Available Technology

Classification of Tumor Composition and Delineation of Cellular Regions via Dictionary and Sparse Coding IB-2013-173

Berkeley Lab researchers Bahram Parvin, Hang Chang, and Yin Zhou have developed software enabling pathologists to quantify tumor composition via cell-by-cell profiling. The software assists with pre-identification of aberrant regions in tissue, saving pathologists’ time. It can assess frequency and organization of cellular processes such as mitosis and quantify tissue composition for assessing tumor-stroma interactions. The technology enables precision medicine by sifting through large amounts of data and will lead to improved predictive models. This is the first attempt to use spatial pyramid matching (SPM) to classify tumor histopathology using engineered or learned features. Using a learned dictionary, excellent performance has been achieved even with a small number of training samples across independent data sets of brain and kidney tumors. The Berkeley Lab technology is extensible to other tumor types. Existing techniques for image-based classification from a large cohort are hindered due to large technical and biological variation. The Berkeley team’s algorithms to classify tissue histology — based on strong representations of morphometric context and built on nuclear-level morphometric features at various locations and scales within the SPM framework — have been found to be extensible to different tumor types; robust despite wide technological and biological variation; invariant to different nuclear segmentation strategies; and effective across multiple tumor types with a limited number of training samples.
Benefits: 
Impervious to biological and technical batch variation - Quantifies tumor composition via cell-by-cell profiling - Measures cellular processes such as mitosis - Extensible to a variety of other tumor types
applications: 
Internal Laboratory Ref #: 
2013-173
Patent Status: 
Patent pending. Available for license or collaborative research.
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