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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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