TCGA KIRC Cohort Cellular Morphometric Context Summarization

Background:

The Cancer Genome Atlas (TCGA) has been one of the clearing houses of genome-wide array data for the understanding of the molecular basis of cancer from large cohorts. These analyses are intrinsically from bulk measurements of mixed cell types, derived from frozen biopsy sections that include tissues with mixed histopathology and/or microanatomies (e.g., tumor, stroma). While bulk array profiling may provide insights into molecular aberrations, it provides only an average genome-wide measurement for a biopsy and fails to reveal inherent cellular composition and heterogeneity of a tumor. On the other hand, histology sections do not provide standardized measurements, but they are rich in content and continue to be the gold standard for the assessment of tissue neoplasm.

In this project, we aims to provide cellular morphometric context summarization for TCGA Kidney Renal Clear Cell Carcinoma (KIRC) Cohort, which can be further utilized for various end points, such as,

  • integrated analysis with OMIC data; and
  • construction of predictive models of clinical outcome.

Examples can be found in our previous publication:

  • Hang Chang, Ju Han, Alexander Borowsky, Leandro Loss, Jow W. Gray, Paul T. Spellman and Bahram Parvin. "Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association." IEEE Trans. on Medical Imaging, 32 4 (2013): 670-682.

Visualization Tool:  KIRC Whole Slide Images (WSI) and the corresponding segmentation results.

Description:

The files in this directory are summarized as follows:

  • README.txt provides the description of files.
  • Cellular_Morph_Context_DictionarySize=64.txt provides a datamatrix of 64-bin histograms at the patient level, where each row represents a patient and each column represents a dictionary (histogram bin).
  • Cellular_Morph_Context_DictionarySize=128.txt provides a datamatrix of 128-bin histograms at the patient level, where each row represents a patient and each column represents a dictionary (histogram bin).
  • Cellular_Morph_Context_DictionarySize=256.txt provides a datamatrix of 256-bin histograms at the patient level, where each row represents a patient and each column represents a dictionary (histogram bin).
  • Cellular_Morph_Context_DictionarySize=512.txt provides a datamatrix of 512-bin histograms at the patient level, where each row represents a patient and each column represents a dictionary (histogram bin).
  • Cellular_Morph_Context_DictionarySize=1024.txt provides a datamatrix of 1024-bin histograms at the patient level, where each row represents a patient and each column represents a dictionary (histogram bin).
  • Cellular_Morph_Context_DictionarySize=2048.txt provides a datamatrix of 2048-bin histograms at the patient level, where each row represents a patient and each column represents a dictionary (histogram bin).

The total number of morphometric features involved in this summarization is 15. These featurs are derived using the methdology described in

  • Hang Chang, Ju Han, Alexander Borowsky, Leandro Loss, Jow W. Gray, Paul T. Spellman and Bahram Parvin. "Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association." IEEE Trans. on Medical Imaging, 32 4 (2013): 670-682.

and are summarized as follows:

Feature Description
Nuclear Size #pixels of a segmented nucleus
Nuclear Voronoi Size #pixels of the voronoi region, where the segmented nucleus resides
Aspect Ratio Aspect ratio of the segmented nucleus
Major Axis Length of Major axis of the segmented nucleus
Minor Axis Length of Minor axis of the segmented nucleus
Rotation Angle between major axis and x axis of the segmented nucleus
Bending Energy Mean squared curvature values along nuclear contour
STD Curvature Standard deviation of absolute curvature values along nuclear contour
Abs Max Curvature Maximum absolute curvature values along nuclear contour
Mean Nuclear Intensity Mean intensity in nuclear region measured in gray scale
STD Nuclear Intensity Standard deviation of intensity in nuclear region measured in gray scale
Mean Background Intensity Mean intensity of nuclear background measured in gray scale
STD Background Intensity Standard deviation of intensity of nuclear background measured in gray scale
Mean Nuclear Gradient Mean gradient within nuclear region measured in gray scale
STD Nuclear Gradient Standard deviation of gradient within nuclear region measured in gray scale

The construction of cellular morphometric context representation is devired using the methodology described in

  • Hang Chang, Alexander Borowsky, Paul T. Spellman and Bahram Parvin, "Classification of Tumor Histology via Morphometric Context," Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, Portland, OR, 2013, pp. 2203-2210.

Related Resources:

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Project category: 
Data