Abstract
IntroductionOvarian cancer is a major global concern. Due to late detection, it is one of the few malignancies for which the five-year survival rate continues to be low without appreciable improvement in recent decades. Screening methods are needed that non-invasively, without ionizing radiation, detect early-stage ovarian cancer with clear distinction from benign lesions. Magnetic resonance spectroscopy (MRS) could be a key contributor to early ovarian cancer detection, insofar as data analysis and interpretation after appropriate signal processing are implemented.MethodsThe derivative non-parametric and parametric fast Padé transform (dFPT) and the derivative fast Fourier transform (dFFT: optimized, unoptimized) are applied to proton MRS time signals encoded from the ovary. These include in vivo MRS for a borderline cyst, and in vitro MRS for serous cystic adenoma and serous cystic adenocarcinoma.ResultsOn a broad chemical shift axis (aliphatic and aromatic), over 300 thin, clearly-delineated peaks are baseline-resolved and displayed in a readily amenable form for clinical interpretation. This is from the in vivo encoding. Clearly quantifiable peaks include cancer biomarkers: total choline components (phosphocholine, glycerophosphocholine, free choline) and the lactate doublet, as well as other diagnostically-relevant metabolites in spectrally-crowded regions. Concordance among three algorithms (parametric and non-parametric dFPT as well as optimized dFFT) provides cross-validation, essential for clinical trustworthiness of derivative MRS.ConclusionWith these results that help benchmark derivative MRS for clinical applications in oncology, the time is deemed ripe to implement the stated advances. More effective early detection of ovarian cancer should be among the most urgent priorities for this upgrade.