Morphologic variations of disease are often linked to underlying molecular events and patient outcome, suggesting that quantitative morphometric analysis may provide further insight into disease mechanisms. In this paper a methodology for the subclassification of disease is developed using image analysis techniques. Morphologic signatures that represent patient-specific tumor morphology are derived from the analysis of hundreds of millions of cells in digitized whole slide images. Clustering these signatures aggregates tumors [...]
Author(s): Cooper, Lee A D, Kong, Jun, Gutman, David A, Wang, Fusheng, Gao, Jingjing, Appin, Christina, Cholleti, Sharath, Pan, Tony, Sharma, Ashish, Scarpace, Lisa, Mikkelsen, Tom, Kurc, Tahsin, Moreno, Carlos S, Brat, Daniel J, Saltz, Joel H
DOI: 10.1136/amiajnl-2011-000700