Tumor image signatures and habitats: a processing pipeline of multimodality metabolic and physiological images

Daekeun You; Michelle M. Kim; Madhava P. Aryal; Hemant Parmar; Morand Piert; Theodore S. Lawrence; Yue Cao

doi:10.1117/1.JMI.5.1.011009

15 November 2017 Tumor image signatures and habitats: a processing pipeline of multimodality metabolic and physiological images

Daekeun You, Michelle M. Kim, Madhava P. Aryal, Hemant Parmar, Morand Piert, Theodore S. Lawrence, Yue Cao

Author Affiliations +

Funded by: US National Institutes of Health (NIH)

Journal of Medical Imaging, Vol. 5, Issue 1, 011009 (November 2017). https://doi.org/10.1117/1.JMI.5.1.011009

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.

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Daekeun You, Michelle M. Kim, Madhava P. Aryal, Hemant Parmar, Morand Piert, Theodore S. Lawrence, and Yue Cao "Tumor image signatures and habitats: a processing pipeline of multimodality metabolic and physiological images," Journal of Medical Imaging 5(1), 011009 (15 November 2017). https://doi.org/10.1117/1.JMI.5.1.011009

Received: 20 June 2017; Accepted: 27 October 2017; Published: 15 November 2017

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