Computer-aided detection of prostate cancer on tissue sections.
Computer-aided detection of prostate cancer on tissue sections.
复制标题
组织切片上前列腺癌的计算机辅助检测。
DOI：
10.1097/pai.0b013e31819e6d65
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Yang,XimingJ
中科院分区：
文献类型：
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作者：
Peng,Yahui;Jiang,Yulei;Chuang,Shang-Tian;Yang,XimingJ
我们报告了一种自动化的计算机技术检测前列腺癌的前列腺组织切片处理与免疫组化。从前列腺组织切片中获取两组彩色光学图像，所述前列腺组织切片用结合α-甲酰基-CoA消旋酶、p63和高分子量细胞角蛋白的双色原三抗体混合物染色。第一组图像包括用于开发计算机技术的20个训练图像（10个恶性）和用于测试和优化该技术的15个测试图像（7个恶性）。第二组图像包括299张图像（114张恶性），用于评价计算机技术的性能。计算机技术识别了α-甲基酰基-CoA消旋酶标记的恶性上皮细胞（红色）、p63和高分子量细胞角蛋白标记的良性基底细胞（棕色）以及分泌和基质细胞（蓝色）的图像片段，用于自动识别前列腺癌。计算机技术的灵敏度和特异性分别为94%（16/17）和94%（17/18），在第一组（训练和测试）图像，88%（79/90）和97%（136/140），分别在第二组（验证）图像。如果包括前列腺上皮内瘤变（前列腺癌的前兆）和不典型病例，则敏感性和特异性分别为85%（97/114）和89%（165/185）。这些结果表明，新的自动化计算机技术可以准确地识别前列腺腺癌的三抗体鸡尾酒染色的前列腺切片。
We report an automated computer technique for detection of prostate cancer in prostate tissue sections processed with immunohistochemistry. Two sets of color optical images were acquired from prostate tissue sections stained with a double-chromogen triple-antibody cocktail combining alpha-methylacyl-CoA racemase, p63, and high-molecular–weight cytokeratin. The first set of images consisted of 20 training images (10 malignant) used for developing the computer technique and 15 test images (7 malignant) used for testing and optimizing the technique. The second set of images consisted of 299 images (114 malignant) used for evaluation of the performance of the computer technique. The computer technique identified image segments of alpha-methylacyl-CoA racemase-labeled malignant epithelial cells (red), p63, and high-molecular–weight cytokeratin-labeled benign basal cells (brown), and secretory and stromal cells (blue) for identifying prostate cancer automatically. The sensitivity and specificity of the computer technique were 94%(16/17) and 94%(17/18), respectively, on the first (training and test) set of images, and 88%(79/90) and 97%(136/140), respectively, on the second (validation) set of images. If high-grade prostatic intraepithelial neoplasia, which is a precursor of cancer, and atypical cases were included, the sensitivity and specificity were 85%(97/114) and 89%(165/185), respectively. These results show that the novel automated computer technique can accurately identify prostatic adenocarcinoma in the triple-antibody cocktail-stained prostate sections.