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©The Author(s) 2023.
World J Gastroenterol. Apr 7, 2023; 29(13): 2001-2014
Published online Apr 7, 2023. doi: 10.3748/wjg.v29.i13.2001
Published online Apr 7, 2023. doi: 10.3748/wjg.v29.i13.2001
Table 1 Clinical and pathological features in the training and test sets
Characteristics | Training set (n = 162) | Test set (n = 70) | P value | ||||
nMTM-HCC (n = 118) | MTM-HCC (n = 44) | P value | nMTM-HCC (n = 51) | MTM-HCC (n = 19) | P value | ||
Age, mean ± SD | 60.6 ± 10.9 | 52.4 ± 9.9 | < 0.001 | 60.7 ± 10.4 | 54.7 ± 9.7 | 0.034 | 0.647 |
Sex (men, %) | 102 (86.4) | 42 (95.5) | 0.179 | 39 (76.5) | 18 (94.7) | 0.161 | 0.125 |
HBsAg (positive, %) | 91 (77.1) | 33 (75.0) | 0.777 | 38 (74.5) | 14 (73.7) | 0.944 | 0.712 |
AFP > 400 µg/L, n (%) | 18 (15.3) | 31 (70.5) | < 0.001 | 12 (23.5) | 9 (47.4) | 0.053 | 0.970 |
CEA (ug/L) | 2.6 (2.1) | 2.7 (1.9) | 0.719 | 2.2 (2.0) | 2.2 (1.2) | 0.543 | 0.332 |
CA19-9 (U/mL) | 15.0 (19.3) | 28.0 (25.4) | 0.021 | 16.7 (17.1) | 16.0 (34.4) | 0.687 | 0.770 |
PLT (× 109/L) | 135.0 (83.0) | 174.0 (88.5) | 0.027 | 135.0 (57.0) | 175.0 (127.0) | 0.001 | 0.970 |
TBIL (µmol/L) | 16.6 (10.8) | 20.7 (9.8) | 0.067 | 15.7 (10.3) | 14.4 (7.6) | 0.916 | 0.509 |
DBIL (µmol/L) | 3.8 (2.6) | 4.8 (2.9) | 0.060 | 3.2 (2.1) | 3.3 (2.7) | 0.620 | 0.419 |
IBIL (µmol/L) | 12.5 (7.0) | 15.0 (7.4) | 0.136 | 12.2 (7.8) | 10.9 (4.5) | 0.712 | 0.061 |
Albumin, mean ± SD | 37.8 ± 5.0 | 37.0 ± 5.8 | 0.387 | 37.8 ± 5.0 | 38.3 ± 5.1 | 0.715 | 0.647 |
AST (U/L) | 32.0 (23.5) | 43.0 (68.8) | 0.004 | 33.0 (29.0) | 43.0 (61.0) | 0.135 | 0.980 |
ALT (U/L) | 31.5 (34.0) | 46.0 (64.0) | 0.023 | 28.0 (35.0) | 57.0 (96.0) | 0.074 | 0.748 |
AST/ALT | 1.1 (0.6) | 1.1 (0.5) | 0.702 | 1.3 (0.5) | 1.1 (0.7) | 0.207 | 0.842 |
Edmondson-Steiner grade (III-IV, %) | 38 (32.2) | 18 (40.9) | 0.300 | 14 (27.5) | 10 (52.6) | 0.048 | 0.967 |
Microvascular invasion, n (%) | 56 (47.5) | 20 (45.5) | 0.820 | 18 (35.3) | 10 (52.6) | 0.188 | 0.331 |
Satellite nodules, n (%) | 9 (7.6) | 5 (11.4) | 0.452 | 1 (2.0) | 1 (5.3) | 0.461 | 0.110 |
Biliary invasion, n (%) | 4 (3.4) | 1 (2.3) | 0.715 | 2 (3.9) | 1 (5.3) | 0.805 | 0.646 |
Table 2 Radiological features of patients in the training and test sets
Radiological features | Training set (n = 162) | Test set (n = 70) | P value | ||||
nMTM-HCC (n = 118) | MTM-HCC (n = 44) | P value | nMTM-HCC (n = 51) | MTM-HCC (n = 19) | P value | ||
Liver cirrhosis (positive, %) | 69 (58.5) | 29 (65.9) | 0.389 | 34 (66.7) | 15 (78.9) | 0.319 | 0.168 |
Tumour size > 5 cm, n (%) | 29 (24.6) | 22 (50.0) | 0.002 | 12 (23.5) | 10 (52.6) | 0.020 | 0.994 |
Tumour shape (irregular, %) | 25 (21.2) | 17 (38.6) | 0.024 | 17 (33.3) | 7 (36.8) | 0.783 | 0.195 |
Intratumor fat, n (%) | 24 (20.3) | 10 (22.7) | 0.740 | 11 (21.6) | 1 (5.3) | 0.210 | 0.500 |
Intratumor necrosis, n (%) | 34 (28.8) | 23 (52.3) | 0.005 | 13 (25.5) | 8 (42.1) | 0.177 | 0.443 |
Intratumor hemorrhage, n (%) | 24 (20.3) | 17 (38.6) | 0.017 | 7 (13.7) | 8 (42.1) | 0.025 | 0.526 |
Enhancing capsule, n (%) | 85 (72.0) | 35 (79.5) | 0.332 | 39 (76.5) | 15 (78.9) | 0.826 | 0.620 |
Tumour-to-liver ADC ratio | 0.9 (0.2) | 0.8 (0.3) | 0.018 | 0.9 (0.1) | 0.8 (0.2) | 0.045 | 0.312 |
Table 3 Results of univariate and multivariate logistic regression analyses
Variables | Univariate logistic regression | Multivariate logistic regression | ||
OR (95%CI) | P value | OR (95%CI) | P value | |
Clinical features | ||||
Age | 0.930 (0.896, 0.964) | < 0.001 | 0.956 (0.918, 0.997) | 0.034 |
AFP > 400 µg/L | 13.248 (5.839, 30.058) | < 0.001 | 10.066 (4.304, 23.541) | < 0.001 |
PLT | 1.006 (1.002, 1.011) | 0.009 | NA | NA |
Radiological features | ||||
Tumour size > 5 cm | 3.069 (1.487, 6.333) | 0.002 | 3.316 (1.579, 6.962) | 0.002 |
Tumour shape | 2.342 (1.106, 4.961) | 0.026 | NA | NA |
Intratumor necrosis | 2.706 (1.326, 5.521) | 0.006 | NA | NA |
Intratumor hemorrhage | 2.466 (1.160, 5.244) | 0.019 | NA | NA |
Tumour-to-liver ADC ratio | 0.183 (0.035, 0.972) | 0.046 | 0.156 (0.027, 0.894) | 0.037 |
Radiomics | ||||
Rad-score | 2.718 (1.809, 4.084) | < 0.001 | 2.923 (1.740, 4.911) | < 0.001 |
Table 4 Predictive performance of different models in training and test sets
Models | Training set | Test set | ||||||
AUC (95%CI) | Accuracy | Sensitivity | Specificity | AUC (95%CI) | Accuracy | Sensitivity | Specificity | |
Clinical | 0.836 (0.773-0.888) | 0.802 | 0.750 | 0.822 | 0.701 (0.575-0.814) | 0.671 | 0.579 | 0.706 |
Radiological | 0.688 (0.604-0.769) | 0.685 | 0.636 | 0.703 | 0.723 (0.610-0.829) | 0.700 | 0.632 | 0.725 |
Radiomics | 0.766 (0.692-0.836) | 0.772 | 0.568 | 0.847 | 0.739 (0.634-0.837) | 0.743 | 0.579 | 0.804 |
Clinical-radiomics | 0.888 (0.835-0.934) | 0.802 | 0.909 | 0.763 | 0.793 (0.682-0.893) | 0.686 | 0.737 | 0.667 |
Radiological-radiomics | 0.796 (0.725-0.858) | 0.772 | 0.636 | 0.822 | 0.764 (0.661-0.859) | 0.729 | 0.632 | 0.765 |
Combined | 0.896 (0.847-0.939) | 0.796 | 0.932 | 0.746 | 0.805 (0.704, 0.895) | 0.700 | 0.895 | 0.628 |
- Citation: Zhang Y, He D, Liu J, Wei YG, Shi LL. Preoperative prediction of macrotrabecular-massive hepatocellular carcinoma through dynamic contrast-enhanced magnetic resonance imaging-based radiomics. World J Gastroenterol 2023; 29(13): 2001-2014
- URL: https://www.wjgnet.com/1007-9327/full/v29/i13/2001.htm
- DOI: https://dx.doi.org/10.3748/wjg.v29.i13.2001