Jun 11 2024Society of Nuclear Medicine and Molecular Imaging By automatically quantifying tumor burden, the new tool can be useful for assessing patient risk, predicting treatment response, and estimating survival.
Kevin H. Leung, PhD, research associate at Johns Hopkins University School of Medicine in Baltimore, Maryland The AI approach automatically extracted radiomic features and whole-body imaging measures from the predicted tumor segmentations to quantify molecular tumor burden and uptake across all cancer types. Quantitative features and imaging measures were used to build predictive models to demonstrate prognostic value for risk stratification, survival estimation, and prediction of treatment response in patients with cancer.
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