Table 3. Survival analysis considering key clinical and pathological features.
Clinical features | Progression-free survival | Overall survival | ||||
---|---|---|---|---|---|---|
Number of recurrence or disease progression/total | HR (95% CI) | p value | Number of deaths/total | HR (95% CI) | p value | |
FIGO staging | ||||||
FIGO I+II | 6 / 29 | Reference | 7/29 | Reference | ||
FIGO III+IV | 52 / 77 | 4.87 (2.08–11.38) | <0.001 | 50/77 | 3.48 (1.57–7.71) | 0.002 |
Post-surgery residual disease | ||||||
No | 20 / 57 | Reference | 20/57 | Reference | ||
Yes | 38 / 49 | 3.39 (1.96–5.88) | <0.001 | 37/49 | 2.58 (1.49–4.45) | 0.001 |
Histological grade | ||||||
Low histological grade | 5 / 21 | Reference | 5/21 | Reference | ||
High histological grade | 53 / 85 | 3.62 (1.44–9.09) | 0.006 | 52/85 | 2.95 (1.17–7.41) | 0.02 |
WT1 expression | ||||||
Negative (complete absence to <1%) | 24 / 42 | Reference | 27/42 | Reference | ||
Positive (≥1%) | 33 / 63 | 1.19 (0.70–2.01) | 0.51 | 29/63 | 1.45 (0.86–2.46) | 0.16 |
IHC p53/p16 index | ||||||
Low-grade pattern (p53 staining in ≥1% and <70% and/or p53 complete absence + p16 <90%) | 10/29 | Reference | 9/29 | Reference | ||
High-grade pattern (p53 ≥70% or p53 complete absence + p16 ≥90%) | 48/76 | 2.19 (1.10–4.34) | 0.02 | 47/76 | 1.99 (0.98–4.08) | 0.05 |
FIGO: The International Federation of Gynecology and Obstetrics; PFS: progression-free survival; OS: overall survival; CI: Confidence interval; HR: Hazard ratio; statistically significant differences are indicated in bold. In the multivariate analysis, FIGO staging and histological grade were significantly associated with PFS; FIGO III + IV had an HR = 2.87 (95% CI: 1.15–7.18) and presence of post-surgery residual disease had an HR = 2.04 (1.12–3.71) and were more likely to progress compared with FIGO I + II and absence of post-surgery residual disease. There was a trend association between FIGO staging (HR = 2.28; 95% CI: 0.96–5.39; p = 0.06) and post-surgery residual disease (HR = 1.73; 95% CI: 0.96–3.13; p = 0.06) with OS in a multivariate model.