New software identifies and stratifies risk posed by lung nodules

"Pulmonary adenocarcinoma is the most common type of lung cancer and early detection using traditional computed tomography (CT) scans can lead to a better prognosis," says Tobias Peikert, M.D., a Mayo Clinic pulmonologist and senior author of the study. "However, a subgroup of the detected adenocarcinomas identified by CT may grow very slowly and may be treatable with less extensive surgery."

CANARY can noninvasively stratify the risk lung adenocarcinomas pose by characterizing the nodule as aggressive or indolent with high-sensitivity, specificity and predictive values.

CANARY uses data obtained from existing high-resolution diagnostic or screening CT images of pulmonary adenocarcinomas to match each pixel of the lung nodule to one of nine unique radiological exemplars. In testing, the CANARY classification of these lesions had an excellent correlation with the microscopic analysis of the surgically removed lesions that were examined by lung pathologists, Dr. Peikert says.

Lung cancer is the leading cause of cancer-related deaths in the United States.

"Without effective screening, most lung cancer patients present with advanced stage disease, which has been associated with poor outcomes," Dr. Peikert says. "While CT lung cancer screening has been shown to improve patient survival, the initiation of a nationwide screening program would carry the risk of overtreatment of slow growing tumors and would be associated with substantial health care costs. CANARY represents a new tool to potentially address these issues."