## 3 Results  
Mass spectra of serum samples, from women with and without ovarian cancer, were generated using two protocols for MALDI-ToF MS for blood serum. For one, we used a CHCA matrix and for the other an SA matrix. Protocols were optimized for taking an average time of 80 minutes to generate a batch of 48 mass spectra (100 s per sample). We analyzed the mass spectral data generated from these protocols using our bioinformatics data pipeline. Most of the data generated through these experimental protocols were of high quality, 96% using CHCA matrix and 99% using SA matrix. The average mass spectra from patients with ovarian cancer at early and late stages were compared (Figure 1). These spectra are characterized by having more pronounced peaks located between 200 \(m/z\) and 700 \(m/z\) using CHCA matrix (Figure 1A) and between 2000 \(m/z\) and 5500 \(m/z\) using SA matrix (Figure 1B). Multiple spectral changes between cancer stages and controls can also be observed across the entire mass spectrum, including mass regions of lower intensities (box I and II in Figure 1). Proportionally, the averaged mass spectrum of early and late stages of ovarian cancer has substantial variation in comparison to women without cancer in particular mass regions across the entire mass spectrum (Figure 2). Some regions show distinct variations among stages, which may potentially be used for classification purposes. However, there is a substantial degree of complexity of these variations across the spectrum and overlap between stages. In addition, one order of magnitude of intensity variation was found across the data set. This further complicates a direct application of mass spectral variations for classification purposes. To simplify the mass spectral data for developing classification models, we focus on analyzing well-defined peaks, which were used as signatures for the pattern-based scoring function for the likelihood of having a particular stage of ovarian cancer. First, we optimized the extraction of well-defined peaks on the data by increasing the number of detected peaks while reducing the variability of total peaks detected (Figure 3). We obtained an optimal increase in well-defined peak distribution ranging from 26 to 57 detected peaks in each sample for CHCA matrix mass spectral data (Figure 3A). For SA matrix mass spectral data, the optimization reduced the number of peaks for a final distribution rangingfrom 12 to 113 detected peaks in each sample (Figure 3B). Secondly, on this resulting data, we applied machine learning for training and validating predictive models based on mass spectral patterns, to optimize the binary classification of ovarian cancer detection on early stages (1 or 2) and late stages (3 and 4). We obtained six classification models, one for each type of matrix explored and specifically for detecting ovarian cancer at early stages (stage 1 or 2), stage 3, and stage 4. The performance evolution of these models is presented in Figure 4. Our machine learning approach enables the evolution of predictive models with good to excellent classification power (98% \(<\) AUC \(>\) 70%) for ovarian cancer stage detection [25,26]. The maximum obtained sensitivities of these models were between 92% and 100% using CHCA and 100% for SA matrix. This indicates high potential for detecting ovarian cancer in early and late stages. However, models generated using SA matrix data show a much higher specificity compared with the ones obtained using CHCA matrix, having a much lower false positive rate. Among SA-generated models, we obtained final classification specificities of 92% for early stages, 96% for stage 3, and 82% for stage 4. Figure 1: Averaged mass spectra of serum samples from women diagnosed with ovarian cancer at different stages and controls. (**A**) Spectrum generated with CHCA matrix. (**B**) Spectrum generated with SA matrix. Red indicates averaged mass spectra of women with ovarian cancer at stage 4, blue at stage 3, and green at early stages (stage 1 and 2).