Quantitative metric profiles capture three-dimensional temporospatial architecture to discriminate cellular functional states

<p>Abstract</p> <p>Background</p> <p>Computational analysis of tissue structure reveals sub-visual differences in tissue functional states by extracting quantitative signature features that establish a diagnostic profile. Incomplete and/or inaccurate profiles contribute to misdiagnosis.</p> <p>Methods</p> <p>In order to create more complete tissue structure profiles, we adapted our cell-graph method for extracting quantitative features from histopathology images to now capture temporospatial traits of three-dimensional collagen hydrogel cell cultures. Cell-graphs were proposed to characterize the spatial organization between the cells in tissues by exploiting graph theory wherein the nuclei of the cells constitute the <it>nodes </it>and the approximate adjacency of cells are represented with <it>edges</it>. We chose 11 different cell types representing non-tumorigenic, pre-cancerous, and malignant states from multiple tissue origins.</p> <p>Results</p> <p>We built cell-graphs from the cellular hydrogel images and computed a large set of features describing the structural characteristics captured by the graphs over time. Using three-mode tensor analysis, we identified the five most significant features (metrics) that capture the compactness, clustering, and spatial uniformity of the 3D architectural changes for each cell type throughout the time course. Importantly, four of these metrics are also the discriminative features for our histopathology data from our previous studies.</p> <p>Conclusions</p> <p>Together, these descriptive metrics provide rigorous quantitative representations of image information that other image analysis methods do not. Examining the changes in these five metrics allowed us to easily discriminate between all 11 cell types, whereas differences from visual examination of the images are not as apparent. These results demonstrate that application of the cell-graph technique to 3D image data yields discriminative metrics that have the potential to improve the accuracy of image-based tissue profiles, and thus improve the detection and diagnosis of disease.</p

Extracellular matrix composition defines an ultra-high-risk group of neuroblastoma within the high-risk patient cohort

Background:Although survival for neuroblastoma patients has dramatically improved in recent years, a substantial number of children in the high-risk subgroup still die.Methods:We aimed to define a subgroup of ultra-high-risk patients from within the high-risk cohort. We used advanced morphometric approaches to quantify and characterise blood vessels, reticulin fibre networks, collagen type I bundles, elastic fibres and glycosaminoglycans in 102 high-risk neuroblastomas specimens. The Kaplan-Meier method was used to correlate the analysed elements with survival.Results:The organisation of blood vessels and reticulin fibres in neuroblastic tumours defined an ultra-high-risk patient subgroup with 5-year survival rate <15%. Specifically, tumours with irregularly shaped blood vessels, large sinusoid-like vessels, smaller and tortuous venules and arterioles and with large areas of reticulin fibres forming large, crosslinking, branching and haphazardly arranged networks were linked to the ultra-high-risk phenotype.Conclusions:We demonstrate that quantification of tumour stroma components by morphometric techniques has the potential to improve risk stratification of neuroblastoma patients