Copyright
©The Author(s) 2024.
World J Gastrointest Oncol. Mar 15, 2024; 16(3): 819-832
Published online Mar 15, 2024. doi: 10.4251/wjgo.v16.i3.819
Published online Mar 15, 2024. doi: 10.4251/wjgo.v16.i3.819
Variable | Training cohort | P value | Validation cohort | P value | ||
Poor | Well/moderate | Poor | Well/moderate | |||
Age (yr) | 59.41 ± 11.26 | 63.38 ± 11.07 | 0.072 | 58.12 ± 9.63 | 61.95 ± 11.79 | 0.148 |
CEA (ng/mL) | 16.65 ± 30.56 | 20.94 ± 61.37 | 0.319 | 24.13 ± 51.84 | 20.94 ± 75.57 | 0.446 |
CA199 (U/mL) | 55.95 ± 101.64 | 41.13 ± 115.48 | 0.174 | 28.74 ± 36.85 | 26.32 ± 58.41 | 0.349 |
Size (cm) | 4.26 ± 1.90 | 4.65 ± 1.59 | 0.252 | 4.24 ± 2.23 | 4.88 ± 1.85 | 0.067 |
Gender | 0.963 | 0.777 | ||||
Male | 18 (56.25) | 110 (58.51) | 9 (52.94) | 47 (60.26) | ||
Female | 14 (43.75) | 78 (41.49) | 8 (47.06) | 31 (39.74) | ||
Location | 0.24 | 0.947 | ||||
Left | 11 (34.38) | 43 (22.87) | 4 (23.53) | 15 (19.23) | ||
Right | 21 (65.62) | 145 (77.13) | 13 (76.47) | 63 (80.77) | ||
T stage | 0.496 | 0.343 | ||||
T1 | Null | 4 (2.13) | Null | 2 (2.56) | ||
T2 | 4 (12.50) | 22 (11.70) | 1 (5.88) | 8 (10.26) | ||
T3 | 22 (68.75) | 142 (75.53) | 11 (64.71) | 58 (74.36) | ||
T4 | 6 (18.75) | 20 (10.64) | 5 (29.41) | 10 (12.82) | ||
N stage | < 0.001 | 0.047 | ||||
N0 | 9 (28.12) | 96 (51.06) | 6 (35.29) | 43 (55.13) | ||
N1 | 6 (18.75) | 56 (29.79) | 5 (29.41) | 26 (33.33) | ||
N2 | 17 (53.12) | 36 (19.15) | 6 (35.29) | 9 (11.54) | ||
Circumference | 0.021 | 0.702 | ||||
≤ 1/2 | 6 (18.75) | 79 (42.02) | 8 (47.06) | 30 (38.46) | ||
> 1/2 | 26 (81.25) | 109 (57.98) | 9 (52.94) | 48 (61.54) | ||
Neural invasion | 0.033 | 0.81 | ||||
Absent | 12 (37.50) | 112 (59.57) | 8 (47.06) | 42 (53.85) | ||
Present | 20 (62.50) | 76 (40.43) | 9 (52.94) | 36 (46.15) | ||
Vascular invasion | 0.014 | 0.177 | ||||
Absent | 11 (34.38) | 112 (59.57) | 8 (47.06) | 53 (67.95) | ||
Present | 21 (65.62) | 76 (40.43) | 9 (52.94) | 25 (32.05) |
- Citation: Zheng HD, Huang QY, Huang QM, Ke XT, Ye K, Lin S, Xu JH. T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma. World J Gastrointest Oncol 2024; 16(3): 819-832
- URL: https://www.wjgnet.com/1948-5204/full/v16/i3/819.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v16.i3.819