Table 3 The baseline characteristics of the training and validation sets used in the prognostic model.

From: Overall survival prediction models for gynecological endometrioid adenocarcinoma with squamous differentiation (GE-ASqD) using machine-learning algorithms

Characteristics

Training (N = 634) n (%)

Validation (N = 273) n (%)

P

Race

0.973

 Black

42 (6.6%)

17 (6.2%)

 

 Other race

61 (9.6%)

26 (9.5%)

 

 White

531 (83.8%)

230 (84.3%)

 

Age

0.384

 Elder

137 (22%)

52 (19%)

 

 Young

497 (78%)

221 (81%)

 

Sequence number

0.430

 1st of 2 or more primaries

89 (14%)

33 (12%)

 

 One primary only

545 (86%)

240 (88%)

 

Marital

0.050

 Married

322 (51%)

158 (58%)

 

 Other marital

312 (49%)

115 (42%)

 

Stage

0.420

 Distant

34 (5.4%)

13 (4.8%)

 

 Localized

454 (71.6%)

207 (75.8%)

 

 Regional

146 (23%)

53 (19.4%)

 

Surgery

0.885

 None

22 (3.5%)

10 (3.7%)

 

 Yes

612 (96.5%)

263 (96.3%)

 

Radiation

0.358

 No Radiation

467 (74%)

209 (77%)

 

 Radiation

167 (26%)

64 (23%)

 

Chemotherapy

0.811

 No/Unknown

549 (87%)

238 (87%)

 

 Yes

85 (13%)

35 (13%)

 

RN examined

0.615

 No examined

235 (37%)

106 (39%)

 

 Yes examined

399 (63%)

167 (61%)

 

T

0.301

 T1

488 (77%)

219 (80.2%)

 

 T2

69 (10.9%)

27 (9.9%)

 

 T3

73 (11.5%)

23 (8.4%)

 

 T4

4 (0.6%)

4 (1.5%)

 

N

0.113

 N0

566 (89%)

253 (92.7%)

 

 N1

68 (11%)

20 (7.3%)

 

M

0.508

 M0

602 (95%)

262 (96%)

 

 M1

32 (5.0%)

11 (4.0%)

 

Primary site

0.489

 Endometrium

608 (95.9%)

259 (94.9%)

 

 Ovary

26 (4.1%)

14 (5.1%)