Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer

Table 1 Comparison of clinical characteristics between responders and non-responders, n (%)

Characteristics	Responders (n = 15)	Non-responders (n = 45)	P value
Age (years), average (mean ± SD)	57.53 (12.710)	58.64 (11.682)	0.756
Sex			0.856
Male	12 (80.0)	35 (77.8)
Female	3 (20.0)	10 (22.2)
Treatment cycles			0.579
2	2 (13.3)	8 (17.8)
3	9 (60.0)	20 (44.4)
4 +	4 (26.7)	17 (37.8)
Differentiation			0.526
High	1 (6.7)	1 (2.2)
Moderate	5 (33.3)	11 (24.4)
Poor	9 (60.0)	33 (73.3)
Primary tumor location			0.977
Cardia	3 (20.0)	8 (17.8)
Body	6 (40.0)	16 (35.6)
Antrum	4 (26.7)	14 (31.1)
Horn	2 (13.3)	7 (15.6)
PD-L1(22C3) CPS			0.237
≥ 5	10 (66.7)	26 (57.8)
< 5	5 (33.3)	19 (42.2)
CEA, median (IQR)	2.84 (0.93-17.84)	4.15 (1.75-9.52)	0.706
CA199, median (IQR)	9.08 (3.85-28.89)	16.23 (8.37-37.61)	0.264
T stage			0.004
1	4 (26.7)	0 (0)
2	1 (6.7)	4 (8.9)
3	3 (20.0)	18 (40.0)
4	7 (46.7)	23 (51.1)
N stage			0.044
0	0 (0)	1 (2.2)
1	11 (73.3)	18 (40.0)
2	4 (26.7)	18 (40.0)
3	0 (0)	8 (17.8)

The P value indicates a statistically significant difference between the responder and non-responder groups (P < 0.05). Responders: Patients with tumor regression grade 0-1 grade; Non-responders: Patients with tumor regression grade 2-3 grade; IQR: Interquartile range; CEA: Carcinoembryonic antigen; CA199: Carbohydrate antigen 19-9; PD-L1: Programmed death-ligand 1; CPS: Combined positive score.

Table 2 Baseline characteristics of enrolled advanced gastric cancer patients in the training cohort and test cohorts, n (%)

Characteristics	Total (n = 60)	Training (n = 42)	Test (n = 18)	P value
Age (years), average (mean ± SD)	58.37 (11.846)	58.26 (11.847)	58.6 (12.186)	0.918
Sex				0.945
Male	47 (78.3)	33 (78.6)	14 (77.8)
Female	13 (21.7)	9 (21.4)	4 (22.2)
Treatment cycles				0.201
2	10 (16.7)	5 (11.9)	5 (27.8)
3	29 (48.3)	23 (54.8)	6 (33.3)
4 +	21 (35)	14 (33.3)	7 (38.9)
Differentiation				0.642
High	2 (3.3)	2 (4.8)	0 (0.0)
Moderate	16 (26.7)	11 (26.2)	5 (27.8)
Poor	42 (70)	29 (69.0)	13 (72.2)
Primary tumor location				0.467
Cardia	11 (18.3)	9 (21.4)	2 (11.1)
Body	22 (36.7)	13 (31.0)	9 (50.0)
Antrum	18 (30.0)	14 (33.3)	4 (22.2)
Horn	9 (15.0)	6 (14.3)	3 (16.7)
PD-L1(22C3) CPS				0.767
≥ 5	36 (60.0)	24 (57.1)	12 (66.7)
< 5	24 (40.0)	18 (42.9)	6 (33.3)
CEA, median (IQR)	3.54 (1.41-11.09)	3.51 (1.49-11.74)	4.75 (2.10-7.89)	0.959
CA199, median (IQR)	14.63 (7.36-33.39)	16.37 (7.59-36.25)	11.13 (4.92-26.16)	0.948
T stage				0.456
1	4 (6.7)	4 (9.5)	0 (0)
2	5 (8.3)	4 (9.5)	1 (5.6)
3	21 (35.0)	13 (30.9)	8 (44.4)
4	30 (50.0)	21 (50.0)	9 (50.0)
N stage				0.344
0	1 (1.7)	0 (0)	1 (5.6)
1	29 (48.3)	22 (52.4)	7 (38.9)
2	22 (36.7)	14 (33.3)	8 (44.4)
3	8 (13.3)	6 (14.3)	2 (11.1)

The P value indicates a statistically significant difference between the responder and non-responder groups (P < 0.05). Responders: Patients with tumor regression grade 0-1 grade; Non-responders: Patients with tumor regression grade 2-3 grade; IQR: Interquartile range; CEA: Carcinoembryonic antigen; CA199: Carbohydrate antigen 19-9; PD-L1: Programmed death-ligand 1; CPS: Combined positive score.

Table 3 Comparison of radiomic models based on various machine learning methods

Radiomic model		Accuracy	AUC	95%CI	Sensitivity	Specificity	PPV	NPV
LR	Train	0.976	1	1.0000-1.0000	0.909	1	1	0.969
	Test	0.778	0.786	0.4783-1.0000	0.5	0.857	0.5	0.857
SVM	Train	0.976	1	1.0000-1.0000	0.909	1	1	0.969
	Test	0.722	0.75	0.4478-1.0000	0.5	0.786	0.4	0.846
KNN	Train	0.81	0.897	0.8034-0.9913	0.545	0.903	0.667	0.848
	Test	0.778	0.821	0.6369-1.0000	0.5	0.857	0.5	0.857
RF	Train	0.976	1	1.0000-1.0000	0.909	1	1	0.969
	Test	0.833	0.679	0.2120-1.0000	0.25	1	1	0.824
Extra trees	Train	0.976	1	1.0000-1.0000	0.909	1	1	0.969
	Test	0.722	0.616	0.2080-1.0000	0.25	0.857	0.333	0.8
XGBoost	Train	0.976	1	1.0000-1.0000	0.909	1	1	0.969
	Test	0.833	0.75	0.4352-1.0000	0.25	1	1	0.824
MLP	Train	0.952	0.997	0.9889-1.0000	0.909	0.968	0.909	0.968
	Test	0.722	0.768	0.4914-1.0000	0.5	0.786	0.4	0.846

NPV: Negative predictive value; PPV: Positive predictive value; CI: Confidence interval; AUC: Area under the curve; LR: Logistic regression; KNN: K-nearest neighbor classification; RF: Random forest; XGBoost: EXtreme gradient boosting; MLP: Multilayer perceptron; SVM: Support vector machine.

Table 4 Comparison of clinical models based on various machine learning methods

Clinical model		Accuracy	AUC	95%CI	Sensitivity	Specificity	PPV	NPV
LR	Train	0.81	0.837	0.6807-0.9938	0.636	0.871	0.636	0.871
	Test	0.278	0.482	0.1362-0.8281	0.75	0.143	0.2	0.667
SVM	Train	0.238	0.151	0.0000-0.3234	0.909	0	0.244	0
	Test	0.444	0.571	0.2439-0.8990	0.75	0.357	0.25	0.833
KNN	Train	0.786	0.787	0.6543-0.9205	0.182	1	1	0.775
	Test	0.722	0.607	0.3612-0.8531	0.25	0.857	0.333	0.8
RF	Train	0.929	0.993	0.9763-1.0000	0.818	0.968	0.9	0.937
	Test	0.556	0.688	0.4253-0.9497	0.75	0.5	0.3	0.875
Extra trees	Train	0.976	0.999	0.9945-1.0000	0.909	1	1	0.969
	Test	0.611	0.589	0.2299-0.9487	0.5	0.643	0.286	0.818
XGBoost	Train	0.857	0.937	0.8683-1.0000	0.818	0.871	0.692	0.931
	Test	0.667	0.696	0.4403-0.9526	0.5	0.714	0.333	0.833
MLP	Train	0.833	0.82	0.6535-0.9858	0.727	0.871	0.667	0.9
	Test	0.722	0.429	0.0520-0.8052	0	0.929	0	0.765

NPV: Negative predictive value; PPV: Positive predictive value; CI: Confidence interval; AUC: Area under the curve; LR: Logistic regression; KNN: K-nearest neighbor classification; RF: Random forest; XGBoost: EXtreme gradient boosting; MLP: Multilayer perceptron; SVM: Support vector machine.