Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.
Journal:
International journal of computer assisted radiology and surgery
Published Date:
Sep 1, 2020
Abstract
PURPOSE: Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. Computational analysis of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) through deep convolution neural network (CNN) has shown a significant performance to distinguish responders and no responder's patients. This study intends to present a new deep learning (DL) model predicting the breast cancer response to NAC based on multiple MRI inputs.
Authors
Keywords
Adult
Antineoplastic Agents
Area Under Curve
Breast Neoplasms
Chemotherapy, Adjuvant
Contrast Media
Deep Learning
Diagnosis, Computer-Assisted
Female
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Middle Aged
Neoadjuvant Therapy
Neural Networks, Computer
Retrospective Studies
ROC Curve
Treatment Outcome