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Writing on the future of cancer diagnostics, this author has predicted that multiparametric biomarker analysis, in combination with artificial neural networks and pattern recognition, will likely represent one of the most promising methodologies for diagnosing and monitoring cancer (1)(2). Over the last few years, we have witnessed publication of many reports dealing with proteomic patterns in biological fluids, and especially serum, by using the so-called “SELDI-TOF” technique (surface-enhanced laser desorption/ionization time-of-flight mass spectrometry), in combination with artificial intelligence (3)(4)(5)(6)(7). The reported sensitivities and specificities of this method for ovarian, prostate, and breast cancer diagnosis are clearly impressive, and they are superior to the sensitivities and specificities obtained with current serologic cancer biomarkers (8)(9)(10)(11)(12). In particular, these techniques appear to detect early as well as advanced disease with similar efficiency, making them candidate tools for cancer screening, an application that is not currently recommended, by utilizing the classical cancer biomarkers, e.g., CA125, carcinoembryonic antigen (CEA), and α-fetoprotein (AFP) (1).In addition to scientific journals, these reports have also been presented in international news media and have attracted public attention. Despite of some important shortcomings of these methodologies, criticism has been minimal (13)(14). It seems that the impressive bottom line (very high diagnostic sensitivity and specificity) overshadows potential problems. The recent publication of three reports, from two different research groups, on the use of this technology in the diagnosis of prostate cancer allows for comparison of the data and the methodology and for the presentation of some important questions that have not been adequately addressed. In the following paragraphs, I will focus on some critical questions and provide discussion that could form the basis for further investigations. I will concentrate only …

Proteomic Patterns in Biological Fluids: Do They Represent the Future of Cancer Diagnostics?