Document Detail


Viable tumor tissue detection in murine metastatic breast cancer by whole-body MRI and multispectral analysis.
MedLine Citation:
PMID:  19859948     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Whole-body MRI combined with a semiautomated hierarchical multispectral image analysis technique was evaluated as a method for detecting viable tumor tissue in a murine model of metastatic breast cancer (4T1 cell line). Whole-body apparent diffusion coefficient, T(2), and proton density maps were acquired in this study. The viable tumor tissue segmentation included three-stage k-means clustering of the parametric maps, morphologic operations, application of a size threshold, and reader discrimination of the segmented objects. The segmentation results were validated by histologic evaluation, and the detection accuracy of the technique was evaluated at three size thresholds (15, 100, and 500 voxels). The accuracy was 88.9% for a 500-voxel size threshold, and the area under receiver operating characteristic curve was 0.84. The regions of segmented viable tumor tissue within the primary tumors were found mostly on the periphery of the tumors in agreement with the histologic findings. The presented technique was found capable of detecting metastases and segmenting the viable tumor from necrotic regions within tumors found in this model. It offers a noninvasive, whole-body, viable tumor tissue detection method for preclinical and potentially clinical applications such as tumor screening and evaluating therapeutic efficacy.
Authors:
Kai H Barck; Brandon Willis; Jed Ross; Dorothy M French; Ellen H Filvaroff; Richard A D Carano
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Publication Detail:
Type:  Evaluation Studies; Journal Article    
Journal Detail:
Title:  Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine     Volume:  62     ISSN:  1522-2594     ISO Abbreviation:  Magn Reson Med     Publication Date:  2009 Dec 
Date Detail:
Created Date:  2009-11-30     Completed Date:  2010-02-22     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8505245     Medline TA:  Magn Reson Med     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1423-30     Citation Subset:  IM    
Copyright Information:
(c) 2009 Wiley-Liss, Inc.
Affiliation:
Department of Tumor Biology and Angiogenesis, Genentech, Inc., South San Francisco, California 94080, USA.
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Animals
Artificial Intelligence*
Breast Neoplasms / diagnosis*,  secondary
Cell Line, Tumor
Image Enhancement / methods
Image Interpretation, Computer-Assisted / methods*
Magnetic Resonance Imaging / methods*
Mice
Mice, Inbred BALB C
Mice, Inbred C3H
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity
Whole Body Imaging / methods*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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