## 4 Discussion  
Failure to detect ovarian cancer at an early stage has motivated many researchers to attempt improving current diagnostic tests or develop novel ones [2; 3; 4]. In this study, we developed and optimized a MALDI-ToF mass spectral pattern-based bioinformatics pipeline, which enabled the detection of ovarian cancer in blood serum with high performance. In comparison with the reported sensitivities for the CA-125 test, used worldwide in screening programs, our approach provides a substantial gain in sensitivity and specificity for early- and late-stage disease detection [3; 5]. These gains were almost of 50% in sensitivity for early-stage detection, suggesting that implementing our MALDI-ToF approach in screening programs would have a huge impact in preventing thousands of misdetected cases every year [2; 3]. Other recently developed solutions to the problem are based on longitudinal biomarker level monitoring, statistical models with additional bio-markers and microRNA quantification. In particular, measurement of plasma circBNC2 has enhanced the performance of CA-125 surveillance/monitoring boosting ovarian cancer detection [5; 8; 9]. For early-stage disease, these approaches reported optimal sensitivities ranging from 82% to 92% with specificities between 86% and 95%. For later-stage ovarian cancers, longitudinal models and circBNC2-based tests report sensitivities of 90% to 100% with specificities of 84% to 95%. In comparison, our MALDI-ToF-based solution demonstrated here has higher sensitivity (95% to 99%) with equally higher specificities (97%), representing a significant gain. Our best solution outcompetes the performance of the recent approach of combin  
Figure 7: Comparison of the predictive scores of controls and those diagnosed with distinct cancer stages using the SA-based stage-specific models. Scores were normalized to reflect the deviation from the cutoff value within the maximum score values detected (maxScore) with the mathematical expression (patient score \(-\) cutoff)/maxScore. Value of zero indicates that patients’ scores are equal to the model cutoff, positive values above cutoff, and negative values below cutoff. Values \(1\) and \(-1\) indicate that patient scores are the maximum score observable. Scores of patients and controls are shown as grouped heatmaps and the colors indicate the normalized scores. Red indicates the highest score and green the lowest score for predicting cancer using a stage-based model. The correlated probability of cancer based on scoring is indicated as an arrow next to the color gradient legend. Low probability starts from dark green and ends in high probability at dark red. ing multiple longitudinal risk models which, although increasing sensitivity, report only equivalent specificity for early-stage ovarian cancer [9]. This was possible due to the fine tuning of the MALDI-ToF MS data pattern recognition models towards improving the specificity, whilst keeping high sensitivity by removing particular signatures in the patterns that contributed to an increase in the number of false positives (Figures 4 and 5). Thus, our MALDI-ToF-based approach provides an optimal solution and framework for maximizing the gain of true positives while minimizing the false positives. Balancing these is useful for designing optimal ovarian cancer screening programs to prevent unnecessary, invasive investigations in false positive cases or delayed diagnosis in the cases of false negatives [2; 4; 5]. Interestingly, the models developed for a given stage have also scored positive for other stages and the combination of predicted outcomes using a decision rule further improved the cancer detection performance. This could be due to multiple intermediate cancer stages that share common mass spectral profiling or a continuous mass spectral profiling evolution that correlates with cancer development.