Abstract DP-001: KEY ACHIEVEMENT OF THE FAST PADE TRANSFORM IN MAGNETIC RESONANCE SPECTROSCOPY FOR EARLY OVARIAN CANCER DIAGNOSTICS

Abstract PURPOSE: An excellent candidate for early ovarian cancer detection would be magnetic resonance spectroscopy (MRS), being non-invasive, ionizing-radiation-free, with the potential to identify metabolic features of cancer. To succeed, encoded MRS time signals must be adequately processed. This has not been feasible in clinical MRS which relies upon the fast Fourier transform (FFT), generating low-quality, poorly-informative spectra, with very few metabolites identified. Our meta-analysis shows that cancerous and benign ovarian lesions are inadequately distinguished via FFT-based MRS. Our advanced signal processor, the fast Pade transform (FPT) has high-resolution capacity and is quantification-equipped. We highlight key achievements of FPT-based MRS for ovarian cancer diagnostics. EXPLANATION OF THE DATA: In studies on noisy in-vitro type MRS data associated with benign and cancerous ovary, the parametric FPT was benchmarked, precisely reconstructing all physical spectral parameters with extremely high resolution. The parametric FPT was successfully applied to MRS time signals encoded in-vivo from serous cystic ovarian tumor. Noise was separated out, to identify and quantify densely-packed, often overlapping genuine spectral components. These include recognized and possible cancer biomarkers: phosphocholine, myoinositol, isoleucine, valine, lactate, threonine, alanine, among some 90 metabolites in a narrow spectral range-of-interest. Nearly all these metabolites remain undetected with Fourier-based in-vivo MRS of the ovary. The previously unexplored properties of the non-parametric derivative FPT (dFPT) have been benchmarked for detection and quantification of phosphocholine, a key biomarker of ovarian cancer and other malignancies. This is a very demanding task for this shape estimation, since phosphocholine is invisible in customary non-derivative envelopes. The dFPT solves this problem, clearly identifying and quantifying all genuine resonances, including phosphocholine. Thereby, on the same screen, phosphocholine is visualized, as are the concentrations and other peak signatures for all the other metabolites. Thus, the higher-order differentiation transform in the dFPT simultaneously enhances resolution, suppresses noise and exactly quantifies, despite non-parametric processing of envelope lineshapes alone. In sharp contrast, even at low differentiation order, the derivative FFT hugely amplifies noise, losing all genuine information. With increased derivative order, the non-parametric dFPT exactly reconstructs the components of the parametric dFPT. The peak signatures (positions, heights, widths) reconstructed by the non-parametric dFPT in the magnitude mode are uniquely related to the absorptive non-derivative parametric FPT. This permits straightforward interpretation and extraction of peak area and associated metabolite concentrations. Thus, in the performed benchmarking, the higher-order non-parametric dFPT is a stand-alone method for clear display with identification and exact quantification of key metabolic information, including for the ovarian cancer biomarker phosphocholine. CONCLUSIONS: Derivative magnetic resonance spectroscopy provides clearly interpretable spectra for clinicians, with all needed quantitative information readily at-hand. Being computationally fast, with robust noise suppression, the dFPT is poised to be implemented in clinical scanners. These proof-of-concept studies with in-vitro type data justify pursuing this strategy in-vivo for ovarian cancer diagnostics. We anticipate that the high-order non-parametric dFPT will be the stand-alone method-of-choice for streamlined detection and exact quantification of key ovarian cancer biomarkers for in-vivo encoded MRS time signals. Work is underway to carry-out these next steps. Note: This abstract was not presented at the symposium. Citation Format: Dzevad Belkic, Karen Belkic. KEY ACHIEVEMENT OF THE FAST PADE TRANSFORM IN MAGNETIC RESONANCE SPECTROSCOPY FOR EARLY OVARIAN CANCER DIAGNOSTICS [abstract]. In: Proceedings of the 12th Biennial Ovarian Cancer Research Symposium; Sep 13-15, 2018; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2019;25(22 Suppl):Abstract nr DP-001.