Quantification of large scale DNA organization for predicting prostate cancer recurrence

Abstract

This study investigates whether Genomic Organization at Large Scales (which we propose to call GOALS) as quantified via nuclear phenotype characteristics and cell sociology features (describing cell organization within tissue) collected from prostate tissue microarrays (TMAs) can separate biochemical failure from biochemical nonevidence of disease (BNED) after radical prostatectomy (RP). Of the 78 prostate cancer tissue cores collected from patients treated with RP, 16 who developed biochemical relapse (failure group) and 16 who were BNED patients (nonfailure group) were included in the analyses (36 cores from 32 patients). A section from this TMA was stained stoichiometrically for DNA using the Feulgen-Thionin methodology, and scanned with a Pannoramic MIDI scanner. Approximately 110 nuclear phenotypic features, predominately quantifying large scale DNA organization (GOALS), were extracted from each segmented nuclei. In addition, the centers of these segmented nuclei defined a Voronoi tessellation and subsequent architectural analysis. Prostate TMA core classification as biochemical failure or BNED after RP using GOALS features was conducted (a) based on cell type and cell position within the epithelium (all cells, all epithelial cells, epithelial >2 cell layers away from basement membrane) from all cores, and (b) based on epithelial cells more than two cell layers from the basement membrane using a Classifier trained on Gleason 6, 8, 9 (16 cores) only and applied to a Test set consisting of the Gleason 7 cores (20 cores). Successful core classification as biochemical failure or BNED after RP by a linear classifier was 75% using all cells, 83% using all epithelial cells, and 86% using epithelial >2 layers. Overall success of predicted classification by the linear Classifier of (b) was 87.5% using the Training Set and 80% using the Test Set. Overall success of predicted progression using Gleason score alone was 75% for Gleason >7 as failures and 69% for Gleason >6 as failures. © 2017 International Society for Advancement of Cytometry.

The topology of vitronectin: A complementary feature for neuroblastoma risk classification based on computer‐aided detection

“…Thus, interpret and relate features with biology are essential to extract new insights. Likewise, most of the works using computerized image analysis rely on recognizable objects to capture the morphology or topology, such as the nuclei of the cells within the sample . In our case, VN cannot be captured as an object due to its variability in size and shape (Figs.…”

“…However, more sophisticated techniques are needed to model highly complicated diseases like cancer. In particular, computerized image analysis has proven useful to find relevant features in different types of cancer . For instance, features related to texture analysis, which is based on the intensity and colors of the images, or morphological features considering the shape of the detected elements, are broadly used.…”

“…For instance, features related to texture analysis, which is based on the intensity and colors of the images, or morphological features considering the shape of the detected elements, are broadly used. These approaches did not consider the spatial relationship between its components or were restricted to nuclei . However, morphometric techniques assure the standardization of all measurements and minimize interobserver differences .…”

“…In particular, computerized image analysis has proven useful to find relevant features in different types of cancer. [12][13][14] For instance, features related to texture analysis, which is based on the intensity and colors of the images, or morphological features considering the shape of the detected elements, are broadly used. These approaches did not consider the spatial relationship between its components 12,13 or were restricted to nuclei.…”

“…These approaches did not consider the spatial relationship between its components 12,13 or were restricted to nuclei. 14 However, morphometric techniques assure the standardization of all measurements and minimize interobserver differences. 15,16 Understanding how biopsy elements are organized is important to find potential new markers for improving treatment strategies and outcome prediction.…”