 157 women and 73 women, respectively. One approach used features extracted from all available exams and the other approach used features extracted from only exams prior to the second cycle of neoadjuvant chemotherapy. The areas under the ROC curve in the prediction of recurrence-free survival status at 2 years post-surgery were 0.80, 95% confidence interval [0.68; 0.88] and 0.75 [0.62; 0.83] for networks trained with all 4 available exams and only the ‘early’ exams, respectively. Hazard ratios at the lowest, median, and highest quartile cut -points were 6.29 [2.91; 13.62], 3.27 [1.77; 6.03], 1.65 [0.83; 3.27] and 2.56 [1.20; 5.48], 3.01 [1.61; 5.66], 2.30 [1.14; 4.67]. Long short-term memory networks were able to predict recurrence-free survival in breast cancer patients, also when analyzing only MRIs acquired ‘early on’ during neoadjuvant treatment.

Conference Presentation

Citation Download Citation

Karen Drukker, Alexandra Edwards, John Papaioannou, and Maryellen Giger "Deep learning predicts breast cancer recurrence in analysis of consecutive MRIs acquired during the course of neoadjuvant chemotherapy", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131410 (16 March 2020); https://doi.org/10.1117/12.2549044

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available