A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.

ABSTRACT:

Background

Recent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship.

Methods

We analysed a set of 922 patients with invasive breast cancer and pre-operative MRI. The MRIs were analysed by a computer algorithm to extract 529 features of the tumour and the surrounding tissue. Machine-learning-based models based on the imaging features were trained using a portion of the data (461 patients) to predict the following molecular, genomic, and proliferation characteristics: tumour surrogate molecular subtype, oestrogen receptor, progesterone receptor and human epidermal growth factor status, as wel

SUBMITTER: Saha A 

PROVIDER: S-EPMC6134102 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature