Table 2:

Five-fold cross-validation and testing results for tumor-related parameters using different machine learning and deep learning models

	KTRES_rel		KTRES_n		KTRES_max		KTRES_50
Model	5-fold CV	Test	5-fold CV	Test	5-fold CV	Test	5-fold CV	Test
LR
R2	−0.08 ± 0.16	0.04	−0.03 ± 0.03	0.03	−0.04 ± 0.18	0.06	−0.04 ± 0.04	0.06
RMSE	2.05 ± 0.45	2.49	2.25 ± 0.15	2.32	3.59 ± 1.03	2.25	33.37 ± 4.86	35.28
SVR
R2	−0.05 ± 0.06	−0.01	−0.08 ± 0.06	−0.05	−0.02 ± 0.01	0.001	−0.18 ± 0.15	−0.02
RMSE	2.03 ± 0.48	2.54	2.29 ± 0.19	2.4	3.62 ± 1.16	2.31	35.4 ± 5.16	36.8
RF
R2	0.03 ± 0.13	0.18	0.138 ± 0.08	0.4	0.035 ± 0.28	0.43	0.11 ± 0.06	0.16
RMSE	1.96 ± 0.49	2.3	2.05 ± 0.07	1.83	3.37 ± 1.05	1.75	31.9 ± 5.33	30.68
XGBoost
R2	0.001 ± 0.13	0.13	0.085 ± 0.103	0.07	0.07 ± 0.24	0.36	0.079 ± 0.05	0.15
RMSE	1.99 ± 0.53	2.37	2.1 ± 0.11	2.27	3.35 ± 1.19	1.84	32.7 ± 4.74	30.78
LightGBM
R2	0.045 ± 0.09	0.2	0.025 ± 0.09	0.11	0.15 ± 0.43	0.31	0.131 ± 0.07	0.16
RMSE	2.03 ± 0.49	2.28	2.17 ± 0.147	2.22	3.58 ± 1.00	1.92	31.7 ± 4.79	30.6
DNN
R2	0.67 ± 0.34	0.47	0.47 ± 0.23	0.53	0.44 ± 0.29	0.91	0.22 ± 0.37	0.23
RMSE	0.74 ± 0.46	1.85	1.51 ± 0.34	1.6	2.65 ± 1.47	0.71	28.6 ± 6.52	32.15

Abbreviations: LR, linear regression; RF, random forest; SVM, regular support vector machine; DNN, deep learning neural network; CV, cross-validation; XGBoost, eXtreme Gradient Boosting; LightGBM, light gradient-boosting machine.