Researchers believe a radiomics approach can eliminate false positives in CT imaging for lung cancer

A team of U.S. researchers have found a radiomics approach to analyze CT images of lung cancers that may eliminate false positives during cancer screening.

Results were published by a group from the Mayo Clinic, Brown University and Vanderbilt University May 14 in the journal 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," said first author Tobias Peikert, MD, a pulmonologist at Mayo Clinic, in a release. "Even in individuals who are at high risk for lung cancer, up to 96 percent of nodules are not cancer."

Peikert and colleagues applied a radiomics approach to the lung cancer CT data of 726 nodules from the National Lung Cancer Screening Trial. Researchers tested a set of 57 quantitative radiologic features for volume, nodule density, shape, nodule surface characteristics and texture of the surrounding lung tissue.

Results found the approach “reliably distinguishes benign from malignant nodules,” authors wrote in the study.

The model has yet to be externally validated. But once ready for clinical use, the approach could have a positive impact on many areas of lung cancer care.

“This approach, if externally validated, could inform management of screen-identified pulmonary nodules and potentially minimize morbidity, mortality, health care costs, radiation exposure and patient anxiety associated with the currently accepted approach for the evaluation and management of indeterminate pulmonary nodules,” Peikert et al. wrote.