ArticleCancer2016
Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage.
Article in Cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 73 citations in OpenAlex.
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- Clinical Artificial Intelligence Applications: Breast Imaging.Radiologic clinics of North America · 2021Review
- Robustness of radiomic features of benign breast lesions and hormone receptor positive/HER2-negative cancers across DCE-MR magnet strengths.Magnetic resonance imaging · 2021Article
- Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes.BMC cancer · 2021Article
- An analysis of Ki-67 expression in stage 1 invasive ductal breast carcinoma using apparent diffusion coefficient histograms.Quantitative imaging in medicine and surgery · 2021Article
- Changes in Diffuse Optical Tomography Images During Early Stages of Neoadjuvant Chemotherapy Correlate with Tumor Response in Different Breast Cancer Subtypes.Clinical cancer research : an official journal of the American Association for Cancer Research · 2021Article
- Machine learning in breast MRI.Journal of magnetic resonance imaging : JMRI · 2020Review
- Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy.European journal of hybrid imaging · 2020Review
- AI in Medical Imaging Informatics: Current Challenges and Future Directions.IEEE journal of biomedical and health informatics · 2020Article
- Article
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Corrections and comments
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Authors and funding
20 authors at 11 institutions in 1 country.
Funding
Abstract
backgroundThe 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.
methodsThe 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.
resultsTumor 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.
conclusionsThe 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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