Computed tomography-based deep learning radiomics model for preoperative prediction of tumor immune microenvironment in colorectal cancer

Table 1 Clinical information of patients in the training and validation sets

Variable	Training set (n = 220)	Validation set (n = 95)	t-test/Z/χ2 value	P value
Sex			1.9223	0.166
F	90	31
M	130	64
Age			2.9513	0.229
≤ 50 years	24	7
51-65 years	109	41
> 65 years	87	47
pT stage			5.8273	0.120
T1	5	1
T2	41	29
T3	150	55
T4	24	10
pN stage			2.9333	0.231
N0	62	36
N1	71	26
N2	87	33
TP	65.14 (61.60, 69.66)	65.30 (61.60)	-0.3752	0.707
ALB	38.55 (35.95, 41.65)	38.40 (36.02, 40.97)	-0.6092	0.543
ALP	76.00 (61.27, 89.00)	76.00 (65.20, 88.00)	-0.7362	0.462
UREA	5.33 (4.46, 6.43)	5.48 (4.35, 6.46)	-0.4562	0.649
CREA	62.50 (53.53, 71.03)	64.40 (56.00, 72.50)	-1.2812	0.200
UA	269.73 (229.00, 331.75)	265.00 (221.00, 314.00)	-0.8512	0.395
TC, mean ± SD	4.11 ± 2.47	7.22 ± 31.31	-1.4691	0.143
TG	1.18 (0.93, 1.53)	1.25 (0.92, 1.60)	-0.6002	0.549
HDL-C	0.95 (0.83, 1.11)	0.97 (0.79, 1.15)	-0.2382	0.812
LDL-C	2.32 (1.90, 2.82)	2.41 (1.77, 2.94)	-0.0422	0.967
FBG	2.96 (2.16, 3.57)	3.02 (2.52, 3.71)	-0.9642	0.335
D-dimer	2.02 (0.75, 3.27)	2.13 (0.93, 3.23)	-0.5052	0.614
WBC	5.75 (4.70, 7.10)	5.70 (4.70, 7.00)	-0.2182	0.828
NEUT	3.26 (2.49, 4.48)	3.29 (2.54, 4.59)	-0.0042	0.997
Lym	1.67 (1.14, 2.11)	1.59 (1.16, 2.07)	-0.4182	0.676
M	0.46 (0.34, 0.56)	0.45 (0.37, 0.54)	-0.1582	0.875
E	0.10 (0.06, 0.17)	0.11 (0.06, 0.18)	-0.0202	0.984
B	0.03 (0.02, 0.04)	0.02 (0.01, 0.04)	-1.3532	0.176
RBC	4.38 (3.96, 4.80)	4.46 (4.06, 4.81)	-0.4462	0.656
HB	132.00 (117.25, 147.00)	134.00 (121.00, 146.00)	-0.7342	0.463
PLT	208.00 (167.25, 251.00)	204.00 (163.00, 248.00)	-0.2942	0.769
BMI	23.05 (20.82, 24.68)	22.61 (20.81, 24.61)	-0.5722	0.568

¹Statistical analysis performed using t-test.

²Statistical analysis performed using Mann-Whitney U test.

³Statistical analysis performed using χ² test.

F: Female; M: Male; TP: Total protein; ALB: Albumin; ALP: Alkaline phosphatase; UREA: Urea; CREA: Creatinine; UA: Uric acid; TC: Total cholesterol determination; TG: Triglycerides; HDL-C: High density lipoprotein-cholesterol; LDL-C: Low density lipoprotein-cholesterol; FBG: Fibrinogen; WBC: White blood cell count; NEUT: Neutrophils; Lym: Lymphocytes; M: Monocyte; E: Eosinophils; B: Basophils; RBC: Red blood cells; HB: Hemoglobin; PLT: Platelets; BMI: Body mass index.

Table 2 Information of the colorectal cancer immune score evaluation model

Number	Model	ACC	AUC	95%CI	Sensitivity	Specificity	PPV	NPV	Precision	Recall	F1	Threshold	Cohort
0	DenseNet-121	0.741	0.797	0.7383-0.8556	0.707	0.769	0.714	0.762	0.714	0.707	0.711	0.520	Train
1	DenseNet-121	0.800	0.759	0.6502-0.8676	0.705	0.882	0.838	0.776	0.838	0.705	0.765	0.407	Test
2	DenseNet-169	0.709	0.780	0.7185-0.8406	0.768	0.661	0.650	0.777	0.650	0.768	0.704	0.385	Train
3	DenseNet-169	0.768	0.772	0.6741-0.8696	0.682	0.834	0.789	0.754	0.789	0.682	0.732	0.541	Test
4	DenseNet-201	0.718	0.765	0.7018-0.8274	0.495	0.901	0.803	0.686	0.803	0.495	0.612	0.591	Train
5	DenseNet-201	0.768	0.737	0.6305-0.8432	0.636	0.882	0.824	0.738	0.824	0.636	0.718	0.586	Test
6	ResNet-101	0.786	0.852	0.8032-0.9011	0.859	0.727	0.720	0.863	0.720	0.859	0.783	0.432	Train
7	ResNet-101	0.737	0.752	0.6503-0.8541	0.636	0.824	0.757	0.724	0.757	0.636	0.691	0.446	Test
8	ResNet-152	0.732	0.816	0.7603-0.8718	0.869	0.620	0.652	0852	0.652	0.869	0.745	0.336	Train
9	ResNet-152	0.737	0.736	0.6272-0.8438	0.614	0.843	0.771	0.717	0.771	0.614	0.684	0.501	Test
10	ResNet-34	0.782	0.860	0.8120-0.9072	0.808	0.760	0.734	0.829	0.734	0.808	0.769	0.444	Train
11	ResNet-34	0.747	0.741	0.6344-0.8474	0.705	0.784	0.738	0.755	0.738	0.705	0.721	0.498	Test
12	ResNet-50	0.795	0.863	0.8144-0.9120	0.828	0.769	0.745	0.845	0.745	0.828	0.785	0.464	Train
13	ResNet-50	0.747	0.754	0.6491-0.8598	0.614	0.863	0.794	0.721	0.794	0.614	0.692	0.501	Test

F1-score = 2 × (precision × recall)/(precision + recall).

ACC: Accuracy; AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value; CI: Confidence interval.