Preoperative perineural invasion in rectal cancer based on deep learning radiomics stacking nomogram: A retrospective study

Table 1 Statistical analysis results of clinical characteristics, n (%)

Characteristics	Training set (n = 242)		P value	Test set (n = 61)		P value
	PNI (n = 148)	PNI+ (n = 94)		PNI (n = 40)	PNI+ (n = 21)
Age, (mean ± SD) (years)	67.01 ± 10.57	64.26 ± 12.06	0.063	67.33 ± 8.18	63.104 ± 8.66	0.065
Gender			0.248			0.86
Male	85 (58.2)	54 (39.1)		20 (64.5)	11 (35.5)
Female	63 (65.6)	33 (34.4)		20 (66.7)	10 (33.3)
Smoking			0.882			0.396
No	101 (60.8)	65 (39.2)		29 (69.0)	13 (31.0)
Yes	47 (51.8)	29 (38.2)		11 (57.9)	8 (42.1)
HGB (g/L)	126.98 ± 20.67	130.69 ± 20.00	0.071	128.881 ± 14.16	128.67 ± 22.28	0.965
RBC (1012/L)	4.28 ± 0.62	4.40 ± 0.44	0.126	4.40 ± 0.47	4.28 ± 0.48	0.339
WBC (109/L)	6.55 ± 1.79	6.84 ± 2.19	0.265	6.18 ± 1.68	6.54 ± 1.67	0.391
PLT (109/L)	229.14 ± 77.31	240.92 ± 76.73	0.248	231.05 ± 70.18	231.62 ± 50.56	0.974
Lymphocyte(109/L)	1.61 ± 0.59	1.63 ± 0.68	0.808	1.59 ± 0.68	1.71 ± 0.95	0.567
Monocyte(109/L)	0.46 ± 0.23	0.47 ± 017	0.667	0.40 ± 0.14	0.70 ± 1.02	0.1
Neutrophil(109/L)	4.27 ± 1.55	4.54 ± 1.94	0.243	3.96 ± 1.51	4.47 ± 1.70	0.228
TG	1.47 ± 1.11	1.31 ± 0.60	0.18	1.43 ± 0.72	1.59 ± 0.98	0.502
Cholesterol	4.58 ± 0.88	4.72 ± 0.98	0.232	4.82 ± 0.89	4.91 ± 1.08	0.707
HDL	1.12 ± 0.28	1.19 ± 0.32	0.088	1.42 ± 1.51	1.13 ± 0.24	0.392
LDL	2.79 ± 0.69	2.93 ± 0.85	0.164	2.96 ± 0.84	3.18 ± 1.09	0.392
AproA	1.24 ± 0.19	1.26 ± 0.20	0.406	1.29 ± 0.17	1.22 ± 0.15	0.126
AproB	0.89 ± 0.19	0.90 ± 0.22	0.795	0.94 ± 0.18	0.89 ± 0.19	0.279
CEA (≥ 5 ng/mL)			0.016			0.173
No	94 (67.6)	45 (32.4)		28 (71.8)	11 (28.2)
Yes	54 (52.4)	49 (47.6)		12 (54.5)	10 (45.5)
CA19-9 (≥ 37 U/mL)			0.003			0.052
No	136 (64.8)	74 (35.2)		37 (71.2)	15 (28.8)
Yes	12 (37.5)	20 (62.5)		3 (33.3)	6 (66.7)
CT T stage			0.000			0.006
1/2	29 (85.3)	5 (14.7)		16 (88.6)	2 (11.1)
3	73 (66.4)	37 (33.6)		17 (68.0)	8 (32.0)
4	46 (46.9)	52 (53.1)		7 (38.9)	11 (61.1)

CEA: Carcinoembryonic antigen; CA19-9: Carbohydrate antigen 19-9; P < 0.05.

Table 2 Performance of four machine learning classifiers (support vector machine, multi-layer perceptron, k-nearest neighbor and logistic regression)

Classifiers	Training set				Test set
	AUC	95%CI	Sensitivity	Specificity	AUC	95%CI	Sensitivity	Specificity
ASVM	0.904	0.865-0.943	0.840	0.885	0.890	0.794-0.987	0.857	0.850
AKNN	0.790	0.736-0.844	0.638	0.791	0.762	0.640-0.884	0.667	0.725
AMLP	0.821	0.769-0.873	0.840	0.649	0.814	0.691-0.937	0.810	0.750
ALR	0.788	0.731-0.846	0.723	0.730	0.750	0.607-0.893	0.714	0.750
VSVM	0.890	0.850-0.930	0.926	0.703	0.867	0.778-0.956	0.810	0.800
VKNN	0.834	0.783-0.884	0.702	0.838	0.790	0.676-0.904	0.667	0.800
VMLP	0.800	0.744-0.856	0.766	0.730	0.769	0.648-0.890	0.571	0.850
VLR	0.760	0.698-0.822	0.628	0.777	0.735	0.606-0.863	0.999	0.375

ASVM: Arterial support vector machine; AKNN: Arterial k-nearest neighbor; AMLP: Arterial multilayer perceptron; ALR: Arterial logistic regression; VSVM: Venous support vector machine; VKNN: Venous k-nearest neighbor; VMLP: Venous multilayer perceptron; VLR: Venous logistic regression.

Table 3 Prediction performance of four models (arterial support vector machine, venous support vector machine, CT-Tstage and Nomogram)

Model	Dataset	AUC	95%CI	Sensitivity	Specificity	Recall	Accuracy	Precision	F1-score
ASVM	Train	0.904	0.865-0.943	0.840	0.885	0.840	0.863	0.880	0.860
	Test	0.890	0.794-0.987	0.857	0.850	0.857	0.854	0.851	0.854
VSVM	Train	0.890	0.850-0.930	0.926	0.703	0.926	0.815	0.757	0.786
	Test	0.867	0.778-0.956	0.810	0.800	0.810	0.805	0.802	0.806
CT-Tstage	Train	0.647	0.583-0.710	0.553	0.689	0.553	0.621	0.640	0.593
	Test	0.730	0.607-0.854	0.525	0.825	0.525	0.675	0.750	0.618
Nomogram	Train	0.964	0.944-0.983	0.800	0.789	0.800	0.795	0.791	0.795
	Test	0.955	0.900-0.999	0.952	0.900	0.952	0.928	0.905	0.919

AUC: Area under curve; ASVM: Arterial support vector machine; VSVM: Venous support vector machine.

Table 4 Delong-test results of four models (Arterial support vector machine, venous support vector machine, CT-T stage and Nomogram)

Model	Training set				Test set
Delong-test	ASVM	VSVM	CT-T stage	Nomogram	ASVM	VSVM	CT-T stage	Nomogram
ASVM	-	0.6304	1.934e-11	0.000271	-	0.6997	0.0527	0.05137
VSVM	-	-	1.737e-10	2.15e-05	-	-	0.0692	0.03611
CT-Tstage	-	-	-	2.2e-16	-	-	-	0.000305
Nomogram	-	-	-	-	-	-	-	-

ASVM: Arterial support vector machine; VSVM: Venous support vector machine; P < 0.05.