Predicting patient outcomes with machine learning of cancer histopathology images
Principal Investigator
Name
Moritz Gerstung
Degrees
Ph.D
Institution
EMBL-EBI
Position Title
Group leader
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-463
Initial CDAS Request Approval
Mar 21, 2019
Title
Predicting patient outcomes with machine learning of cancer histopathology images
Summary
Histopathology imaging, notably Hematoxylin and Eosin stained (H&E) images are widely used for cancer diagnosis, classification and prognosis. Unprecedented progress in computer vision has made the accurate quantification of digitalised histopathology images possible. The aim of this research proposal is to explore the utility of machine learning algorithms for disease diagnosis and prognosis. Deep convolutional neural network will be trained for classifying tumor and normal tissues of lung, ovarian and colorectal cancer and to extract histopathology patterns characteristic of different diseases and cell types. These features then feed into statistical models to predict tumor characterisations such as tumor lymphocyte infiltrating score and tumor proliferation rate. Finally, we will combine imaging features and clinical data to improve the survival predictions.
Aims
1. Extract imaging features from H&E images that can accurately distinguish tumor and normal tissues of lung, ovarian and colorectal cancer.
2. Predict tumor characteristics such as tumor infiltrating lymphocyte scores and tumor proliferation rate using imaging features.
3. Integrate imaging features and clinical follow up data to train prognostic models.
Collaborators
Yu Fu, yufu@ebi.ac.uk, EMBL-EBI
Alexander Jung, alexwjung@ebi.ac.uk, EMBL-EBI
Ramon Vinas, ramon.torne.17@ucl.ac.uk, UCL, EMBL-EBI