title: Computer-Aided Nodule Assessment and Risk Yield Risk Management of Adenocarcinoma: The Future of Imaging?
abstract: 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.
authors: Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota.
authors: Finbar
authors: F
authors: Foley
authors: Department of Radiology, Mayo Clinic College of Medicine, Rochester, Minnesota.
authors: Srinivasan
authors: S
authors: Rajagopalan
authors: Department of Radiology, Mayo Clinic College of Medicine, Rochester, Minnesota.
authors: Sushravya M
authors: SM
authors: Raghunath
authors: Department of Pathology, Mayo Clinic, Rochester, Minnesota.
authors: Jennifer M
authors: JM
authors: Boland
authors: Biomedical Imaging Resource, Mayo Clinic, Rochester, Minnesota.
authors: Ronald A
authors: RA
authors: Karwoski
authors: Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.
authors: Fabien
authors: F
authors: Maldonado
authors: Department of Radiology, Mayo Clinic College of Medicine, Rochester, Minnesota.
authors: Brian J
authors: BJ
authors: Bartholmai
authors: Department of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota. Electronic address: peikert.tobias@mayo.edu.
authors: Tobias
authors: T
authors: Peikert
mesh_terms: D000230:Adenocarcinoma / Q000000981:diagnostic imaging*; D000077192:Adenocarcinoma of Lung; D003936:Diagnosis, Computer-Assisted; D055088:Early Detection of Cancer; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging* / Q000473:pathology; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008403:Mass Screening; D018570:Risk Assessment; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed
keywords: lung adenocarcinoma; lung cancer screening; pulmonary nodule; quantitative image analytics; risk stratification