Zhao ZC, Liu JX, Sun LL. Preoperative perineural invasion in rectal cancer based on deep learning radiomics stacking nomogram: A retrospective study. Artif Intell Med Imaging 2024; 5(1): 93993 [DOI: 10.35711/aimi.v5.i1.93993]
Corresponding Author of This Article
Jia-Xuan Liu, MD, Doctor, Department of Interventional Radiology, The First Affiliated Hospital of China Medical University, Heping District, Shenyang 110000, Liaoning Province, China. 3304352470@qq.com
Research Domain of This Article
Oncology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Artif Intell Med Imaging. Sep 28, 2024; 5(1): 93993 Published online Sep 28, 2024. doi: 10.35711/aimi.v5.i1.93993
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)
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
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
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
-
-
-
-
-
-
-
-
Citation: Zhao ZC, Liu JX, Sun LL. Preoperative perineural invasion in rectal cancer based on deep learning radiomics stacking nomogram: A retrospective study. Artif Intell Med Imaging 2024; 5(1): 93993