Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation.

Neuroblastoma (NB) is one of the most frequently occurring cancerous tumors in children. The current grading evaluations for patients with this disease require pathologists to identify certain morphological characteristics with microscopic examinations of tumor tissues. Thanks to the advent of modern digital scanners, it is now feasible to scan cross-section tissue specimens and acquire whole-slide digital images. As a result, computerized analysis of these images can generate key quantifiable parameters and assist pathologists with grading evaluations. In this study, image analysis techniques are applied to histological images of haematoxylin and eosin (H&E) stained slides for identifying image regions associated with different pathological components. Texture features derived from segmented components of tissues are extracted and processed by an automated classifier group trained with sample images with different grades of neuroblastic differentiation in a multi-resolution framework. The trained classification system is tested on 33 whole-slide tumor images. The resulting whole-slide classification accuracy produced by the computerized system is 87.88%. Therefore, the developed system is a promising tool to facilitate grading whole-slide images of NB biopsies with high throughput.

Computer-aided Prognosis of Neuroblastoma on Whole-slide Images: Classification of Stromal Development.

We are developing a computer-aided prognosis system for neuroblastoma (NB), a cancer of the nervous system and one of the most malignant tumors affecting children. Histopathological examination is an important stage for further treatment planning in routine clinical diagnosis of NB. According to the International Neuroblastoma Pathology Classification (the Shimada system), NB patients are classified into favorable and unfavorable histology based on the tissue morphology. In this study, we propose an image analysis system that operates on digitized H&E stained whole-slide NB tissue samples and classifies each slide as either stroma-rich or stroma-poor based on the degree of Schwannian stromal development. Our statistical framework performs the classification based on texture features extracted using co-occurrence statistics and local binary patterns. Due to the high resolution of digitized whole-slide images, we propose a multi-resolution approach that mimics the evaluation of a pathologist such that the image analysis starts from the lowest resolution and switches to higher resolutions when necessary. We employ an offine feature selection step, which determines the most discriminative features at each resolution level during the training step. A modified k-nearest neighbor classifier is used to determine the confidence level of the classification to make the decision at a particular resolution level. The proposed approach was independently tested on 43 whole-slide samples and provided an overall classification accuracy of 88.4%.

An Assessment of Imaging Informatics for Precision Medicine in Cancer.

Objectives: Precision medicine requires the measurement, quantification, and cataloging of medical characteristics to identify the most effective medical intervention. However, the amount of available data exceeds our current capacity to extract meaningful information. We examine the informatics needs to achieve precision medicine from the perspective of quantitative imaging and oncology. Methods: The National Cancer Institute (NCI) organized several workshops on the topic of medical imaging and precision medicine. The observations and recommendations are summarized herein. Results: Recommendations include: use of standards in data collection and clinical correlates to promote interoperability; data sharing and validation of imaging tools; clinician's feedback in all phases of research and development; use of open-source architecture to encourage reproducibility and reusability; use of challenges which simulate real-world situations to incentivize innovation; partnership with industry to facilitate commercialization; and education in academic communities regarding the challenges involved with translation of technology from the research domain to clinical utility and the benefits of doing so. Conclusions: This article provides a survey of the role and priorities for imaging informatics to help advance quantitative imaging in the era of precision medicine. While these recommendations were drawn from oncology, they are relevant and applicable to other clinical domains where imaging aids precision medicine.

Classification of small cell lung cancer and pulmonary carcinoid by gene expression profiles.

Small cell lung cancer is a common type of lung cancer that is generally classified within the spectrum of neuroendocrine lung neoplasms. Using high-density cDNA arrays, we profiled gene expression of small cell lung cancers and compared these expression profiles to those of normal bronchial epithelial cells and pulmonary carcinoids, which are classified as benign neuroendocrine tumors. We found the overall expression profiles of two small cell lung cancer cell lines, two microdissected tissue samples of primary small cell lung cancer, and cultured bronchial epithelial cells to be relatively similar to one another, with an average Pearson correlation coefficient for these comparisons of 0.63. However, we found the expression profiles of small cell lung cancers (and bronchial epithelial cells) to be surprisingly dissimilar to those of two samples of pulmonary carcinoid tumors, with an average correlation coefficient for these comparisons of 0.20. We then compared the pulmonary carcinoid expression profiles to those of two samples of infiltrating astrocytic brain cancers (oligodendroglioma and high-grade astrocytoma) and found similarity of gene expression among these four samples (average correlation coefficient, 0.57). These gene expression profiles suggest that small cell lung cancers are closely related to (and possibly derived from) epithelial cells, and that pulmonary carcinoids are related to neural crest-derived brain tumors. More generally, our results suggest that broad profiles of gene expression may reveal similarities and differences between tumors that are not apparent by traditional morphological criteria.

A proprotein convertase/MMP-14 proteolytic cascade releases a novel 40 kDa vasculostatin from tumor suppressor BAI1.

Brain-specific angiogenesis inhibitor 1 (BAI1), an orphan G protein-coupled receptor-type seven transmembrane protein, was recently found mutated or silenced in multiple human cancers and can interfere with tumor growth when overexpressed. Yet, little is known about its regulation and the molecular mechanisms through which this novel tumor suppressor exerts its anti-cancer effects. Here, we demonstrate that the N terminus of BAI1 is cleaved extracellularly to generate a truncated receptor and a 40-kDa fragment (Vasculostatin-40) that inhibits angiogenesis. We demonstrate that this novel proteolytic processing event depends on a two-step cascade of protease activation: proprotein convertases, primarily furin, activate latent matrix metalloproteinase-14, which then directly cleaves BAI1 to release the bioactive fragment. These findings significantly augment our knowledge of BAI1 by showing a novel post-translational mechanism regulating BAI1 activity through cancer-associated proteases, have important implications for BAI1 function and regulation, and present novel opportunities for therapy of cancer and other vascular diseases.

Supercomputers and biological sequence comparison algorithms.

Comparison of biological (DNA or protein) sequences provides insight into molecular structure, function, and homology and is increasingly important as the available databases become larger and more numerous. One method of increasing the speed of the calculations is to perform them in parallel. We present the results of initial investigations using two dynamic programming algorithms on the Intel iPSC hypercube and the Connection Machine as well as an inexpensive, heuristically-based algorithm on the Encore Multimax.