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Scientists develop a model to predict which patients will have a poor outcome from lung cancer

The researchers hope that their findings will eventually enable physicians to distinguish between patients who require aggressive treatment and those who may only require routine follow-up.

The Moffitt Cancer Center’s researchers developed a model based on the radiomic feature in order to improve the ability to identify patients at increased risk of poor survival. According to scientists, radiomics is a branch of science that employs imaging techniques such as CT scans and MRIs to deduce tumor patterns and characteristics that may be difficult to detect with the naked eye.

The team of scientists published their findings in the journal Cancer Biomarkers.

“Another serious adverse effect of cancer screening and early detection is overtreatment. Identification of patients with aggressive, high-risk tumors associated with extremely poor survival outcomes would assist oncologists in determining which patients may require more aggressive treatment, such as adjuvant therapies. On the other hand, patients with less aggressive, low-risk tumors have a better chance of being cured surgically and may not require adjuvant therapy,” Matthew Schabath, Ph.D., an associate member of Moffitt’s Cancer Epidemiology Department, said in a statement.

According to the researchers, they are attempting to identify biomarkers that can be used to forecast tumor behavior. These researchers assert that these biomarkers will aid in distinguishing lung cancers detected during screening tests that require aggressive treatment from those that are slow-growing and may be cured solely through surgery.

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Unlike biomarkers derived from tissue or blood samples, radiomic biomarkers are collected noninvasively and reflect the entire tumor rather than just a small sample, according to the researchers.

Scientists develop a model to predict which patients will have a poor outcome from lung cancer
Scientists develop a model to predict which patients will have a poor outcome from lung cancer

The researchers analyzed radiomic features of the internal and surrounding tumor area using images from the National Lung Screening Trial. They then developed a model using the radiomic characteristic of compactness and the volume doubling time of sequential patient images from baseline to the first and second follow-up. The model classified patients into groups based on their likelihood of having a poor outcome.

Additionally, a similar pattern was observed for patients with early-stage lung cancers and those diagnosed with lung cancer during their initial follow-up. Additionally, the researchers identified a volume doubling time cut-off that could be used to distinguish between patients with aggressive versus low-risk tumors.

The researchers hope that their findings will one day enable physicians to distinguish between patients who require aggressive treatment and those who may only require routine follow-up.

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