New software identifies and stratifies risk posed by lung nodules

Apr. 8, 2013 ? A multidisciplinary team of researchers at Mayo Clinic has developed a new software tool to noninvasively characterize pulmonary adenocarcinoma, a common type of cancerous nodule in the lungs. Results from a pilot study of the computer-aided nodule assessment and risk yield (CANARY) are published in the Journal of Thoracic Oncology.

"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."

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The above story is reprinted from materials provided by Mayo Clinic.

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Journal Reference:

1. Fabien Maldonado, Jennifer M. Boland, Sushravya Raghunath, Marie Christine Aubry, Brian J. Bartholmai, Mariza deAndrade, Thomas E. Hartman, Ronald A. Karwoski, Srinivasan Rajagopalan, Anne-Marie Sykes, Ping Yang, Eunhee S. Yi, Richard A. Robb, Tobias Peikert. Noninvasive Characterization of the Histopathologic Features of Pulmonary Nodules of the Lung Adenocarcinoma Spectrum using Computer-Aided Nodule Assessment and Risk Yield (CANARY)?A Pilot Study. Journal of Thoracic Oncology, 2013; 8 (4): 452 DOI: 10.1097/JTO.0b013e3182843721

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

Source: http://feeds.sciencedaily.com/~r/sciencedaily/most_popular/~3/1ggmHkcLxXo/130408133044.htm

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