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

Publication ,  Journal Article
Saha, A; Harowicz, MR; Grimm, LJ; Kim, CE; Ghate, SV; Walsh, R; Mazurowski, MA
Published in: Br J Cancer
August 2018

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.

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Published In

Br J Cancer

DOI

EISSN

1532-1827

Publication Date

August 2018

Volume

119

Issue

4

Start / End Page

508 / 516

Location

England

Related Subject Headings

  • Young Adult
  • Receptors, Progesterone
  • Receptors, Estrogen
  • Receptor, erbB-2
  • Receptor, ErbB-2
  • Oncology & Carcinogenesis
  • Middle Aged
  • Magnetic Resonance Imaging
  • Machine Learning
  • Humans
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Saha, A., Harowicz, M. R., Grimm, L. J., Kim, C. E., Ghate, S. V., Walsh, R., & Mazurowski, M. A. (2018). A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features. Br J Cancer, 119(4), 508–516. https://doi.org/10.1038/s41416-018-0185-8
Saha, Ashirbani, Michael R. Harowicz, Lars J. Grimm, Connie E. Kim, Sujata V. Ghate, Ruth Walsh, and Maciej A. Mazurowski. “A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.Br J Cancer 119, no. 4 (August 2018): 508–16. https://doi.org/10.1038/s41416-018-0185-8.
Saha A, Harowicz MR, Grimm LJ, Kim CE, Ghate SV, Walsh R, et al. A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features. Br J Cancer. 2018 Aug;119(4):508–16.
Saha, Ashirbani, et al. “A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.Br J Cancer, vol. 119, no. 4, Aug. 2018, pp. 508–16. Pubmed, doi:10.1038/s41416-018-0185-8.
Saha A, Harowicz MR, Grimm LJ, Kim CE, Ghate SV, Walsh R, Mazurowski MA. A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features. Br J Cancer. 2018 Aug;119(4):508–516.

Published In

Br J Cancer

DOI

EISSN

1532-1827

Publication Date

August 2018

Volume

119

Issue

4

Start / End Page

508 / 516

Location

England

Related Subject Headings

  • Young Adult
  • Receptors, Progesterone
  • Receptors, Estrogen
  • Receptor, erbB-2
  • Receptor, ErbB-2
  • Oncology & Carcinogenesis
  • Middle Aged
  • Magnetic Resonance Imaging
  • Machine Learning
  • Humans