  Recent advances in tissue microarray technology have allowed
immunohistochemistry to become a powerful medium-to-high throughput analysis
tool, particularly for the validation of diagnostic and prognostic biomarkers.
However, as study size grows, the manual evaluation of these assays becomes a
prohibitive limitation; it vastly reduces throughput and greatly increases
variability and expense. We propose an algorithm - Tissue Array Co-Occurrence
Matrix Analysis (TACOMA) - for quantifying cellular phenotypes based on
textural regularity summarized by local inter-pixel relationships. The
algorithm can be easily trained for any staining pattern, is absent of
sensitive tuning parameters and has the ability to report salient pixels in an
image that contribute to its score. Pathologists' input via informative
training patches is an important aspect of the algorithm that allows the
training for any specific marker or cell type. With co-training, the error rate
of TACOMA can be reduced substantially for a very small training sample (e.g.,
with size 30). We give theoretical insights into the success of co-training via
thinning of the feature set in a high-dimensional setting when there is
"sufficient" redundancy among the features. TACOMA is flexible, transparent and
provides a scoring process that can be evaluated with clarity and confidence.
In a study based on an estrogen receptor (ER) marker, we show that TACOMA is
comparable to, or outperforms, pathologists' performance in terms of accuracy
and repeatability.
