From: Machine learning models in evaluating the malignancy risk of ovarian tumors: a comparative study
Characteristic | Benign (n = 1196) | Malignant (n = 359) | P value |
---|---|---|---|
Age (years) | 41.0 (32.0–55.0) | 54.0 (43.0–64.0) | < 0.001 |
Menopausal Status | Pre/Post (845/351) | Pre/Post (159/200) | < 0.001 |
CA125 (U/mL) | 17.6 (10.1–40.0) | 122.2 (23.4-791.5) | < 0.001 |
Maximum lesion diameter (mm)* | 55.0 (39.0–76.0) | 74.0 (46.0-115.0) | < 0.001 |
Solid Component | |||
No. of solid components | 124 (10.4) | 165 (46.0) | < 0.001 |
Maximum largest solid component diameter (mm)* | 24.0 (12.0–39.0) | 50.0 (33.0–78.0) | < 0.001 |
Color Doppler score | |||
No flow, score 1 | 725 (60.6) | 38 (10.6) | |
Minimal flow, score 2 | 322 (26.9) | 60 (16.7) | |
Moderate flow, score 3 | 79 (6.6) | 57 (15.9) | |
Very strong flow, score 4 | 70 (5.9) | 204 (56.8) | |
External Contour | |||
Regular | 187 (59.7) | 134 (41.9) | < 0.001 |
Irregular | 126 (40.3) | 186 (58.1) | < 0.001 |
Internal Wall | |||
Smooth | 546 (50.9) | 17 (8.8) | < 0.001 |
Irregular | 526 (49.1) | 177 (91.2) | < 0.001 |
Ascites | 12 (1.0) | 96 (26.7) | < 0.001 |
Pelvic Nodules | 16 (1.3) | 78 (21.7) | < 0.001 |