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About this Publication
CANARY Risk Management of Adenocarcinoma: The Future of Imaging?
j.semtcsv. 2016

Finbar Foley
Srinivasan Rajagopalan
Sushravya M. Raghunath
Jennifer M. Boland
Ronald A. Karwoski
Fabien Maldonado
Brian J. Bartholmai
Tobias Peikert


Increased clinical utilization of chest high resolution computed tomography results in increased identification of lung adenocarcinomas and persistent sub-solid opacities. However, these lesions range from very indolent to extremely aggressive tumors. Clinically relevant diagnostic tools to non-invasively risk stratify and guide individualized management of these lesions are lacking. Research efforts investigating semi-quantitative measures to decrease inter- and intra-rater variability are emerging, and in some cases steps have been taken to automate this process. However, many such methods currently are still sub-optimal, require validation and are not yet clinically applicable. The Computer-Aided Nodule Assessment and Risk Yield (CANARY) software application represents a validated tool for the automated, quantitative, non-invasive tool for risk stratification of adenocarcinoma lung nodules. CANARY correlates well with consensus histology and post-surgical patient outcomes and therefore may help to guide individualized patient management e.g. in identification of nodules amenable to radiological surveillance, or in need of adjunctive therapy.

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