Virtual microscopy and grid-enabled decision support for large-scale analysis of imaged pathology specimens

Authors:
Lin Yang;Wenjin Chen;Peter Meer;Gratian Salaru;Lauri A. Goodell;Viktors Berstis;David J. Foran
Affiliations:
University of Medical and Dentistry of New Jersey, Robert Wood Johnson Hospital, New Brunswick, NJ and Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ and Can ...;Cancer Institute of New Jersey, New Brunswick, NJ;Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ;Department of Pathology, University of Medical and Dentistry of New Jersey, Robert Wood Johnson Hospital, New Brunswick, NJ;Department of Pathology, University of Medical and Dentistry of New Jersey, Robert Wood Johnson Hospital, New Brunswick, NJ;IBM research, Austin, TX;Center for Biomedical Imaging and Informatics, Robert Wood Johnson Medical School, University of Medical and Dentistry of New Jersey, Piscataway, NJ
Venue:
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Year:
2009

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Abstract

Breast cancer accounts for about 30% of all cancers and 15% of cancer deaths in women. Advances in computer-assisted analysis hold promise for classifying subtypes of disease and improving prognostic accuracy. We introduce a grid-enabled decision support system for performing automatic analysis of imaged breast tissue microarrays. To date, we have processed more than 1 00 000 digitized specimens (1200 × 1200 pixels each) on IBM's World Community Grid (WCG). As a part of the Help Defeat Cancer (HDC) project, we have analyzed that the data returned from WCG along with retrospective patient clinical profiles for a subset of 3744 breast tissue samples, and have reported the results in this paper. Texture-based features were extracted from the digitized specimens, and isometric feature mapping was applied to achieve nonlinear dimension reduction. Iterative prototyping and testing were performed to classify several major subtypes of breast cancer. Overall, the most reliable approach was gentle AdaBoost using an eight-node classification and regression tree as the weak learner. Using the proposed algorithm, a binary classification accuracy of 89% and the multiclass accuracy of 80% were achieved. Throughout the course of the experiments, only 30% of the dataset was used for training.