Quantitative assessment of distant recurrence risk in early stage breast cancer using a nonlinear combination of pathological, clinical and imaging variables.

Published

Journal Article

Use of genomic assays to determine distant recurrence risk in patients with early stage breast cancer has expanded and is now included in the American Joint Committee on Cancer staging manual. Algorithmic alternatives using standard clinical and pathology information may provide equivalent benefit in settings where genomic tests, such as OncotypeDx, are unavailable. We developed an artificial neural network (ANN) model to nonlinearly estimate risk of distant cancer recurrence. In addition to clinical and pathological variables, we enhanced our model using intraoperatively determined global mammographic breast density (MBD) and local breast density (LBD). LBD was measured with optical spectral imaging capable of sensing regional concentrations of tissue constituents. A cohort of 56 ER+ patients with an OncotypeDx score was evaluated. We demonstrated that combining MBD/LBD measurements with clinical and pathological variables improves distant recurrence risk prediction accuracy, with high correlation (r = 0.98) to the OncotypeDx recurrence score.

Full Text

Duke Authors

Cited Authors

  • Nichols, BS; Chelales, E; Wang, R; Schulman, A; Gallagher, J; Greenup, RA; Geradts, J; Harter, J; Marcom, PK; Wilke, LG; Ramanujam, N

Published Date

  • October 2020

Published In

Volume / Issue

  • 13 / 10

Start / End Page

  • e201960235 -

PubMed ID

  • 32573935

Pubmed Central ID

  • 32573935

Electronic International Standard Serial Number (EISSN)

  • 1864-0648

Digital Object Identifier (DOI)

  • 10.1002/jbio.201960235

Language

  • eng

Conference Location

  • Germany