Features selection and prediction models development
POSITIVE
Two independent prediction models have been established based on the SUVmax and size of hypermetabolic mediastinal–hilar LNs, respectively. Their combination was considered as the diagnostic efficacy of PET/CT. The CBI Model was developed via 4 valuable clinical and biological features selected only using Lasso algorithm due to the low dimensionality of CBI sub-dataset with 34 features (Features selection for prediction models using Lasso algorithm using tenfold cross-validation in the training set. The Pre-score (LN SUVmax) = − 2.79 + 0.57*LN SUVmax.Pre-score (LN Enlarged) =  − 0.62 + 1.84*LN Enlarged (Negative: 0, Positive: 1).Pre-score (LN_PET/CT) =  − 2.68 + 0.50* LN SUVmax + 0.56* LN Enlarged.Pre-score (CBI Model) = 1.70–0.07*Age + 0.03*Weight (Kg) + 0.04*CA153 (U/mL) + 0.65*CEA status (Negative: 0, Positive: 1).Pre-score (Rad Model) =  − 381.80 + 4.71e−09*PET_WLLH_GLCM_Cluster Shade + 312.10*PET_WHLL_GLRLM_Short Run Emphasis + 5.31* PET_WHLH_GLDM_Large Dependence Low Gray Level Emphasis − 2.23e–10*PET_WHHL_GLCM_Cluster Prominence + 71.66*PET_WHHL_GLCM_Informational Measure of Correlation 2 (Imc2) − 4.26* CT_shape_Surface Volume Ratio (SVR) + 0.03*CT _first order_90 Percentile − 0.04*CT_GLDM_Large Dependence Low Gray Level Emphasis + 0.33*CT_WLHH_first order_Median − 3.00*CT_WHHL_GLCM_MCC.Pre-score (CBR Model) =  − 88.38 + 0.57*LN SUVmax + 0.57*LN Enlarged − 0.12*Age + 0.91*CEA status + 90.95*PET_WHHL_GLCM_Imc2 − 2.06* CT_ shape_SVR + 0.38*CT_WLLH_GLDM_Dependence Entropy.LN + patients generally had higher Pre-scores in all prediction models than those in LN− patients (Violin plot of 6 prediction models for LN− (blue) and LN + (red) patients in training set (