Computer-Aided Nodule Assessment and Risk Yield Risk Management of Adenocarcinoma: The Future of Imaging?

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

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Increased clinical use of chest high-resolution computed tomography results in increased identification of lung adenocarcinomas and persistent subsolid opacities. However, these lesions range from very indolent to extremely aggressive tumors. Clinically relevant diagnostic tools to noninvasively risk stratify and guide individualized management of these lesions are lacking. Research efforts investigating semiquantitative measures to decrease interrater and intrarater variability are emerging, and in some cases steps have been taken to automate this process. However, many such methods currently are still suboptimal, require validation and are not yet clinically applicable. The computer-aided nodule assessment and risk yield software application represents a validated tool for the automated, quantitative, and noninvasive tool for risk stratification of adenocarcinoma lung nodules. Computer-aided nodule assessment and risk yield correlates well with consensus histology and postsurgical patient outcomes, and therefore may help to guide individualized patient management, for example, in identification of nodules amenable to radiological surveillance, or in need of adjunctive therapy.

Original languageEnglish (US)
Pages (from-to)120-126
Number of pages7
JournalSeminars in thoracic and cardiovascular surgery
Issue number1
StatePublished - Feb 1 2016


  • lung adenocarcinoma
  • lung cancer screening
  • pulmonary nodule
  • quantitative image analytics
  • risk stratification

ASJC Scopus subject areas

  • Surgery
  • Pulmonary and Respiratory Medicine
  • Cardiology and Cardiovascular Medicine


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