Polytomous diagnosis of ovarian tumors as benign, borderline, primary invasive or metastatic: development and validation of standard and kernel-based risk prediction models

Table 4 Validation results using pairwise c-indexes

Model	Ben vs Bord	Ben vs PrInv	Ben vs Meta	Bord vs PrInv	Bord vs Meta	PrInv vs Meta
Group 1: logistic regression models
Internal: LR-PC	.82	.95	.93	.88	.96	.73
Temporal: LR-PC	.88	.95	.93	.81	.83	.51
External: LR-PC	.88	.96	.93	.81	.89	.56
Group 2: kernel-based and logistic regression models (based on R1U variable selection)*
Internal: LR-PC2	.86	.94	.92	.88	.96	.73
Temporal: LR-PC2	.90	.94	.92	.81	.83	.51
External: LR-PC2	.91	.95	.93	.81	.89	.56

Models are ranked by the value of the polytomous c-index. Ben: benign; PrInv: primary invasive; Bord: borderline; Meta: metastatic; CI: confidence interval. 95% CIs are computed using the bias-corrected bootstrap method using 1000 bootstrap samples.
* R1U variable selection: variable selection within the framework of least squares support vector machines that is based on rank 1 updates of the kernel matrix [27].

ISSN: 1471-2288