Moffitt develops predictive model of poor lung cancer outcomes 

Tampa, Fla.-based Moffitt Cancer Center researchers developed a radiomic model to predict patients who may experience poor lung cancer outcomes. 

Researchers analyzed the radiomic features of internal and surrounding tumor areas in images from the National Lung Screening Trial. They developed a model based on the radiomic feature of compactness and the volume doubling time of sequential images from baseline and follow-up images. 

Their model divided patients into groups according to their risk of having poor outcomes. The low-risk patient group had a five-year overall survival of 83.3 percent while the high-risk group had an overall five-year survival of 25 percent. Their findings were published April 27 in Cancer Biomarkers.

"The results from our analyses revealed that radiomics combined with volume doubling time can identify a vulnerable subset of screen-detected lung cancers that are associated with poor survival outcomes, suggesting that such patients may need more aggressive treatment," said Jaileene Pérez-Morales, PhD, a postdoctoral fellow in the Cancer Epidemiology Department.

"We hope to do further studies to validate our findings before applying our model to patient care."

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