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Proceedings Paper

Predicting axillary lymph node metastasis from kinetic statistics of DCE-MRI breast images
Author(s): Ahmed B. Ashraf; Lilie Lin; Sara C. Gavenonis; Carolyn Mies; Eric Xanthopoulos; Despina Kontos
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Paper Abstract

The presence of axillary lymph node metastases is the most important prognostic factor in breast cancer and can influence the selection of adjuvant therapy, both chemotherapy and radiotherapy. In this work we present a set of kinetic statistics derived from DCE-MRI for predicting axillary node status. Breast DCE-MRI images from 69 women with known nodal status were analyzed retrospectively under HIPAA and IRB approval. Axillary lymph nodes were positive in 12 patients while 57 patients had no axillary lymph node involvement. Kinetic curves for each pixel were computed and a pixel-wise map of time-to-peak (TTP) was obtained. Pixels were first partitioned according to the similarity of their kinetic behavior, based on TTP values. For every kinetic curve, the following pixel-wise features were computed: peak enhancement (PE), wash-in-slope (WIS), wash-out-slope (WOS). Partition-wise statistics for every feature map were calculated, resulting in a total of 21 kinetic statistic features. ANOVA analysis was done to select features that differ significantly between node positive and node negative women. Using the computed kinetic statistic features a leave-one-out SVM classifier was learned that performs with AUC=0.77 under the ROC curve, outperforming the conventional kinetic measures, including maximum peak enhancement (MPE) and signal enhancement ratio (SER), (AUCs of 0.61 and 0.57 respectively). These findings suggest that our DCE-MRI kinetic statistic features can be used to improve the prediction of axillary node status in breast cancer patients. Such features could ultimately be used as imaging biomarkers to guide personalized treatment choices for women diagnosed with breast cancer.

Paper Details

Date Published: 23 February 2012
PDF: 7 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831525 (23 February 2012); doi: 10.1117/12.911576
Show Author Affiliations
Ahmed B. Ashraf, The Univ. of Pennsylvania Health System (United States)
Lilie Lin, The Univ. of Pennsylvania Health System (United States)
Sara C. Gavenonis, The Univ. of Pennsylvania Health System (United States)
Carolyn Mies, The Univ. of Pennsylvania Health System (United States)
Eric Xanthopoulos, The Univ. of Pennsylvania Health System (United States)
Despina Kontos, The Univ. of Pennsylvania Health System (United States)

Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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