Ovarian cancer is a major cause of cancer death among women worldwide, and particularly in Israel. Although the disease at stage IA has 5 year survival rates of over 90%, early detection methods are not sufficiently accurate. Consequently, ovarian cancer is typically diagnosed late, which results in high fatality rates. An excellent candidate for early ovarian cancer detection would be in vivo magnetic resonance spectroscopy (MRS) because it is non-invasive and free of ionizing radiation. In addition, it potentially identifies metabolic features of cancer. Detecting these metabolic features depends on adequate processing of encoded MRS time signals for reconstructing interpretable information. The conventional Fourier-based method currently used in all clinical scanners is inadequate for this task. Thus, cancerous and benign ovarian lesions are not well distinguished. Advanced signal processing, such as the fast Padé transform (FPT) with high-resolution and clinically reliable quantification, is needed. The effectiveness of the FPT was demonstrated in proof-of-concept studies on noise-controlled MRS data associated with benign and cancerous ovaries. The FPT has now been successfully applied to MRS time signals encoded in vivo from a borderline serous cystic ovarian tumor. Noise was effectively separated out to identify and quantify genuine spectral constituents that are densely packed and often overlapping. Among these spectral constituents are recognized and possible cancer biomarkers including phosphocholine, choline, isoleucine, valine, lactate, threonine, alanine, and myoinositol. Most of these resonances remain undetected with Fourier-based in vivo MRS of the ovary. With Padé optimization, in vivo MRS could become a key method for assessing ovarian lesions, more effectively detecting ovarian cancer early, thereby improving survival for women afflicted with this malignancy.