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Quantitative ultrasound image analysis of axillary lymph node status in breast cancer patients

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Ultrasonography has the potential to accurately stage breast cancer with automated analysis to detect axillary lymph node metastasis. The aim of this study was to develop and test automated quantitative ultrasound image analysis of axillary lymph nodes for breast cancer staging.

Methods

Following an IRB-approved HIPAA compliant protocol, ultrasound images of 90 breast cancer patients presenting for lymph node assessment were retrospectively collected. There were 51 node-positive and 39 node-negative patients, yielding images of 223 lymph nodes (109 positive for metastasis and 114 negative for metastasis). The analysis was completely automated apart from the manual indication of the approximate center of each lymph node. Mathematical descriptors of the nodes, which served as image-based biomarkers, were computer-extracted and input to a classifier for the task of distinguishing between positive (i.e., metastatic) and negative lymph nodes. The performance of this task was assessed using receiver operating characteristic (ROC) analysis with evaluation by-node and by-patient using the area under the ROC curve (AUC) as the performance metric.

Results

The AUC was 0.85 (standard error 0.03) for by-node evaluation when distinguishing between positive and negative lymph nodes. The AUC was 0.87 (0.04) for patient-based prognosis, i.e., assessing whether patients were lymph node-positive or lymph node-negative.

Conclusion

Based on these classification results, we conclude that mathematical descriptors of sonographically imaged lymph nodes may be useful as prognostic biomarkers in breast cancer staging and demonstrate potential for predicting patient lymph node status.

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Acknowledgments

This work was supported in part by Department of Energy grant DE-FG02-08ER6478, National Institute of Health grants NIH P50-CA125183 and NIH S10 RR021039, and a research grant from Philips Medical Research.

Conflict of iInterest

M.L.G. is a stockholder in R2 Technology/Hologic is cofounder & equity holder in Quantitative Insights, Inc., and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi and Toshiba. K.D. received royalties from Hologic. It is the University of Chicago Conflict of Interest Policy that investigators disclose publicly actual or potential significant financial interest that would reasonably appear to be directly and significantly affected by the research activities.

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Authors and Affiliations

  1. University of Chicago, Chicago, IL, USA

    Karen Drukker, Maryellen Giger, Lina Arbash Meinel, Adam Starkey, Jyothi Janardanan & Hiroyuki Abe

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  1. Karen Drukker
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  2. Maryellen Giger
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Correspondence to Karen Drukker.

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Drukker, K., Giger, M., Meinel, L.A. et al. Quantitative ultrasound image analysis of axillary lymph node status in breast cancer patients. Int J CARS 8, 895–903 (2013). https://doi.org/10.1007/s11548-013-0829-3

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  • DOI: https://doi.org/10.1007/s11548-013-0829-3

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