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Proceedings Paper

Identifying metastatic breast tumors using textural kinetic features of a contrast based habitat in DCE-MRI
Author(s): Baishali Chaudhury; Mu Zhou; Dmitry B. Goldgof; Lawrence O. Hall; Robert A. Gatenby; Robert J. Gillies; Jennifer S. Drukteinis
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Paper Abstract

The ability to identify aggressive tumors from indolent tumors using quantitative analysis on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) would dramatically change the breast cancer treatment paradigm. With this prognostic information, patients with aggressive tumors that have the ability to spread to distant sites outside of the breast could be selected for more aggressive treatment and surveillance regimens. Conversely, patients with tumors that do not have the propensity to metastasize could be treated less aggressively, avoiding some of the morbidity associated with surgery, radiation and chemotherapy. We propose a computer aided detection framework to determine which breast cancers will metastasize to the loco-regional lymph nodes as well as which tumors will eventually go on to develop distant metastses using quantitative image analysis and radiomics. We defined a new contrast based tumor habitat and analyzed textural kinetic features from this habitat for classification purposes. The proposed tumor habitat, which we call combined-habitat, is derived from the intersection of two individual tumor sub-regions: one that exhibits rapid initial contrast uptake and the other that exhibits rapid delayed contrast washout. Hence the combined-habitat represents the tumor sub-region within which the pixels undergo both rapid initial uptake and rapid delayed washout. We analyzed a dataset of twenty-seven representative two dimensional (2D) images from volumetric DCE-MRI of breast tumors, for classification of tumors with no lymph nodes from tumors with positive number of axillary lymph nodes. For this classification an accuracy of 88.9% was achieved. Twenty of the twenty-seven patients were analyzed for classification of distant metastatic tumors from indolent cancers (tumors with no lymph nodes), for which the accuracy was 84.3%.

Paper Details

Date Published: 20 March 2015
PDF: 6 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941415 (20 March 2015); doi: 10.1117/12.2081386
Show Author Affiliations
Baishali Chaudhury, Univ. of South Florida (United States)
Mu Zhou, Univ. of South Florida (United States)
Dmitry B. Goldgof, Univ. of South Florida (United States)
Lawrence O. Hall, Univ. of South Florida (United States)
Robert A. Gatenby, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Robert J. Gillies, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Jennifer S. Drukteinis, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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