Graph Convolutional Networks for Cancer Diagnosis, Prognosis and Therapeutic Response Prediction
Principal Investigator
Name
Faisal Mahmood
Degrees
Ph.D.
Institution
Brigham and Women's Hospital
Position Title
Assistant Professor
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCOI-562
Initial CDAS Request Approval
Dec 18, 2019
Title
Graph Convolutional Networks for Cancer Diagnosis, Prognosis and Therapeutic Response Prediction
Summary
Most current deep learning-based histology classification methods are limited to extracted ROIs and require pixel-level annotation. Moreover, such methods are not data-efficient and sufficiently context-aware i.e. they do not make use of the explicit spatial relationships between cells. This project aims to develop data-efficient methods for whole slide analysis using contrastive predictive coding (CPC) and graph convolutional networks (GCNs) that do not require slide level labels. This project will involve developing methods based on graph convolutional networks (GCNs) for predicting recurrence, survival, and response to treatment form histopathology images. We will use histology images available from the PLCO trial and associate them with corresponding response to treatment and patient survival.
Aims
Aim 1: To develop a data-efficient, context-aware and semi-supervised graph convolutional network (GCN)-based approaches for survival outcome and therapeutic response prediction from histology images.
Aim 2: Using contrastive predictive coding (CPC) to improve GCN performance for whole slide level classification without pixel-level annotations.
Aim 3: To develop mechanisms for integrating clinical and demographic data for improving prognostic determination.
Collaborators
Max Lu (Brigham and Women's Hospital)