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Early-Stage Lung Cancer Identified Using CAD Shown to Increase Survival Rates

By MedImaging International staff writers
Posted on 19 May 2009
A computer-aided detection (CAD) method may help radiologists identify cancerous lung nodules at an early stage.

"In our study, we identified 88 nodules that were not detected at the time of interpretation but visible in retrospect and were subsequently determined to be lung cancers,” said Joseph Jen-Sho Chen, M.D., from the University of Maryland Medical Center (Baltimore, MD, USA), and lead author of the study. "CAD was applied to the overlooked nodules and we found that 45-55% of the missed nodules were found using the CAD software. The implication of our study is that it is possible that at least some of the nodules representing lung cancer might have been diagnosed at an earlier stage, resulting in early treatment and perhaps a better outcome,” said Dr. Chen.

According to Dr. Chen, the complexity of the structures in the chest including the ribs, mediastinum, and pulmonary vessels can make it difficult to identify separate pulmonary nodules that may represent an early lung cancer from normal anatomy. "Computer-aided detection is a method that can be used to assist the radiologist in the search for lung cancer. The software highlights abnormalities that may be overlooked by the radiologist on an initial search,” said Dr. Chen.

This study's findings were presented at the 2009 American Roentgen Ray Society (ARRS) annual meeting in Boston, MA, USA, on April 28, 2009.

"Lung cancer accounts for more than 150,000 deaths annually in the US alone. Overall, only about 15% of patients survive five years or more, but with early detection, survival increases to greater than 70%,” said Dr. Chen. "The use of CAD may be particularly valuable in early lung cancer, where the findings are often subtle. We hope ultimately that studies such as ours will determine whether CAD should be adopted as part of the standard armamentarium for evaluating lung nodules.”

Related Links:

University of Maryland Medical Center


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Image: Researchers develop a vision-language model trained on large-scale data to generate clinically relevant findings from chest computed tomography images through visual question answering (Ms. Maiko Nagao from Meijo University, Japan)

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