Barsky Sanford, Gentchev Lynda, Basu Amitabha, Jimenez Rafael, Boussaid Kamel, Gholap Abhi
Department of Pathology, University of Nevada School of Medicine, University of Nevada, Reno, NV 89557, USA.
Biotechniques. 2009 Nov;47(5):927-38. doi: 10.2144/000113207.
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.
虽然组织微阵列（TMA）是一种高通量筛选形式，但目前它们仍需要人工构建和解读。由于预计对TMA的需求会不断增加，我们研究了其构建是否可以自动化。我们创建了上皮识别算法（ERA）和视野（FOV）算法，这些算法可以分析虚拟切片并在组织块中选择癌细胞密度最高的区域进行取芯（算法TMA），并将其与从相同组织块中手动选择的芯（手动TMA）进行比较。我们还使用由这些算法引导的机器人TMAker构建了TMA（机器人TMA）。我们将这些TMA相互进行了比较。我们的成像算法生成了数百个视野的网格，在基质背景中识别癌细胞，并计算每个视野中的上皮百分比（癌细胞密度）。那些百分比最高的区域指导芯的选择和TMA的构建。与手动TMA相比，算法TMA和机器人TMA的癌细胞密度总体上大约高50%。这些观察结果适用于乳腺癌、结肠癌和肺癌TMA。我们的数字图像算法在TMA构建自动化方面是有效的。
