Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage.
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 Nat...
| Publicado en: | Cancer (0008543X) Vol. 122; no. 5; pp. 748 - 758 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Mar2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=113205741&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113205741 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0008543X 23I jtl: Cancer (0008543X) issn: 0008543X maglogo: Y pubinfo: dt: Mar2016 vid: 122 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 113205741 113205741 NLM26619259 113205741 10.1002/cncr.29791 NLM26619259 PMC4764425 [Available on 03/01/17] 113205741 ppf: 748 ppct: 10 formats: tig: atl: Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage. aug: au: Burnside, Elizabeth S. Drukker, Karen Li, Hui Bonaccio, Ermelinda Zuley, Margarita Ganott, Marie Net, Jose M. Sutton, Elizabeth J. Brandt, Kathleen R. Whitman, Gary J. Conzen, Suzanne D. Lan, Li Ji, Yuan Zhu, Yitan Jaffe, Carl C. Huang, Erich P. Freymann, John B. Kirby, Justin S. Morris, Elizabeth A. Giger, Maryellen L. affil: Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison Wisconsin sug: subj: Lymph Nodes Pathology Carcinoma, Ductal, Breast Pathology Breast Neoplasms Pathology Carcinoma, Lobular Pathology Image Processing, Computer Assisted Methods Phenotype ROC Curve Adult Middle Age Neoplasm Staging Prognosis Female Magnetic Resonance Imaging Aged Aged, 80 and Over Funding Source Human Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Female ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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