## 4 Discussion
Further research is still necessary for understanding how to correlate mass spectral profiling with cancer development to enable better stage classification or establishing a correlation with cancer aggressiveness. The application of MALDI-ToF mass spectrometry for ovarian cancer detection on blood serum samples has previously been attempted, but the results have been poor and with experimental bias [16; 27]. In a recent systemic review and meta-analysis of 18 studies evaluating the accuracy of MALDI-ToF MS for ovarian cancer, the reported overall sensitivity was 77% (95% CI: 73-80%) and specificity was 72% (95% CI: 70-74%) [28]. In a more recent study, [PERSON] et al. followed a traditional proteomic approach for multiple biomarker detection using high-performance liquid chromatography (HPLC) applying conventional statistical models [6; 16; 29; 30]. This study reported only a 71% sensitivity with a 68% specificity. For classification purposes, we followed quite a different approach. Instead, we applied mass spectral pattern recognition and used novel machine learning algorithms to optimize predictive models [17; 31; 32]. The substantial improvement in performance demonstrates a methodological breakthrough, which launches MALDI-ToF mass spectrometry as a highly sensitive technique for ovarian cancer diagnostics. The reduced cost of running MALDI-ToF-based technology is a huge advantage for the implementation of our proposed solution for ovarian cancer screening programs. This advantage is mainly due to the reduced costs of reagents and the possibility of running multiple samples in one single run [11; 14; 19]. In our approach we have not used HPLC for biomarker resolution prior to mass signature detection in a mass analyzer, as is traditionally employed in most mass spectral techniques in proteomics and metabolomics [6; 29; 30]. Thus, this 'dilute and shoot' direct mass analysis enables further economization of operational costs, which are otherwise considerable from reagents and additional equipment. Indeed, the total estimated costs per test using our approach would be five to ten GBP, given a sample throughput of 50,000 a year over three years. It would approximately halve the cost when compared to the CA-125 screening test [7; 33]. According to recent analysis, the major benefit to health care systems for each person's case of early versus late stage detection of ovarian cancer is the saving of approximately 90 thousand GBP per year [33]. Thus, the impact of implementing MALDI-ToF technology with our approach for screening would definitely improve current screening programs in terms of cost-effectiveness and long-term health economics. This is not the case for longitudinal follow-up monitoring models with additional biomarkers or circBNC2-based tests, since these techniques require the purchase of more expensive reagents [11; 14]. However, there is still the need for governmental or private investment for implementing MALDI-ToF technology into clinical laboratories as each piece of equipment costs hundreds of thousands of GBP [11; 14]. However, it can be argued that this investment is absolutely necessary, and the savings will pay off in the short-term, given that the hardware of this technology is used in other diagnostics such as microbiology and is being applied in the diagnosis of multiple other diseases [14]. Future implementation of MALDI-ToF-basedtechnology would enable government support for a broad number of screening programs, in particular in poorer countries [4, 5, 14]. Another advantage of MALDI-ToF mass spectrometry for disease screening purposes is its capacity for generating multiple spectra in the scale of minutes [17, 19]. We have further optimized and automatized the testing using bioinformatics, which rendered multiple results at a rate of a few seconds per sample.