TCGA LGG Cohort Cellular Morphometric Univariate 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 univariate cellular morphoetric summarization for TCGA lower grade glioma (LGG) 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:  LGG 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.
  • patient_equal_bin_widths_histograms.txt provides a datamatrix of equal bin widths histograms at the patient level, where each column represents a patient.
  • patient_equal_probability_histograms.txt provides a datamatrix of equal probability histograms at the patient level, where each column represents a patient.
  • patient_statistics.txt provides basis statistics per feature per patient.

The total number of morphometric features, involved in this summarization is 53, which 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 type Feature name Annotation
Cellular organization cell_voronoi_area measured at the log10 scale
Cellular organization cellularity cell density per unit area, inverse of cell_voronoi_area, at the log10 scale
Cellular organization edge_length  
Cytoplasm feature background_intensity_mean  
Cytoplasm feature background_intensity_sd  
Cytoplasm feature contrast_mean  
Cytoplasm feature gradient_mean  
Cytoplasm feature gradient_sd  
Cytoplasm feature intensity_mean  
Cytoplasm feature intensity_sd  
Cytoplasm feature intensity_total  
Nuclear feature area  
Nuclear feature aspect ratio  
Nuclear feature background_intensity_mean  
Nuclear feature background_intensity_sd  
Nuclear feature bending_energy_s1_mean  
Nuclear feature contrast_mean  
Nuclear feature curvature_s1_sd  
Nuclear feature deviation_from_polygon_convexity  
Nuclear feature gradient_mean  
Nuclear feature gradient_sd  
Nuclear feature intensity_mean  
Nuclear feature intensity_sd  
Nuclear feature intensity_total  
Nuclear feature major_axis  
Nuclear feature max_curvature_s1  
Nuclear feature minor_axis  
Nuclear feature orientation  
Nuclear feature perimeter  
Nuclear feature texture_feature_0_mean 1st order steerable filter at angle 0
Nuclear feature texture_feature_0_sd 1st order steerable filter at angle 0
Nuclear feature texture_feature_1_mean 2nd order steerable filter at angle 0
Nuclear feature texture_feature_1_sd 2nd order steerable filter at angle 0
Nuclear feature texture_feature_2_mean 3rd order steerable filter at angle 0
Nuclear feature texture_feature_2_sd 3rd order steerable filter at angle 0
Nuclear feature texture_feature_3_mean 1st order steerable filter at angle 0.78
Nuclear feature texture_feature_3_sd 1st order steerable filter at angle 0.78
Nuclear feature texture_feature_4_mean 2nd order steerable filter at angle 0.78
Nuclear feature  texture_feature_4_sd 2nd order steerable filter at angle 0.78
Nuclear feature texture_feature_5_mean 3rd order steerable filter at angle 0.78
Nuclear feature texture_feature_5_sd 3rd order steerable filter at angle 0.78
Nuclear feature texture_feature_6_mean 1st order steerable filter at angle 1.56
Nuclear feature texture_feature_6_sd 1st order steerable filter at angle 1.56
Nuclear feature texture_feature_7_mean 2nd order steerable filter at angle 1.56
Nuclear feature texture_feature_7_sd 2nd order steerable filter at angle 1.56
Nuclear feature texture_feature_8_mean 3rd order steerable filter at angle 1.56
Nuclear feature texture_feature_8_sd 3rd order steerable filter at angle 1.56
Nuclear feature texture_feature_9_mean 1st order steerable filter at angle 2.34
Nuclear feature texture_feature_9_sd 1st order steerable filter at angle 2.34
Nuclear feature texture_feature_10_mean 2nd order steerable filter at angle 2.34
Nuclear feature texture_feature_10_sd 2nd order steerable filter at angle 2.34
Nuclear feature texture_feature_11_mean 3rd order steerable filter at angle 2.34
Nuclear feature texture_feature_11_sd 3rd order steerable filter at angle 2.34

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Data