# Why are Kaplan-Meier curves crossing when Cox PH assumption is not violated (Global Shoenfeld non-significant)?

I am not sure how to interpret the results given from the Cox PH model and the Kaplan-Meier curves. The analyses are conducted in R.

In an observational study, 172 patients are categorized based on tumor profileration status (Ki67): 0-2%, 3-4%, 5-10% and >10% (an index of tumor activity).

I am investigating the correlation between Ki67-status and the event of tumor recurrence.

 library(survival)  library(survminer)

Call:
coxph(formula = Surv(time.to.recurrence, event.recurrence) ~ 1 +
factor(ki67), data=p)

n= 172, number of events= 30
(4 observations deleted due to missingness)

coef exp(coef) se(coef)     z Pr(>|z|)
factor(p$$ki67)1 0.1109 1.1172 0.5781 0.192 0.84793 factor(p$$ki67)2 0.7136    2.0412   0.6065 1.176  0.23942
factor(p$ki67)3 1.8830 6.5731 0.6774 2.780 0.00544 **  I want to test if the proportional hazard assumption is violated:  rho chisq p factor(p$$ki67)1 0.064 0.123 0.726 factor(p$$ki67)2 -0.151 0.702 0.402 factor(p$ki67)3 -0.222 1.450 0.229
GLOBAL              NA 4.686 0.196


By using ggcoxzph(), I have the following print: Interpretation

1) Each covariate is non-significant correlated to time - thus, time independent supporting that the proportional hazard assumption is valid and the model is fit for use

2) The graphic visualizes a non-random pattern further supporting the PH assumption

The Kaplan-Meier analysis was subsequently conducted

Call: survfit(formula = Surv(time.to.recurrence, event.recurrence) ~ 1 +
factor(ki67), data = p, conf.type = "log")

4 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
factor(p$$ki67)=0 30 4 NA NA NA factor(p$$ki67)=1 86     12     NA     124      NA
factor(p$$ki67)=2 44 9 NA 118 NA factor(p$$ki67)=3 12      5     56       7      NA


The Kaplan-Meier curves were then computed: 1) How can this graph be interpreted? Intuitively, that curves should not be crossed if the PH assumption is valid?