Astronomical algorithms for automated analysis of tissue protein expression in breast cancer.
Astronomical algorithms for automated analysis of tissue protein expression in breast cancer.
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DOI：
10.1038/bjc.2012.558
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发表时间：
2013-02-19
影响因子：
8.8
通讯作者：
Caldas, C.
中科院分区：
文献类型：
--
作者：
Ali, H. R.;Irwin, M.;Morris, L.;Dawson, S-J;Blows, F. M.;Provenzano, E.;Mahler-Araujo, B.;Pharoah, P. D.;Walton, N. A.;Brenton, J. D.;Caldas, C.
组织微阵列（tma）和免疫组织化学的广泛应用极大地促进了肿瘤组织生物标志物的高通量评估。尽管tma有潜力在一定规模上促进蛋白质表达谱分析，以与肿瘤转录组实验相媲美，但人工对tma进行评分的瓶颈和不精确阻碍了进展。我们报告了适用于天文学的图像分析算法，用于所有亚细胞区室中IHC的精确自动分析。这项技术的力量通过使用超过2000个乳腺肿瘤，并将定量自动评分与病理学家的手动评估进行比较来证明。所有连续自动得分与其对应的顺序人工得分均表现出良好的相关性。雌激素受体（ER）的相关系数为0.82，P<0.0001； BCL2的相关系数为0.72，P<0.0001； HER2的相关系数为0.62，P<0.0001。在无监督的情况下，自动评分与人工评分的“积极”或“消极”类别具有良好的一致性，一致性率高达96%。天文算法的适应加上它们在大型注释研究队列中的应用，构成了实现数字病理学巨大潜力的强大工具。
High-throughput evaluation of tissue biomarkers in oncology has been greatly accelerated by the widespread use of tissue microarrays (TMAs) and immunohistochemistry. Although TMAs have the potential to facilitate protein expression profiling on a scale to rival experiments of tumour transcriptomes, the bottleneck and imprecision of manually scoring TMAs has impeded progress. We report image analysis algorithms adapted from astronomy for the precise automated analysis of IHC in all subcellular compartments. The power of this technique is demonstrated using over 2000 breast tumours and comparing quantitative automated scores against manual assessment by pathologists. All continuous automated scores showed good correlation with their corresponding ordinal manual scores. For oestrogen receptor (ER), the correlation was 0.82, P<0.0001, for BCL2 0.72, P<0.0001 and for HER2 0.62, P<0.0001. Automated scores showed excellent concordance with manual scores for the unsupervised assignment of cases to ‘positive' or ‘negative' categories with agreement rates of up to 96%. The adaptation of astronomical algorithms coupled with their application to large annotated study cohorts, constitutes a powerful tool for the realisation of the enormous potential of digital pathology.
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影响因子：
3.8
作者：
Brugmann, Anja;Eld, Mikkel;Vyberg, Mogens
通讯作者：
Vyberg, Mogens
影响因子：
6.4
作者：
Faratian, Dana;Kay, Charlene;Bartlett, John M. S.
通讯作者：
Bartlett, John M. S.
影响因子：
82.9
作者：
Kononen, J;Bubendorf, L;Kallioniemi, OP
通讯作者：
Kallioniemi, OP
DOI：
10.1098/rsta.2010.0158
发表时间：
2010-08-28
影响因子：
5
作者：
Walton, N. A.;Brenton, J. D.;Rixon, G. T.
通讯作者：
Rixon, G. T.
影响因子：
3.8
作者：
Turbin, Dmitry A.;Leung, Samuel;Huntsman, David G.
通讯作者：
Huntsman, David G.