Computer-aided prognosis: predicting patient and disease outcome via multi-modal image analysis

Authors:
Anant Madabhushi;Ajay Basavanhally;Scott Doyle;Shannon Agner;George Lee
Affiliations:
Rutgers University, Department of Biomedical Engineering, Piscataway, NJ;Rutgers University, Department of Biomedical Engineering, Piscataway, NJ;Rutgers University, Department of Biomedical Engineering, Piscataway, NJ;Rutgers University, Department of Biomedical Engineering, Piscataway, NJ;Rutgers University, Department of Biomedical Engineering, Piscataway, NJ
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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Abstract

Computer-aided prognosis (CAP) is a new and exciting complement to the field of computer-aided diagnosis (CAD) and involves developing computerized image analysis and multi-modal data fusion algorithms for helping physicians predict disease outcome and patient survival. At the Laboratory for Computational Imaging and Bioinformatics (LCIB)1 at Rutgers University we have been developing computerized algorithms for high dimensional data and image analysis for predicting disease outcome from multiple modalities including MRI, digital pathology, and protein expression. Additionally, we have been developing novel data fusion algorithms based on nonlinear dimensionality reduction methods (such as Graph Embedding) to quantitatively integrate prognostic information from multiple data sources and modalities. In this paper, we briefly describe 5 representative and ongoing CAP projects at LCIB. These projects include (1) an Image-based Risk Score (IbRiS) algorithm for predicting outcome of ER+ breast cancer patients based on quantitative image analysis of digitized breast cancer biopsy specimens alone, (2) segmenting and determining extent of lymphocytic infiltration (identified as a possible prognostic marker for outcome in Her2+ breast cancers) from digitized histopathology, (3) segmenting and diagnosing highly agressive triple-negative breast cancers on dynamic contrast enhanced (DCE) MRI, (4) distinguishing patients with different Gleason grades of prostate cancer (grade being known to be correlated to outcome) from digitzed needle biopsy specimens, and (5) integrating protein expression measurements obtained from mass spectrometry with quantitative image features derived from digitized histopathology for distinguishing between prostate cancer patients at low and high risk of disease recurrence.