Multimodal microscopy for automated histologic analysis of prostate cancer.
Multimodal microscopy for automated histologic analysis of prostate cancer.
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DOI：
10.1186/1471-2407-11-62
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发表时间：
2011-02-09
期刊：
影响因子：
3.8
通讯作者：
Bhargava R
中科院分区：
文献类型：
--
作者：
Kwak JT;Hewitt SM;Sinha S;Bhargava R
前列腺癌是美国男性中最常见的癌症，其诊断的金标准是活检的组织学评估。对所有活检染色组织的手动评估限制了前列腺癌诊断的临床实践和研究的速度和准确性。我们试图开发一种全自动多模式显微镜方法来区分癌性组织样本和非癌性组织样本。我们使用傅里叶变换红外 (FT-IR) 光谱成像记录了未染色的组织微阵列 (TMA) 的化学数据。使用模式识别，我们无需用户输入即可识别上皮细胞。我们将细胞类型信息与临床实践中常用的相应染色图像融合。提取的形态特征，通过使用最小冗余最大相关性（mRMR）标准和顺序浮动前向选择（SFFS）的两阶段特征选择方法进行优化，用于将组织样本分类为癌症或非癌症。我们在不同条件下染色的两个数据集的交叉验证中实现了高精度（ROC 曲线下面积 (AUC) >0.97）。当分类器在一个数据集上进行训练并在另一数据集上进行测试时，观察到 AUC 值约为 0.95。在缺乏 IR 数据的情况下，同一分类系统的性能对于两个数据集以及数据集之间都会下降。我们能够非常有效地融合来自两个不同图像的信息，这两个图像提供了具有不同特征的非常不同类型的数据。该方法对用户完全透明，并且不涉及基于光谱数据的任何调整或决策。通过结合红外和光学数据，我们实现了高精度分类。
Prostate cancer is the single most prevalent cancer in US men whose gold standard of diagnosis is histologic assessment of biopsies. Manual assessment of stained tissue of all biopsies limits speed and accuracy in clinical practice and research of prostate cancer diagnosis. We sought to develop a fully-automated multimodal microscopy method to distinguish cancerous from non-cancerous tissue samples. We recorded chemical data from an unstained tissue microarray (TMA) using Fourier transform infrared (FT-IR) spectroscopic imaging. Using pattern recognition, we identified epithelial cells without user input. We fused the cell type information with the corresponding stained images commonly used in clinical practice. Extracted morphological features, optimized by two-stage feature selection method using a minimum-redundancy-maximal-relevance (mRMR) criterion and sequential floating forward selection (SFFS), were applied to classify tissue samples as cancer or non-cancer. We achieved high accuracy (area under ROC curve (AUC) >0.97) in cross-validations on each of two data sets that were stained under different conditions. When the classifier was trained on one data set and tested on the other data set, an AUC value of ~0.95 was observed. In the absence of IR data, the performance of the same classification system dropped for both data sets and between data sets. We were able to achieve very effective fusion of the information from two different images that provide very different types of data with different characteristics. The method is entirely transparent to a user and does not involve any adjustment or decision-making based on spectral data. By combining the IR and optical data, we achieved high accurate classification.
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