Integrated diagnostics: a conceptual framework with examples.
With the advent of digital pathology, imaging scientists have begun to develop computerized image analysis algorithms for making diagnostic (disease presence), prognostic (outcome prediction), and theragnostic (choice of therapy) predictions from high resolution images of digitized histopathology. One of the caveats to developing image analysis algorithms for digitized histopathology is the ability to deal with highly dense, information rich datasets; datasets that would overwhelm most computer vision and image processing algorithms. Over the last decade, manifold learning and non-linear dimensionality reduction schemes have emerged as popular and powerful machine learning tools for pattern recognition problems. However, these techniques have thus far been applied primarily to classification and analysis of computer vision problems (e.g., face detection). In this paper, we discuss recent work by a few groups in the application of manifold learning methods to problems in computer aided diagnosis, prognosis, and theragnosis of digitized histopathology. In addition, we discuss some exciting recent developments in the application of these methods for multi-modal data fusion and classification; specifically the building of meta-classifiers by fusion of histological image and proteomic signatures for prostate cancer outcome prediction.
Madabhushi
Anant
A
Laboratory for Computational Imaging and Bioinformatics, Department of Biomedical Engineering, Rutgers University, NJ, USA.
Doyle
Scott
S
Lee
George
G
Basavanhally
Ajay
A
Monaco
James
J
Masters
Steve
S
Tomaszewski
John
J
Feldman
Michael
M
D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008297:Male; D010338:Pathology, Clinical; D010363:Pattern Recognition, Automated; D011471:Prostatic Neoplasms / Q000473:pathology; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism
D018719:Erb-b2 Receptor Tyrosine Kinases; C508053:ERBB2 protein, human