Tumour Parcellation and Quantification (TuPaQ): a tool for refining biomarker analysis through rapid and automated segmentation of tumour epithelium

Abstract

Background and aims

Immunohistochemistry (IHC) is an essential component of biomarker research in cancer. Automated biomarker quantification is hampered by the failure of computational algorithms to discriminate "negative" tumour cells from "negative" stromal cells. We sought to develop an algorithm for segmentation of tumour epithelium in colorectal cancer (CRC) irrespective of the biomarker expression in the cells.

Methods

We developed TuPaQ (Tumour Parcellation and Quantification) to segment tumour epithelium and parcellate sections into "epithelium" and "non‐epithelium". TuPaQ comprises image pre‐processing, extraction of region of interests (ROIs), quantification of tumour epithelium (total area occupied by epithelium and number of nuclei in the occupied area). A total of 286 TMA cores from CRC were manually annotated and analysed using the commercial HALO software to provide ground truth. The performance of TuPaQ was evaluated against the ground truth using a variety of metrics.

Results

The image size of each core was 7000 X 7000 pixels and each core was analysed in a matter of seconds. Pixel by pixel analysis showed a sensitivity of 84% and specificity of 95% in detecting epithelium. The mean tumour area obtained by TuPaQ was very close to the area quantified after manual annotation (r = 0.956, p < 0.001). Moreover, quantification of tumour nuclei by TuPaQ correlated very strongly with that of HALO (r = 0.891, (p < 0.001)).

Conclusion

TuPaQ is a very rapid and accurate method of separating the epithelial and stromal compartments of colorectal tumours. This will allow more accurate and objective analysis of immunohistochemistry.

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