Finding cancerous anaplastic cells with image analysis K. Nicol, M. Plaskow, T. Barr, D. Billiter

Anaplasia – –refers to a reversion of differentiation in cells and is characteristic of malignant neoplasms/tumors – –literally means "to form backward" It implies dedifferentiation, or loss of structural and functional differentiation of normal cells – –Anaplasia is the most extreme disturbance in cell growth encountered in the spectrum of cellular proliferations

Anaplastic cells display marked pleomorphism – –The nuclei are characteristically extremely hyperchromatic and large. They may approach 1:1 instead of the normal 1:4 or 1:6 Anaplastic nuclei are variable and bizarre in size and shape – –The chromatin is coarse and clumped, and nucleoli may be of astounding size – –With / without large atypical mitotic figures – –Giant cells that are considerably larger than their neighbors may be formed and possess either one enormous nucleus or several nuclei

Forms of Anaplasia – –Focal Wilms – –Anaplastic cells confined to a sharply localized area within the primary tumor Rhabdomyosarcoma (RMS) – –Anaplastic cells scattered among nonanaplastic cells – –Diffuse Wilms – –Non-localized anaplasia, or anaplasia noted beyond the tumor capsule, or present in a biopsy specimen RMS – –Anaplastic cells in clusters or continuous sheets

Significance of anaplasia – –Wilms tumor Recognized as the only unfavorable histological feature of this tumor – –Prognostically significant if anaplasia is seen in extrarenal tumor sites lower relapse-free survival rate – –Chemotherapy regimens are augmented if anaplasia is seen

– –Medulloblastoma Younger age at diagnosis Greater risk of extracranial metastasis Lower survival – –Rhabdomyosarcoma Significant association with anaplasia and clinical outcome in Embryonal RMS Older age at diagnosis with larger tumors

Utilization of image analysis to advance cancer research –The application was built using Microsoft technologies Visual Basic 6.0 Visual Basic.NET C++.NET SQLServer

Our Review – –Identified cases of Wilms tumor and Rhabdomyosarcoma having evidence of anaplasia – –Scanned the slides using Aperio® scanscope – –Construct algorithms to analyze cellular characteristics of the tissue on the slide

Anaplastic nuclei were detected by: – –ran many trials to determine constant pixel characteristics to define the nuclei of cells on the slide – –measured the nuclei based on total pixels of area – –Compared the nuclear “size” within the tissue section – –highlighted and “stored” the location of all “suspect” nuclei on the tissue section

Pathologists are able to set a custom sensitivity for the algorithm, which effects the area comparison between a nucleus and all of the others within the field on tissue section.

Results: – –It currently takes the anaplasia algorithm 40 minutes to process an entire slide. Slide specimens scanned in to high quality. – –TIF images (Tagged Image File) average from 80,000 to 150,000 pixels in width and 50,000 to 130,000 pixels in height, totaling 3 to 10 billion pixels per slide specimen.

Using this method, 87% of all anaplastic nuclei analyzed were located, counted, and highlighted for the pathologist/ reviewer.

Conclusion – –Pathologists are able to get electronic decision support in rapidly finding certain features for malignant tumors – –The association and utilization of a high performance computing environment within cancer research will enhance detection and optimize the pathology review process thus allowing for rapid and specific patient therapy.

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