Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage
Open accessAbstract
BACKGROUND: The objective of this study was to demonstrate that computer‐extracted image phenotypes (CEIPs) of biopsy‐proven breast cancer on magnetic resonance imaging (MRI) can accurately predict pathologic stage. METHODS: The authors used a data set of deidentified breast MRIs organized by the National Cancer Institute in The Cancer Imaging Archive. In total, 91 biopsy‐proven breast cancers were analyzed from patients who had information available on pathologic stage (stage I, n = 22; stage II, n = 58; stage III, n = 11) and surgically verified lymph node status (negative lymph nodes, n = 46; ≥ 1 positive lymph node, n = 44; no lymph nodes examined, n = 1). Tumors were characterized according to 1) radiologist‐measured size and 2) CEIP. Then, models were built that combined 2 CEIPs to predict tumor pathologic stage and lymph node involvement, and the models were evaluated in a leave‐1‐out, cross‐validation analysis with the area under the receiver operating characteristic curve (AUC) as the value of interest. RESULTS: Tumor size was the most powerful predictor of pathologic stage, but CEIPs that captured biologic behavior also emerged as predictive (eg, stage I and II vs stage III demonstrated an AUC of 0.83). No size measure was successful in the prediction of positive lymph nodes, but adding a CEIP that described tumor “homogeneity” significantly improved discrimination (AUC = 0.62; P = .003) compared with chance. CONCLUSIONS: The current results indicate that MRI phenotypes have promise for predicting breast cancer pathologic stage and lymph node status. Cancer 2016;122:748–757. © 2015 American Cancer Society.
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- Authors
- Burnside, E. S., Drukker, K., Li, H., Bonaccio, E., Zuley, M., Ganott, M., Net, J. M., Sutton, E. J., Brandt, K. R., Whitman, G. J., Conzen, S. D., Lan, L., Ji, Y., Zhu, Y., Jaffe, C. C., Huang, E. P., Freymann, J. B., Kirby, J. S., Morris, E. A., & Giger, M. L.
- Affiliations
- University of Wisconsin–Madison (US)
- University of Chicago (US)
- Roswell Park Comprehensive Cancer Center (US)
- University of Pittsburgh (US)
- University of Miami (US)
- Memorial Sloan Kettering Cancer Center (US)
- Mayo Clinic (US)
- The University of Texas MD Anderson Cancer Center (US)
- Department of Radiology University of Chicago Chicago Illinois
- Endeavor Health (US)
- National Institutes of Health (US)
- Corresponding author
- Morris, Elizabeth A.
- Giger, Maryellen L.
- ORCID
- Venue
- Cancer
- Publisher
- Wiley
- Published
- 2015-11-30
- Volume
- 122
- Issue
- 5
- Pages
- 748-757
- ISSN
- 0008-543X (ISSN-L)
- 1097-0142
- PMC
- PMC4764425
- OpenAlex
- W2184968530
- Cited by
- 71
- References
- 35
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 7 · 2022: 7 · 2021: 9 · 2020: 10 · 2019: 10 · 2018: 11 · 2017: 7 · 2016: 2
- FWCI
- 5.40
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cancer (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (R01CA165229, R01LM010921, K24CA194251, T32 EB002103)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- breast cancer, breast MRI, lymph node status, tumor size, radiomics, The Cancer Imaging Archive
- MeSH terms
- Adult, Aged, Aged, 80 and over, Breast Neoplasms, Female, Humans, Image Processing, Computer-Assisted, Lymph Nodes, Magnetic Resonance Imaging, Middle Aged, Neoplasm Staging, Phenotype, Prognosis, ROC Curve, Carcinoma, Ductal, Breast, Carcinoma, Lobular
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