Construction of a predictive model for gastric cancer neuroaggression and clinical validation analysis: A single-center retrospective study

doi:10.4240/wjgs.v16.i8.2602

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World Journal of Gastrointestinal Surgery

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1948-9366

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Retrospective Study

World J Gastrointest Surg. Aug 27, 2024; 16(8): 2602-2611
Published online Aug 27, 2024. doi: 10.4240/wjgs.v16.i8.2602

Table 1 Clinical data analysis of 80 gastric cancer patients, n (%)

Project	PNI positive group (n = 54)	PNI negative group (n = 26)	T/χ²/Z	P value
Age (years, mean ± SD)	65.00 ± 8.75	66.76 ± 4.82	-0.823	0.416
Sex			4.786	0.029
Male	42 (78)	14 (54)
Female	12 (22)	12 (46)
Alpha fetoprotein, ng/mL	2.980 (2.160, 3.650)	2.590 (1.700, 3.780)	0.578	0.568
Carcinoembryonic antigen (ng/mL)	56.735 ± 268.309	2.109 ± 1.085	0.729	0.470
CA125 (U/mL, mean ± SD)	124.600 ± 540.554	9.191 ± 4.435	0.765	0.449
CA199 [U/mL, M (Q1, Q3)]	16.485 (11.890, 36.635)	6.77 (3.260, 16.335)	3.133	0.001
Tumor thickness (mm, mean ± SD)	20.149 ± 6.410	14.746 ± 5.065	2.664	0.011
Lauren typing			15.949	0.000
Intestinal type	16 (30)	20 (77)
Diffuse type	16 (30)	2 (8)
Mixed type	22 (40)	4 (15)
Borrmann classification			13.219	0.004
I	2 (3)	2 (7)
Ⅱ	8 (15)	12 (47)
Ⅲ	34 (63)	6 (23)
IV	10 (19)	6 (23)
Differentiation degree			2.825	0.244
High	10 (19)	2 (8)
Middle	18 (33)	8 (31)
Low	26 (48)	16 (61)

CA125: Carbohydrate antigen 125; CA199: Carbohydrate antigen 199; M: Median.

Table 2 Energy spectrum computed tomography parameter analysis of 80 gastric cancer patients

Project	PNI positive group (n = 54)	PNI negative (n = 26)	T/Z value	P value
Arterial phase
CT value [Hu, M (Q1, Q3)]	67.240 (56.800, 80.290)	59.130 (52.585, 71.875)	1.141	0.264
CT_{60 kev} [Hu, M (Q1, Q3)]	88.040 (72.260, 107.690)	62.370 (60.235, 75.330)	3.018	0.002
CT_{70 kev} [Hu, M (Q1, Q3)]	68.690 (59.260, 86.120)	56.750 (50.760, 67.735)	2.584	0.009
CT_{80 kev} (Hu, mean ± SD)	61.944 ± 14.476	50.405 ± 9.996	2.584	0.014
CT_{90 kev} (Hu, mean ± SD)	54.610 ± 11.875	45.160 ± 8.884	2.540	0.015
CT_{100 kev} (Hu, mean ± SD)	49.912 ± 10.568	41.404 ± 8.025	2.562	0.014
CT_{110 kev} (Hu, mean ± SD)	46.061 ± 9.261	38.783 ± 7.537	2.463	0.018
IC [μg/cm³, M (Q1, Q3)]	14.400 (11.320, 18.720)	11.310 (10.375, 13.815)	1.863	0.064
ICao (μg/cm³, mean ± SD)	105.161 ± 21.732	114.863 ± 18.730	-1.380	0.176
NIC [M (Q1, Q3)]	0.135 (0.097, 0.205)	0.097 (0.090, 0.125)	1.891	0.060
Portal venous phase
CT value (Hu, mean ± SD)	97.302 ± 21.606	78.460 ± 15.938	2.792	0.008
CT_{60 kev}[Hu, M (Q1, Q3)]	120.317 ± 28.755	97.011 ± 23.240	2.544	0.015
CT_{70 kev}[Hu, M (Q1, Q3)]	94.877 ± 21.566	80.289 ± 19.614	2.061	0.046
CT_{80 kev} (Hu, mean ± SD)	78.673 ± 17.013	65.790 ± 14.930	2.329	0.125
CT_{90 kev} (Hu, mean ± SD)	67.918 ± 14.126	57.403 ± 12.745	2.273	0.029
CT_{100 kev} (Hu, mean ± SD)	60.452 ± 12.191	51.541 ± 11.360	2.212	0.033
CT_{110 kev} (Hu, mean ± SD)	55.154 ± 10.891	47.410 ± 10.376	2.138	0.039
IC [μg/cm³, M (Q1, Q3)]	55.154 ± 10.891	18.270 (13.440, 18.960)	2.902	0.003
ICao [ug/cm³, M (Q1, Q3)]	48.350 (40.200, 52.150)	44.180 (36.690, 51.945)	0.462	0.648
NIC (mean ± SD)	0.528 ± 0.184	0.402 ± 0.098	2.799	0.008

CT: Computed tomography; IC: Iodine concentration; ICao: Normalized iodine concentration in the same abdominal aorta; NIC: Normalized iodine concentration; M: Median.

Table 3 Logistic regression analysis results of the model

Factor	β	Corrected OR (95%CI)	P value
Clinical features
Sex	-5.259	0.005 (0-0.318)	0.012
Tumor thickness	0.351	1.421 (1.051-1.920)	0.022
Borrmann typing	2.167	8.732 (1.486-51.324)	0.016
Spectral CT parameters
Arterial phase CT_{60 kev}	0.065	1.067 (1.012-1.124)	0.016
Portal phase NIC	0.065	1.067 (1.012-1.126)	0.016
Imaging omics features
First-order (median)	0.078	1.081 (1.015-1.151)	0.016

CT: Computed tomography; NIC: Normalized iodine concentration; OR: Odds ratio.

Table 4 Diagnostic efficacy of PNI prediction model for gastric cancer

Model name	AUC (95%CI)	Optimal threshold	Yoden index	Sensitivity	Specificity
Clinical model	0.858 (0.692-1.000)	0.486	0.772	0.926	0.845
Spectral CT model	0.832 (0.680-0.983)	0.531	0.695	0.925	0.769
Imaging omics model	0.897 (0.778-1.000)	0.718	0.809	0.963	0.845
Clinical + spectral model	0.772 (0.608-0.936)	0.643	0.735	0.889	0.845
Clinical and imaging omics models	0.842 (0.717-0.967)	0.67	0.584	0.815	0.769
Energy spectrum + imaging omics model	0.846 (0.720-0.972)	0.809	0.553	0.63	0.923
Clinical + energy spectrum + imaging omics model	0.927 (0.850-1.000)	0.879	0.778	0.778	1.00

CT: Computed tomography; AUC: Area under the curve.

Citation: Lan YY, Han J, Liu YY, Lan L. Construction of a predictive model for gastric cancer neuroaggression and clinical validation analysis: A single-center retrospective study. World J Gastrointest Surg 2024; 16(8): 2602-2611
URL: https://www.wjgnet.com/1948-9366/full/v16/i8/2602.htm
DOI: https://dx.doi.org/10.4240/wjgs.v16.i8.2602