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

Journal Article (Journal Article)

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 well as a tumour proliferation marker (Ki-67). Trained models were evaluated on the set of the remaining 461 patients. RESULTS: Multivariate models were predictive of Luminal A subtype with AUC = 0.697 (95% CI: 0.647-0.746, p < .0001), triple negative breast cancer with AUC = 0.654 (95% CI: 0.589-0.727, p < .0001), ER status with AUC = 0.649 (95% CI: 0.591-0.705, p < .001), and PR status with AUC = 0.622 (95% CI: 0.569-0.674, p < .0001). Associations between individual features and subtypes we also found. CONCLUSIONS: There is a moderate association between tumour molecular biomarkers and algorithmically assessed imaging features.

Full Text

Duke Authors

Cited Authors

  • Saha, A; Harowicz, MR; Grimm, LJ; Kim, CE; Ghate, SV; Walsh, R; Mazurowski, MA

Published Date

  • August 2018

Published In

Volume / Issue

  • 119 / 4

Start / End Page

  • 508 - 516

PubMed ID

  • 30033447

Pubmed Central ID

  • PMC6134102

Electronic International Standard Serial Number (EISSN)

  • 1532-1827

Digital Object Identifier (DOI)

  • 10.1038/s41416-018-0185-8


  • eng

Conference Location

  • England