Share Email Print

Journal of Medical Imaging • new

Tumor image signatures and habitats: a processing pipeline of multimodality metabolic and physiological images
Author(s): Daekeun You; Michelle M. Kim; Madhava P. Aryal; Hemant Parmar; Morand Piert; Theodore S. Lawrence; Yue Cao
Format Member Price Non-Member Price
PDF $20.00 $25.00

Paper Abstract

To create tumor “habitats” from the “signatures” discovered from multimodality metabolic and physiological images, we developed a framework of a processing pipeline. The processing pipeline consists of six major steps: (1) creating superpixels as a spatial unit in a tumor volume; (2) forming a data matrix { D } containing all multimodality image parameters at superpixels; (3) forming and clustering a covariance or correlation matrix { C } of the image parameters to discover major image “signatures;” (4) clustering the superpixels and organizing the parameter order of the { D } matrix according to the one found in step 3; (5) creating “habitats” in the image space from the superpixels associated with the “signatures;” and (6) pooling and clustering a matrix consisting of correlation coefficients of each pair of image parameters from all patients to discover subgroup patterns of the tumors. The pipeline was applied to a dataset of multimodality images in glioblastoma (GBM) first, which consisted of 10 image parameters. Three major image “signatures” were identified. The three major “habitats” plus their overlaps were created. To test generalizability of the processing pipeline, a second image dataset from GBM, acquired on the scanners different from the first one, was processed. Also, to demonstrate the clinical association of image-defined “signatures” and “habitats,” the patterns of recurrence of the patients were analyzed together with image parameters acquired prechemoradiation therapy. An association of the recurrence patterns with image-defined “signatures” and “habitats” was revealed. These image-defined “signatures” and “habitats” can be used to guide stereotactic tissue biopsy for genetic and mutation status analysis and to analyze for prediction of treatment outcomes, e.g., patterns of failure.

Paper Details

Date Published: 15 November 2017
PDF: 11 pages
J. Med. Imag. 5(1) 011009 doi: 10.1117/1.JMI.5.1.011009
Published in: Journal of Medical Imaging Volume 5, Issue 1
Show Author Affiliations
Daekeun You, Univ. of Michigan (United States)
Michelle M. Kim, Univ. of Michigan (United States)
Madhava P. Aryal, Univ. of Michigan (United States)
Hemant Parmar, Univ. of Michigan (United States)
Morand Piert, Univ. of Michigan (United States)
Theodore S. Lawrence, Univ. of Michigan (United States)
Yue Cao, Univ. of Michigan (United States)

© SPIE. Terms of Use
Back to Top