Method to overcome false positives in CT imaging for lung cancer

Date:
May 15, 2018
Source:
Mayo Clinic
Summary:
A team of researchers has identified a technology to address the problem of false positives in CT-based lung cancer screening.
FULL STORY

A team of researchers including investigators from Mayo Clinic has identified a technology to address the problem of false positives in CT-based lung cancer screening. The team’s findings are published in the current issue of PLOS One.

“As physicians, one of the most challenging problems in screening patients for lung cancer is that the vast majority of the detected pulmonary nodules are not cancer,” says Tobias Peikert, M.D., a pulmonologist at Mayo Clinic. “Even in individuals who are at high risk for lung cancer, up to 96 percent of nodules are not cancer.”

Dr. Peikert says false-positive test results cause significant patient anxiety and often lead to unnecessary additional testing, including surgery. “False-positive lung cancer screening results also increase health care costs and may lead to unintentional physician-caused injury and mortality,” Dr. Peikert says.

To address the problem of false positives in lung cancer screening Dr. Peikert and Fabien Maldonado, M.D., from Vanderbilt University, along with their collaborators used a radiomics approach to analyze the CT images of all lung cancers diagnosed as part of the National Lung Cancer Screening Trial. Radiomics is a field of medicine that involves extracting large amounts of quantitative data from medical images and using computer programs to identify disease characteristics that cannot be seen by the naked eye.

Researchers tested a set of 57 variables for volume, nodule density, shape, nodule surface characteristics and texture of the surrounding lung tissue. They identified eight variables which enabled them to distinguish a benign nodule from a cancerous nodule. None of the eight variables were directly linked to nodule size and the researchers did not include any demographic variables such as age, smoking status and prior cancer history as part of their testing.

Dr. Peikert says that while the technology looks very promising and has the potential to change the way physicians evaluate incidentally detected lung nodules, it still requires additional validation.


Story Source:

Materials provided by Mayo Clinic. Note: Content may be edited for style and length.


Journal Reference:

  1. Tobias Peikert, Fenghai Duan, Srinivasan Rajagopalan, Ronald A. Karwoski, Ryan Clay, Richard A. Robb, Ziling Qin, JoRean Sicks, Brian J. Bartholmai, Fabien Maldonado. Novel high-resolution computed tomography-based radiomic classifier for screen-identified pulmonary nodules in the National Lung Screening Trial. PLOS ONE, 2018; 13 (5): e0196910 DOI: 10.1371/journal.pone.0196910

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