CT纹理分析：结直肠癌KRAS基因突变状态评估的潜在生物标志物

doi:10.24920/003770

Abstract

Objective Texture analysis is deemed to reflect intratumor heterogeneity invisible to the naked eyes. The aim of this study was to evaluate the feasibility of assessing the KRAS mutational status in colorectal cancer (CRC) patients using CT texture analysis.
Methods This retrospective study included 92 patients who had histopathologically confirmed CRC and underwent preoperative contrast-enhanced CT examinations. The patients were assigned into a training cohort (n=51) and a validation cohort (n=41). We placed the region of interest in the tumour regions on the selected axial images using software of TexRad to extract a series of quantitative parameters based on the spatial scaling factors (SSFs), including mean, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. The texture parameters and clinical characteristics (age, gender, tumour location, histopathology, tumour size, T, N, M stages) were compared between the mutated and wild-type KRAS patient groups in training cohort and validation cohort. Before building the multiple feature classifier, we calculated the correlations of the features using Pearson’s correlation coefficient, and if any two features were significantly correlated, the one with lower AUC was removed. Ultimately, only the most discriminative isolated features were combined to train a supporting vector machine (SVM) classifier. The receiver operating characteristic (ROC) curve was processed for evaluating the diagnostic efficiency of texture parameters in differentiating CRC patients with mutated KRAS from those with wild-type KRAS.
Results None of the clinical characteristics were significant different between CRC patients with wild-type KRAS and mutated KRAS in both cohorts. For predicting the expression of mutated KRAS in CRC patients, the perfect model which combined skewness on SSF 5 by unenhanced CT, entropy on SSF 2, skewness and kurtosis on SSF 0, and kurtosis and mean on SSF 3 by enhanced CT, showed a desirable AUC of 0.951 (95% CI: 0.895-1, P<0.001), with a sensitivity of 88.9% and a specificity of 91.7%, when the cut-off value was 0.46 in the training cohort; while in the validation cohort, the AUC value was 0.995 (95% CI: 0.982-1, P<0.001), the sensitivity was 100%, and the specificity was 93.7% when the cut-off value was 0.28.
Conclusion It is feasible to evaluate the KRAS mutational status in CRC using CT texture analysis.

Key words: biological markers, colorectal neoplasms, texture analysis, computed tomography

Funding: National Public Welfare Basic Scientific Research Program of Chinese Academy of Medical Sciences(2018PT32003)

Jian Cao, Guorong Wang, Zhiwei Wang, Zhengyu Jin. CT Texture Analysis: A Potential Biomarker for Evaluating KRAS Mutational Status in Colorectal Cancer[J].Chinese Medical Sciences Journal, 2020, 35(4): 306-314.

Figures/Tables 5

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Characteristics	Training cohort				Validation cohort
Characteristics	Mutated group (n=27)	Wild-type group (n=24)	t/x²	P	Mutated group (n=25)	Wild-type group (n=16)	t/x²	P
Age (years)	61.1±10.4	59.4±13.9	0.258	0.620	58.0±14.4	59.9±9.8	0.223	0.610
Gender [n (%)]			1.457	0.227			0.009	0.923
Male	9 (33.3)	12 (50.0)			9 (36.0)	6 (37.5)
Female	18 (66.7)	12 (50.0)			16 (64.0)	10 (62.5)
Tumor location [n (%)]			0.695	0.952			9.163	0.057
Ascending colon	8 (29.6)	7 (29.2)			10 (40.0)	1 (6.3)
Transverse colon	2 (7.4)	1 (4.2)			0 (0)	1 (6.3)
Descending colon	2 (7.4)	1 (4.2)			0 (0)	2 (12.5)
Sigmoid colon	6 (22.2)	7 (29.2)			5 (20.0)	4 (25.0)
Rectum	9 (33.3)	8 33.3)			10 (40.0)	8 (50.0)
Tumor size (mm)	14.6±3.3	15.2±3.8	0.357	0.553	15.4±3.9	14.2±3.4	0.898	0.349
Histological grade [n (%)]			1.153	0.562			0.391	0.822
Well	8 (29.6)	5 (20.8)			3 (12.0)	2 (12.5)
Moderate	14 (51.9)	16 (66.7)			17 (68.0)	12 (75.0)
Poor	5 (18.5)	3 (12.5)			5 (20.0)	2 (12.5)
T stage [n (%)]			1.979	0.372			1.128	0.288
T1	0 (0)	0 (0)			0 (0)	0 (0)
T2	4 (14.8)	1 (4.2)			0 (0)	0 (0)
T3	12 (44.4)	10 (41.7)			13 (52.0)	11 (68.8)
T4	11 (40.7)	13 (54.2)			12 (48.0)	5 (31.3)
N stage [n (%)]			2.204	0.332			2.046	0.360
N0	8 (29.6)	3 (12.5)			9 (36.0)	4 (25.0)
N1	9 (33.3)	10 (41.7)			10 (40.0)	10 (62.5)
N2	10 (37.0)	11 (45.8)			6 (24.0)	2 (12.5)
M stage [n (%)]			2.422	0.120			0.010	0.922
M0	4 (14.8)	8 (33.3)			5 (20.0)	3 (18.8)
M1	23 (85.2)	16 (66.7)			20 (80.0)	13 (81.3)

Models	CT images	Selected features	Training cohort (n=51)				Validation cohort (n=41)
Models	CT images	Selected features	AUC (95% CI)	Cut-off value	Sen (%)	Spe (%)	AUC (95% CI)	Cut-off value	Sen (%)	Spe (%)
Model 1	CE	entropy (CT_SSF 2)	0.951 (0.895-1)	4.11*	91.7	88.9	0.951 (0.891-1)	4.06*	100	84
Model 2	Non-CE	skewness (CT_SSF 5)	0.951 (0.895-1)	0.46^#	88.9	91.7	0.995 (0.982-1)	0.28^#	100	93.7
	CE	skewness (CT_SSF 0)
	CE	entropy (CT_SSF 2)
	CE	kurtosis (CT_SSF 0)
	CE	kurtosis (CT_SSF 3)
	CE	mean (CT_SSF 3)

Models	CT images	Selected features	Training cohort				Validation cohort
Models	CT images	Selected features	AUC (95% CI)	Cut-off value	Sen (%)	Spe (%)	AUC (95% CI)	Cut-off value	Sen (%)	Spe (%)
Model 3	Non-CE	MPP (CT_SSF 0) entropy (CT_SSF 2) skewness (CT_SSF 3) kurtosis (CT_SSF 5)	0.975 (0.939-1)	0.39*	96.3	91.7	0.963 (0.907-1)	0.79*	88.0	93.7
Model 4	CE	kurtosis (CT_SSF 0) entropy (CT_SSF 2) kurtosis (CT_SSF 3) skewness (CT_SSF 4)	0.951 (0.895-1)	0.46*	88.9	91.7	0.951 (0.891-1)	0.80*	84.0	100

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