Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning with Integrative Imaging, Molecular and Demographic Data

Hongyi Duanmu, Pauline Boning Huang, Srinidhi Brahmavar, Stephanie Lin, Thomas Ren, Jun Kong, Fusheng Wang, Tim Q. Duong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Neoadjuvant chemotherapy is widely used to reduce tumor size to make surgical excision manageable and to minimize distant metastasis. Assessing and accurately predicting pathological complete response is important in treatment planing for breast cancer patients. In this study, we propose a novel approach integrating 3D MRI imaging data, molecular data and demographic data using convolutional neural network to predict the likelihood of pathological complete response to neoadjuvant chemotherapy in breast cancer. We take post-contrast T1-weighted 3D MRI images without the need of tumor segmentation, and incorporate molecular subtypes and demographic data. In our predictive model, MRI data and non-imaging data are convolved to inform each other through interactions, instead of a concatenation of multiple data type channels. This is achieved by channel-wise multiplication of the intermediate results of imaging and non-imaging data. We use a subset of curated data from the I-SPY-1 TRIAL of 112 patients with stage 2 or 3 breast cancer with breast tumors underwent standard neoadjuvant chemotherapy. Our method yielded an accuracy of 0.83, AUC of 0.80, sensitivity of 0.68 and specificity of 0.88. Our model significantly outperforms models using imaging data only or traditional concatenation models. Our approach has the potential to aid physicians to identify patients who are likely to respond to neoadjuvant chemotherapy at diagnosis or early treatment, thus facilitate treatment planning, treatment execution, or mid-treatment adjustment.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages242-252
Number of pages11
ISBN (Print)9783030597122
DOIs
StatePublished - 2020
Externally publishedYes
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: Oct 4 2020Oct 8 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12262 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/TerritoryPeru
CityLima
Period10/4/2010/8/20

Keywords

  • Artificial intelligence
  • Convolutional neural network
  • Magnetic resonance imaging

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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