Computational and Mathematical Methods in Medicine

Research Article

Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images

Table 3

r_s between features (portion of the handcrafted and deep features) and glioma recurrence versus necrosis (, α = 0.05, and K = 176, 4,096, and 2,048 for handcrafted, AlexNet, and Inception v3 features, respectively).


Type	Feature	Modality	r_s	value

Nontexture	Volume	T1	0.0373	0.7949
		T2
		FLAIR
		T1C
	Size	T1	0.0172	0.9045
		T2
		FLAIR
		T1C
	Solidity	T1	0.0115	0.9363
		T2
		FLAIR
		T1C
	Eccentricity	T1	−0.0172	0.9045
		T2
		FLAIR
		T1C
GLRLM	HGRE	T1	0.3273	0.0190
		T2	−0.3331	0.0169
		FLAIR	−0.3187	0.0226
		T1C	0.4594	0.0007
GLSZM	HGZE	T1	0.3790	0.0061
		T2	−0.4508	0.0009
		FLAIR	−0.4852	0.0003
		T1C	0.4738	0.0004
	SZLGE	T1	0.3876	0.0049
		T2	−0.3790	0.0061
		FLAIR	−0.4652	0.0006
		T1C	−0.3962	0.0040
	SZHGE	T1	0.4163	0.0024
		T2	−0.3446	0.0133
		FLAIR	−0.4738	0.0004
		T1C	0.3618	0.0091
AlexNet	F7_618	T1C	0.5656	0.00001
	F7_1394	T1	0.5168	0.0001
	F7_2793	FLAIR	0.4823	0.0003
	F7_3501	T2	0.4421	0.0012
Inception v3	avg_pool_663	T1	0.5770	0.0000093
	avg_pool__469	T1C	0.5483	0.000031
	avg_pool_827	FLAIR	0.3876	0.005
	avg_pool_774	T2	0.4651	0.000584

For deep feature names, the first character indicates the layer of the CNN and the second character represents the neuron. For example, F7_618 was extracted from a T1C image and taken from the 618th neuron of fully connected layer 7.