Table 4 Risk values for different factors in univariate and multivariate analyses.
From: Machine learning for grading prediction and survival analysis in high grade glioma
Feature | HR (univariable) | HR (multivariable) | HR (final) |
|---|---|---|---|
Minimum | 0.80(0.60–1.08, P =0.142) | ||
Skewness | 1.21(0.99–1.47, P = 0.066) | ||
Idn | 0.83(0.69-1.00, P = 0.054) | ||
Imc1 | 1.49(1.16–1.91, P = 0.002) | 1.33(0.98–1.79, P = 0.066) | 1.37(1.07–1.74, P = 0.012) |
Inverse Variance | 0.94(0.77–1.14, P = 0.519) | ||
MCC | 0.77(0.63–0.95, P = 0.001) | 0.96(0.74–1.25, P = 0.761) | |
Large Area Low Gray Level Emphasis | 0.67(0.39–1.15, P = 0.144) | ||
Size Zone Nonuniformity | 0.97(0.80–1.17, P = 0.728) | ||
Joint Energy | 0.91(0.73–1.13, P = 0.389) | ||
IDH1 | |||
Wild | |||
Mutant | 0.39(0.23–0.67, P < 0.001) | 0.55(0.30-1.00, P = 0.050) | 0.54(0.30–0.97, P = 0.039) |
MGMT | |||
Negative | |||
Positive | 1.56(0.96–2.54, P = 0.071) | ||
Ki_67 | |||
≤ 20 | |||
> 20 | 1.76(1.15–2.68, P = 0.009) | 1.56(1.00-2.42, P = 0.050) | 1.54(0.99–2.40, P = 0.053) |
Sex | |||
Female | |||
Male | 1.21(0.82–1.79, P = 0.341) | ||
Operate | |||
Part | |||
All | 0.66(0.45–0.98, P = 0.038) | 0.70(0.46–1.06, P = 0.088) | 0.69(0.46–1.03, P = 0.068) |
Grade | |||
III | |||
IV | 2.73(1.59–4.68, P < 0.001) | 1.93(1.07–3.48, P = 0.029) | 1.91(1.06–3.45, P = 0.030) |
KPS | |||
≤ 70 | |||
> 70 | 0.41(0.26–0.64, P < 0.001) | 0.38(0.24–0.60, P < 0.001) | 0.38(0.24–0.61, P < 0.001) |
