A four class model for digital breast histopathology using high-definition Fourier transform infrared (FT-IR) spectroscopic imaging
A four class model for digital breast histopathology using high-definition Fourier transform infrared (FT-IR) spectroscopic imaging
复制标题
使用高清傅立叶变换红外 (FT-IR) 光谱成像的数字乳腺组织病理学四类模型
DOI：
10.1117/12.2217358
复制
发表时间：
2016
期刊：
影响因子：
3.8
通讯作者：
R. Bhargava
中科院分区：
文献类型：
--
作者：
Shachi Mittal;T. Wróbel;L. S. Leslie;Andre Kadjacsy;R. Bhargava
高清晰度（HD）傅里叶变换红外（FT-IR）光谱成像是一种新兴技术，其不仅能够实现组织成分的基于化学的可视化，并且能够无标记地提取生化信息，而且其更高的空间细节使其成为进行数字病理学的潜在有用平台。这种方法，沿着快速和有效的数据分析，可以实现定量和自动病理学。在这里，我们展示了乳腺组织微阵列（TMAs）的高清FT-IR光谱成像与数据分析算法相结合，以进行组织学分析。样品包括四种组织状态，即增生、发育异常、癌性和正常。我们确定了各种细胞类型，这些细胞类型将作为乳腺癌检测的生物标志物，并使用统计模式识别工具，即随机森林（RF）和贝叶斯算法来区分它们。对RF算法进行了整体的特征优化，减少了计算时间和冗余的谱特征。我们实现了一个数量级减少的功能，具有可比的预测精度的原始特征集的数量。总之，组织学的演示和特征的选择为未来在更复杂的模型和快速数据采集中的应用铺平了道路。
High-definition (HD) Fourier transform infrared (FT-IR) spectroscopic imaging is an emerging technique that not only enables chemistry-based visualization of tissue constituents, and label free extraction of biochemical information but its higher spatial detail makes it a potentially useful platform to conduct digital pathology. This methodology, along with fast and efficient data analysis, can enable both quantitative and automated pathology. Here we demonstrate a combination of HD FT-IR spectroscopic imaging of breast tissue microarrays (TMAs) with data analysis algorithms to perform histologic analysis. The samples comprise four tissue states, namely hyperplasia, dysplasia, cancerous and normal. We identify various cell types which would act as biomarkers for breast cancer detection and differentiate between them using statistical pattern recognition tools i.e. Random Forest (RF) and Bayesian algorithms. Feature optimization is integrally carried out for the RF algorithm, reducing computation time as well as redundant spectral features. We achieved an order of magnitude reduction in the number of features with comparable prediction accuracy to that of the original feature set. Together, the demonstration of histology and selection of features paves the way for future applications in more complex models and rapid data acquisition.
影响因子：
7.4
作者：
Bassan, Paul;Mellor, Joe;Gardner, Peter
通讯作者：
Gardner, Peter