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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