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Open Access Highly Accessed Research article

Multimodal microscopy for automated histologic analysis of prostate cancer

Jin Tae Kwak12, Stephen M Hewitt3, Saurabh Sinha1* and Rohit Bhargava24*

Author Affiliations

1 Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

2 Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

3 Tissue array research program, National Cancer Institute, National Institutes of Health, Bethesda, MD 20850, USA

4 Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

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BMC Cancer 2011, 11:62  doi:10.1186/1471-2407-11-62

Published: 9 February 2011

Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.