Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer

BACKGROUND: Tumor classification is inexact and largely dependent on the qualitative pathological examination of the images of the tumor tissue slides. In this study, our aim was to develop an automated computational method to classify Hematoxylin and Eosin (H&E) stained tissue sections based on cancer tissue texture features. METHODS: Image processing of histology slide images was used to detect and identify adipose tissue, extracellular matrix, morphologically distinct cell nuclei types, and the tubular architecture. The texture parameters derived from image analysis were then applied to classify images in a supervised classification scheme using histologic grade of a testing set as guidance. RESULTS: The histologic grade assigned by pathologists to invasive breast carcinoma images strongly correlated with both the presence and extent of cell nuclei with dispersed chromatin and the architecture, specifically the extent of presence of tubular cross sections. The two parameters that differentiated tumor grade found in this study were (1) the number density of cell nuclei with dispersed chromatin and (2) the number density of tubular cross sections identified through image processing as white blobs that were surrounded by a continuous string of cell nuclei. Classification based on subdivisions of a whole slide image containing a high concentration of cancer cell nuclei consistently agreed with the grade classification of the entire slide. CONCLUSION: The automated image analysis and classification presented in this study demonstrate the feasibility of developing clinically relevant classification of histology images based on micro- texture. This method provides pathologists an invaluable quantitative tool for evaluation of the components of the Nottingham system for breast tumor grading and avoid intra-observer variability thus increasing the consistency of the decision-making process

DNA index determination with Automated Cellular Imaging System (ACIS) in Barrett's esophagus: Comparison with CAS 200

BACKGROUND: For solid tumors, image cytometry has been shown to be more sensitive for diagnosing DNA content abnormalities (aneuploidy) than flow cytometry. Image cytometry has often been performed using the semi-automated CAS 200 system. Recently, an Automated Cellular Imaging System (ACIS) was introduced to determine DNA content (DNA index), but it has not been validated. METHODS: Using the CAS 200 system and ACIS, we compared the DNA index (DI) obtained from the same archived formalin-fixed and paraffin embedded tissue samples from Barrett's esophagus related lesions, including samples with specialized intestinal metaplasia without dysplasia, low-grade dysplasia, high-grade dysplasia and adenocarcinoma. RESULTS: Although there was a very good correlation between the DI values determined by ACIS and CAS 200, the former was 25% more sensitive in detecting aneuploidy. ACIS yielded a mean DI value 18% higher than that obtained by CAS 200 (p < 0.001; paired t test). In addition, the average time required to perform a DNA ploidy analysis was shorter with the ACIS (30–40 min) than with the CAS 200 (40–70 min). Results obtained by ACIS gave excellent inter-and intra-observer variability (coefficient of correlation >0.9 for both, p < 0.0001). CONCLUSION: Compared with the CAS 200, the ACIS is a more sensitive and less time consuming technique for determining DNA ploidy. Results obtained by ACIS are also highly reproducible

Comparison of DNA histograms by standard flow cytometry and image cytometry on sections in Barrett's adenocarcinoma

<p>Abstract</p> <p>Background</p> <p>The purpose of this study was to compare DNA histograms obtained by standard flow cytometry (FC) and high fidelity image cytometry on sections (ICS) in normal gastrointestinal mucosa and Barrett's adenocarcinoma (BAC).</p> <p>Methods</p> <p>Archival formalin-fixed paraffin-embedded tissue blocks of 10 normal controls from 10 subjects and 42 BAC tissues from 17 patients were examined. DNA FC was performed using standard techniques and ICS was carried out by Automated Cellular Imaging System (ACIS). DNA ploidy histograms were classified into diploid with peak DNA index (DI) at 0.9–1.1, and aneuploid with peak DI > 1.1. DI values of aneuploid peaks were determined. Additionally, for DNA ICS, heterogeneity index (HI) representing DNA content heterogeneity, and histograms containing cells with DI > G2 were also identified.</p> <p>Results</p> <p>All control samples were diploid by both FC and ICS analyses. In BAC, FC showed diploid peaks in 29%, diploid peaks with additional aneuploid or tetraploid peaks in 57%, and 14% of the samples, respectively. In contrast, ICS showed aneuploid peaks in all the cases with peak DI > 1.25; 37 cases had peak DI between 1.25 and 2.25; and 5 cases had peak DI > 2.25. HI values (mean ± SD) were 11.3 ± 1.1 in controls and 32.4 ± 8.5 in BAC (p < 0.05). Controls had no G2 exceeding cells. However, 19/37 (51%) of the cases with primary peak DI < 2.25 had cells exceeding 9N.</p> <p>Conclusion</p> <p>ICS detects DNA aneuploidy in all BAC samples while FC missed the diagnosis of aneuploidy in 29%. In addition, ICS provides more information on HI and G2 exceeding rates.</p