Risks groups for early stage non-small cell lung cancer (NSCLC) stratified by computed tomography (CT) images through novel software

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Computer-aided nodule assessment and risk yield (CANARY) is a novel software tool that automatically quantitates adenocarcinoma pulmonary nodule characteristics from noninvasive high-resolution computed tomography (HRCT) images. This software stratifies patients with non-small cell lung cancer (NSCLC) into risk groups that have significantly different disease-free survival outcomes, according to a new study.

The majority of patients with NSCLC have advanced-stage disease at diagnosis, which is concomitant with an exceptionally poor prognosis (5-year survival rate of 4%). In contrast, tumors detected at an early stage have 5-year survival rates of 54%.

The National Lung Cancer Screening Trial (NLST) demonstrated a 20% reduction in lung-cancer specific mortality by screening with HRCT, but many detected nodules are noncancerous and slow growing, which can lead to costly and risky overdiagnosis and overtreatment. Thus, better risk classification based on the nodules characteristics is needed.

HRCT images from 264 clinical stage I pulmonary nodules of the lung adenocarcinoma spectrum were analyzed with the CANARY system developed at the Mayo Clinic in Rochester, Minnesota. The software used an unsupervised clustering algorithm to classify the patients into categories of similar nodule characteristics.

The results, published in the Journal of Thoracic Oncology (2014; doi:10.1097/JTO.0000000000000319), show that the adenocarcinomas naturally segregated into three groups based on HRCT characteristics. The three identified groups corresponded to good, intermediate, and poor postoperative outcomes with 5-year disease-free survival rates of 100%, 73%, and 51%, respectively.

“Our preliminary assessment suggests CANARY represents a robust risk stratification tool that can be utilized with a wide variety of HRCT techniques and equipment for retrospective or prospective evaluation of lung nodules in a real-world setting,” concluded the authors.

“HRCT-based CANARY classification could ultimately guide the individualized treatment of HRCT-detected lesions with nodules noninvasively categorized as good managed with less aggressive surgical approaches, noninvasive or minimally invasive therapy or watchful waiting; whereas nodules that have characteristics corresponding to the poor group would be managed with current standard of care, such as lobectomy, and perhaps additional adjuvant therapy,” suggested lead author Sushravya Raghunath, PhD.

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