Multi-Window CT Based Radiomics Features are Valuable Predictors of Indolent Lung Cancers

June 4, 2020 | Lung Cancer

Hong Lu, MD and colleagues at Moffitt Cancer Center reported that quantitative radiomic signatures showed the potential to reveal and predict tumor growth speed non-invasively, and could identify the indolent subgroup from aggressive lung cancer. Overall, radiomic features extracted from the combined window yielded a highly predictive model to discriminate indolent from aggressive lung cancers which yielded an accuracy of 84.67%. They concluded that the features would be valuable for precision lung cancer screening and longitudinal management of lung cancer.

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