Table 1.

Neural network hyperparameters

	CV means		Retrain
Label	R 2	epoch	R 2	epoch	Hidden layers	optimizer	activation	dropout
Transcriptome	0.45	467	0.61	486	4000/2000/1000	Adadelta	tanh	0.0
									AE
Label	AUC	epoch	AUC	epoch	Hidden layers	optimizer	activation	dropout	Layer	Frozen
Enhancing	0.72	38	1.00	14	4000/2000/1000	Adadelta	tanh	0.6	3	0
nCET	0.83	38	1.00	11	4000/2000/1000	Adadelta	tanh	0.0	1	1
Necrosis	0.75	44	1.00	11	4000/2000/1000	Adadelta	tanh	0.0	1	1
Edema	0.78	109	1.00	16	4000/2000/1000	Adadelta	tanh	0.0	1	1
Infiltrative	0.78	70	1.00	12	4000/2000/1000	Adadelta	tanh	0.0	2	1
Focal	0.85	44	1.00	12	4000/2000/1000	Adadelta	tanh	0.6	3	0
Subtype	0.99	14	0.998	66	3000/1500/750	Nadam	sigmoid	0.4	—	—

Note: Layer refers to the depth of hidden layers in the radiogenomic model that used pretrained weights from the autoencoder (AE; e.g. two AE layers indicate the first two hidden layers used pretrained weights). Retrain refers to models trained on the full dataset.