A web-based system for tissue microarray data management

PATMA: parser of archival tissue microarray

Tissue microarrays are commonly used in modern pathology for cancer tissue evaluation, as it is a very potent technique. Tissue microarray slides are often scanned to perform computer-aided histopathological analysis of the tissue cores. For processing the image, splitting the whole virtual slide into images of individual cores is required. The only way to distinguish cores corresponding to specimens in the tissue microarray is through their arrangement. Unfortunately, distinguishing the correct order of cores is not a trivial task as they are not labelled directly on the slide. The main aim of this study was to create a procedure capable of automatically finding and extracting cores from archival images of the tissue microarrays. This software supports the work of scientists who want to perform further image processing on single cores. The proposed method is an efficient and fast procedure, working in fully automatic or semi-automatic mode. A total of 89% of punches were correctly extracted with automatic selection. With an addition of manual correction, it is possible to fully prepare the whole slide image for extraction in 2 min per tissue microarray. The proposed technique requires minimum skill and time to parse big array of cores from tissue microarray whole slide image into individual core images.

Datamining approach for automation of diagnosis of breast cancer in immunohistochemically stained tissue microarray images

Cancer of the breast is the second most common human neoplasm, accounting for approximately one quarter of all cancers in females after cervical carcinoma. Estrogen receptor (ER), Progesteron receptor and human epidermal growth factor receptor (HER-2/neu) expressions play an important role in diagnosis and prognosis of breast carcinoma. Tissue microarray (TMA) technique is a high throughput technique which provides a standardized set of images which are uniformly stained, facilitating effective automation of the evaluation of the specimen images. TMA technique is widely used to evaluate hormone expression for diagnosis of breast cancer. If one considers the time taken for each of the steps in the tissue microarray process workflow, it can be observed that the maximum amount of time is taken by the analysis step. Hence, automated analysis will significantly reduce the overall time required to complete the study. Many tools are available for automated digital acquisition of images of the spots from the microarray slide. Each of these images needs to be evaluated by a pathologist to assign a score based on the staining intensity to represent the hormone expression, to classify them into negative or positive cases. Our work aims to develop a system for automated evaluation of sets of images generated through tissue microarray technique, representing the ER expression images and HER-2/neu expression images. Our study is based on the Tissue Microarray Database portal of Stanford university at http://tma.stanford.edu/cgi-bin/cx?n=her1, which has made huge number of images available to researchers. We used 171 images corresponding to ER expression and 214 images corresponding to HER-2/neu expression of breast carcinoma. Out of the 171 images corresponding to ER expression, 104 were negative and 67 were representing positive cases. Out of the 214 images corresponding to HER-2/neu expression, 112 were negative and 102 were representing positive cases. Our method has 92.31% sensitivity and 93.18% specificity for ER expression image classification and 96.67% sensitivity and 88.24% specificity for HER-2/neu expression image classification.

Flow cytometric immunophenotyping (FCI) of lymphoma: correlation with histopathology and immunohistochemistry

BACKGROUND

To evaluate the role of flow cytometric immunophenotyping (FCI) in diagnosis and characterization of lymphoma tissue specimens from Egyptian patients.

METHODS

FCI using 2 and 3 color staining approaches, was performed on 50 fresh lymph nodes specimen from Cairo NCI patients with suspected lymphoma presenting with either localized or generalized lymphadenopathy.FCI results were correlated with histopathologic as well as immunophenotypic[by immunohistochemistry (IHC)] findings.

RESULTS

By FCI, cases were diagnosed as follows: 9(18%) reactive hyperplasia (RH), 32(64%) B-cell non-Hodgkin's lymphoma (B-NHL) [24 diffuse large (DLBCL), 2 follicular, 3 small lymphocytic, 2 mantle cell lymphoma and a case of T cell rich B cell lymphoma], 3 (6%) T cell NHL [2 peripheral T cell lymphoma and a case of anaplastic large cell lymphoma], 2(4%) Hodgkin's lymphoma (HL) while 4 (8%) were non-lymphomatous tumors (NLT). Light chain restriction (LCR) was detected in the 32 FCI diagnosed B-NHL. The overall concordance between FCI versus histopathology and IHC was 88%. The sensitivity and specificity of FCI in diagnosis of NHL was 94.9% and 100% respectively; in HL they were 40% and 100% respectively and in NLT, both sensitivity and specificity were 100% while for RH were 100% and 89.1% respectively.

CONCLUSION

FCI is a sensitive and specific method in diagnosis and classification of NHL as well as in detection of monoclonality. False negative results could be due to the presence of heterogeneous populations of lymphocytes in special types of lymphoma.

Ontology-based, Tissue MicroArray oriented, image centered tissue bank

BACKGROUND

Tissue MicroArray technique is becoming increasingly important in pathology for the validation of experimental data from transcriptomic analysis. This approach produces many images which need to be properly managed, if possible with an infrastructure able to support tissue sharing between institutes. Moreover, the available frameworks oriented to Tissue MicroArray provide good storage for clinical patient, sample treatment and block construction information, but their utility is limited by the lack of data integration with biomolecular information.

RESULTS

In this work we propose a Tissue MicroArray web oriented system to support researchers in managing bio-samples and, through the use of ontologies, enables tissue sharing aimed at the design of Tissue MicroArray experiments and results evaluation. Indeed, our system provides ontological description both for pre-analysis tissue images and for post-process analysis image results, which is crucial for information exchange. Moreover, working on well-defined terms it is then possible to query web resources for literature articles to integrate both pathology and bioinformatics data.

CONCLUSIONS

Using this system, users associate an ontology-based description to each image uploaded into the database and also integrate results with the ontological description of biosequences identified in every tissue. Moreover, it is possible to integrate the ontological description provided by the user with a full compliant gene ontology definition, enabling statistical studies about correlation between the analyzed pathology and the most commonly related biological processes.