Serum samples (33 healthy women, 34 ovarian cancer, 28 colorectal cancer, 34 syphilis patients and 136 patients with various benign gynecological diseases) were analyzed by MALDI-TOF MS peptide profiling and respective predictive models were generated by genetic and supervised neural network algorithms. Classification models for pathology versus healthy control showed up to 100% sensitivity and specificity for all target diseases. However, the specificity dropped to unsatisfactory 25–40% in case of target versus nontarget disease diagnostics. Expansion of the control group to an artificial “nominal control” group by adding profiles of benign gynecological diseases considerably improved specificity of the models distinguishing ovarian cancer from healthy control and benign gynecological diseases. The suggested version of MALDI-TOF MS profiling of sera could be applied to differentiate between cancers and benign neoplasms of the same localization which is a challenging task for classical methods. To increase the specificity of diagnostic methods based on peptidome analysis of blood samples, it is necessary to identify sets of concrete peptide structures which qualitatively or quantitatively differ among patients with different diseases. © 2016, Pleiades Publishing, Ltd.