Integration of architectural and cytologic driven image algorithms for prostate adenocarcinoma identification.

INTRODUCTION: The advent of digital slides offers new opportunities within the practice of pathology such as the use of image analysis techniques to facilitate computer aided diagnosis (CAD) solutions. Use of CAD holds promise to enable new levels of decision support and allow for additional layers of quality assurance and consistency in rendered diagnoses. However, the development and testing of prostate cancer CAD solutions requires a ground truth map of the cancer to enable the generation of receiver operator characteristic (ROC) curves. This requires a pathologist to annotate, or paint, each of the malignant glands in prostate cancer with an image editor software - a time consuming and exhaustive process. Recently, two CAD algorithms have been described: probabilistic pairwise Markov models (PPMM) and spatially-invariant vector quantization (SIVQ). Briefly, SIVQ operates as a highly sensitive and specific pattern matching algorithm, making it optimal for the identification of any epithelial morphology, whereas PPMM operates as a highly sensitive detector of malignant perturbations in glandular lumenal architecture. METHODS: By recapitulating algorithmically how a pathologist reviews prostate tissue sections, we created an algorithmic cascade of PPMM and SIVQ algorithms as previously described by Doyle el al. [1] where PPMM identifies the glands with abnormal lumenal architecture, and this area is then screened by SIVQ to identify the epithelium. RESULTS: The performance of this algorithm cascade was assessed qualitatively (with the use of heatmaps) and quantitatively (with the use of ROC curves) and demonstrates greater performance in the identification of malignant prostatic epithelium. CONCLUSION: This ability to semi-autonomously paint nearly all the malignant epithelium of prostate cancer has immediate applications to future prostate cancer CAD development as a validated ground truth generator. In addition, such an approach has potential applications as a pre-screening/quality assurance tool.

Assessment of normalization strategies for quantitative RT-PCR using microdissected tissue samples.

Gene expression measurement techniques such as quantitative reverse transcriptase (qRT)-PCR require a normalization strategy to allow meaningful comparisons across biological samples. Typically, this is accomplished through the use of an endogenous housekeeping gene that is presumed to show stable expression levels in the samples under study. There is concern regarding how precisely specific genes can be measured in limited amounts of mRNA such as those from microdissected (MD) tissues. To address this issue, we evaluated three different approaches for qRT-PCR normalization of dissected samples; cell count during microdissection, total RNA measurement, and endogenous control genes. The data indicate that both cell count and total RNA are useful in calibrating input amounts at the outset of a study, but do not provide enough precision to serve as normalization standards. However, endogenous control genes can accurately determine the relative abundance of a target gene relative to the entire cellular transcriptome. Taken together, these results suggest that precise gene expression measurements can be made from MD samples if the appropriate normalization strategy is employed.

Multimembrane dot-blotting: a cost-effective tool for proteome analysis.

The molecular profiles of protein expression from hundreds of cell lysates can be determined in a high-throughput manner by using fluorescent bead technologies, enzyme-linked immunosorbent assays (ELISAs), and protein microarrays. Although powerful, these tools are costly and technically challenging and thus have limited accessibility for many research groups. We propose a modification of traditional dot blotting that increases throughput of this approach and provides a simple and cost-effective technique for profiling multiple samples. In contrast to traditional blotting that uses a single membrane, we introduce blotting onto a stack of novel, thin, sieve-like membranes. These membranes have a high affinity for binding proteins, but have a lower capacity of protein binding compared to traditional (nitrocellulose) membranes. We compare the linear binding capacity and variability of these novel membranes with nitrocellulose membranes. Also, we describe the use of these membranes in a multilayer dot blot format for profiling mitogen-mediated signal transduction pathways in T cells.