Research Article
A Machine Learning Approach for the Association of ki-67 Scoring with Prognostic Factors
Table 1
Patient characteristics (discrete data are given as numbers, continuous as the mean ± standard deviation) (n = 223).
| Variable | | Ki-67 ≤ | Ki-67 > | p |
| Ki-67 Proliferation | | 74 (33.2) | 149 (66.8) | 0.001 | |
| Molecular Classification | | | | | | LA | 37 (50) | 1 (0.7)! | 0.001 | LB | 28 (37.8) | 112 (75.2) | Her-2 | 6 (8.2) | 27 (18.1) | TN | 3 (4) | 9 (6) |
| Lymphovascular Invasion | | | | | | - | 36 (48.6) | 32 (21.5) | 0.001 | + | 38 (51.4) | 117 (78.5) |
| Age | | | | | | Age <50 | 38 (51.4) | 80 (53.7) | 0.742 | Age >50 | 36 (48.6) | 69 (46.3) |
| Number of Metastatic Lymph Nodes | | | | | | 0-3 LNm | 27 (36.5) | 38 25.5) | 0.234 | 4-9 LNm | 8 (10.8) | 18 (12.1) | 10 LNm | 39 (52.7) | 93 (62.4) |
| Nuclear Grade | | | | | | Grade I | 26 (35.1) | 16 (10.7) | 0.001 | Grade II | 33 (44.6) | 82 (55) | Grade III | 15 (20.3) | 51 (34.2) |
| Tumor Size | | | | | | T1 | 32 (43.2) | 41 (27.5) | 0.01 | T2 | 38 (51.4) | 82 (55) | T3 and T4 | 4 (5.4) | 26 (17.4) |
| Body Mass Index | | | | | | 18.5-24.9 | 7 (9.5) | 18 (12.1) | 0,386 | 25-29.9 | 28 (37.8) | 43 (28.9) | 30+ | 39 (52.7) | 88 (59.1) |
| Surgical Type | | | | | | Mastectomy | 55 (74.3) | 77 (51.7) | 0,001 | Segmental Mastectomy | 19 (25.7) | 72 (48.3) |
| Histopathological Type | | | | | | IDC | 61 (82.4) | 131 (87.9) | 0,403 | ILC | 6 (8.1) | 6 (4) | Other | 7 (9.5) | 12 (8.1) |
| Perivascular Invasion | | | | | | - | 52 (70.3) | 36 (24.2) | 0.01 | + | 22 (29.7) | 113 (75.8) |
| Number of Lymph Nodes | | | | | | | 2.39 ± 4.62 | 3.74 ± 6.3 | 0.07 |
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: 6 mucinous carcinomas, 7 DCIS, 6 neuroendocrine carcinomas. !: “rfinput” imputation. Molecular classification: LA: luminal A, LB: luminal B, and TNBC: triple negative breast carcinoma; BMI: underweight <18.50, normal range 18.50-24.99, overweight ≥ 25.00, and obese ≥ 30.00. |