Abstract
BackgroundProstate 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.MethodsWe 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.ResultsWe 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.ConclusionsWe 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.
Highlights
Prostate cancer is the single most prevalent cancer in US men whose gold standard of diagnosis is histologic assessment of biopsies
A key aspect of our approach is the use of Fourier transform infrared (FT-IR) imaging data on a serial section that is hematoxylin and eosin (H&E)-stained to enhance the segmentation of nuclei and lumens
The IR image specifies the exact areas corresponding to each cell type, but the difficulty in precisely extracting such regions from the H&E image hinders us from using celltype information for registration
Summary
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. Manually-conducted histologic assessment of tissue upon biopsy forms the definitive diagnosis of PCa [5]. This need places a large demand on pathology services and manual examination limits speed and throughput. Histologic assessment is critical to scientific progress as it Optical microscopy and automated PCa detection Since the tissue does not have appreciable contrast in optical brightfield microscopy (Figure 1A), tissue samples are commonly stained using hematoxylin and eosin (H&E) prior to review by a pathologist. The greater the distortion and loss of regular structure, the worse (higher grade) the cancer
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