Quantitative assessment of distant recurrence risk in early stage breast cancer using a nonlinear combination of pathological, clinical and imaging variables.
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.
Duke Scholars
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Related Subject Headings
- Risk Assessment
- Optoelectronics & Photonics
- Neoplasm Staging
- Neoplasm Recurrence, Local
- Humans
- Breast Neoplasms
- 3404 Medicinal and biomolecular chemistry
- 3401 Analytical chemistry
- 1004 Medical Biotechnology
- 0304 Medicinal and Biomolecular Chemistry
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Risk Assessment
- Optoelectronics & Photonics
- Neoplasm Staging
- Neoplasm Recurrence, Local
- Humans
- Breast Neoplasms
- 3404 Medicinal and biomolecular chemistry
- 3401 Analytical chemistry
- 1004 Medical Biotechnology
- 0304 Medicinal and Biomolecular Chemistry