Citations 2
NCCN Guidelines® Insights: Breast Cancer Screening and Diagnosis, Version 1.2023
Bevers, Therese B. , Niell, Bethany L. , Bonaccio, Ermelinda, Camp, Melissa , Chikarmane, Sona A. , Eghtedari, Mohammad , Flanagan, Meghan R. , Hawley, Jeffrey , Helvie, Mark A. , Hodgkiss, Linda, Hoyt, Tamarya L., Ivanovich, Jennifer , Kulkarni, Swati , Lancaster, Rachael, Mauer, Caitlin , Pearlman, Mark D. , Philpotts, Liane E. , Plichta, Jennifer K. , Shakeri, Shadi Aminololama, Smith, Mary Lou , Streibert, Clarie L., Strigel, Roberta M. , Tumyan, Lusine , Winkler, Nicole , Wolverton, Dulcy , Bergman, Mary Anne, Stehman, Katie, Plichta, Jennifer K , Baker, Jennifer L. , Bennett, Debbie Lee , Conant, Emily F. , Jochelson, Maxine S. , Maxwell, Jessica, Patel, Bhavika , Plecha, Donna , Kumar, Rashmi
Journal of the National Comprehensive Cancer Network | September 6, 2023 | Volume: 21 | Issue: 9 | Pages: 900-909
The NCCN Guidelines for Breast Cancer Screening and Diagnosis provide health care providers with a practical, consistent framework for screening and evaluating a spectrum of clinical presentations and breast lesions. The NCCN Breast Cancer Screening and Diagnosis Panel is composed of a multidisciplinary team of experts in the field, including representation from medical oncology, gynecologic oncology, surgical oncology, internal medicine, family practice, preventive medicine, pathology, diagnostic and interventional radiology, as well as patient advocacy. The NCCN Breast Cancer Screening and Diagnosis Panel meets at least annually to review emerging data and comments from reviewers within their institutions to guide updates to existing recommendations. These NCCN Guidelines Insights summarize the panel’s decision-making and discussion surrounding the most recent updates to the guideline’s screening recommendations.
Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage
Drukker, Karen , Li, Hui , Bonaccio, Ermelinda, Zuley, Margarita L. , Ganott, Marie A., Net, Jose , Sutton, Elizabeth J. , Brandt, Kathleen R. , Conzen, Suzanne D. , Li, Lan , Zhu, Yitan , Jaffe, C. Carl, Huang, Erich P. , Freymann, John , Kirby, Justin , Morris, Elizabeth A. , Burnside, Elizabeth S. , Whitman, Gary J. , Ji, Yuan , Giger, Maryellen , Drukker, Karen
Cancer | November 30, 2015 | Volume: 122 | Issue: 5 | Pages: 748-757
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.