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While tissue microarrays (TMAs) are a form of high-throughput screening, they presently still require manual construction and interpretation. Because of predicted increasing demand for TMAs, we investigated whether their construction could be automated. We created both epithelial recognition algorithms (ERAs) and field of view (FOV) algorithms that could analyze virtual slides and select the areas of highest cancer cell density in the tissue block for coring (algorithmic TMA) and compared these to the cores manually selected (manual TMA) from the same tissue blocks. We also constructed TMAs with TMAker, a robot guided by these algorithms (robotic TMA). We compared each of these TMAs to each other. Our imaging algorithms produced a grid of hundreds of FOVs, identified cancer cells in a stroma background and calculated the epithelial percentage (cancer cell density) in each FOV. Those with the highest percentages guided core selection and TMA construction. Algorithmic TMA and robotic TMA were overall approximately 50% greater in cancer cell density compared with Manual TMA. These observations held for breast, colon, and lung cancer TMAs. Our digital image algorithms were effective in automating TMA construction.

Use and validation of epithelial recognition and fields of view algorithms on virtual slides to guide TMA construction