Using deep learning to identify bladder cancers with FGFR-activating mutations from histology images.
Journal:
Cancer medicine
Published Date:
Jul 1, 2021
Abstract
BACKGROUND: In recent years, the fibroblast growth factor receptor (FGFR) pathway has been proven to be an important therapeutic target in bladder cancer. FGFR-targeted therapies are effective for patients with FGFR mutation, which can be discovered through genetic sequencing. However, genetic sequencing is not commonly performed at diagnosis, whereas a histologic assessment of the tumor is. We aim to computationally extract imaging biomarkers from existing tumor diagnostic slides in order to predict FGFR alterations in bladder cancer.
Authors
Keywords
Databases, Factual
Deep Learning
Female
Gene Expression
Humans
Logistic Models
Lymphocytes, Tumor-Infiltrating
Male
Molecular Targeted Therapy
Mutation
Neural Networks, Computer
Receptor, Fibroblast Growth Factor, Type 2
Receptor, Fibroblast Growth Factor, Type 3
Receptors, Fibroblast Growth Factor
Sensitivity and Specificity
Urinary Bladder Neoplasms