https://www.selleckchem.com/products/rxc004.html
Machine learning models were developed and validated to identify lung adenocarcinoma (LUAD) and lungsquamouscellcarcinoma (LUSC) using clinical factors, laboratory metrics, and 2-deoxy-2[ F]fluoro-D-glucose ([ F]F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomic features. One hundred and twenty non-smallcelllungcancer (NSCLC) patients (62 LUAD and 58 LUSC) were analyzed retrospectively and randomized into a training group (n = 85) and validation group (n = 35). A total of 99 feature parameters-four clinical facto
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