A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI

Kavya Ravichandran; Nathaniel Braman; Andrew Janowczyk; Anant Madabhushi

doi:10.1117/12.2294056

27 February 2018 A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI

Kavya Ravichandran, Nathaniel Braman, Andrew Janowczyk, Anant Madabhushi

Author Affiliations +

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105750C (2018) https://doi.org/10.1117/12.2294056
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Neoadjuvant chemotherapy (NAC) is routinely used to treat breast tumors before surgery to reduce tumor size and improve outcome. However, no current clinical or imaging metrics can effectively predict before treatment which NAC recipients will achieve pathological complete response (pCR), the absence of residual invasive disease in the breast or lymph nodes following surgical resection. In this work, we developed and applied a convolu- tional neural network (CNN) to predict pCR from pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans on a per-voxel basis. In this study, DCE-MRI data for a total of 166 breast cancer pa- tients from the ISPY1 Clinical Trial were split into a training set of 133 patients and a testing set of 33 patients. A CNN consisting of 6 convolutional blocks was trained over 30 epochs. The pre-contrast and post-contrast DCE-MRI phases were considered in isolation and conjunction. A CNN utilizing a combination of both pre- and post-contrast images best distinguished responders, with an AUC of 0.77; 82% of the patients in the testing set were correctly classified based on their treatment response. Within the testing set, the CNN was able to produce probability heatmaps that visualized tumor regions that most strongly predicted therapeutic response. Multi- variate analysis with prognostic clinical variables (age, largest diameter, hormone receptor and HER2 status), revealed that the network was an independent predictor of response (p=0.05), and that the inclusion of HER2 status could further improve capability to predict response (AUC = 0.85, accuracy = 85%).

Conference Presentation

Citation Download Citation

Kavya Ravichandran, Nathaniel Braman, Andrew Janowczyk, and Anant Madabhushi "A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105750C (27 February 2018); https://doi.org/10.1117/12.2294056

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