CxCaDSS: A Web-Based Clinical Decision Support System for Cervical Cancer

Data from countries with well-organized screening programs and cancer registries indicate that the vast majority of participants who developed cervical cancer could be explained as underestimation of cases that had at least one abnormal Pap test. Nowadays, there are ancillary molecular biology techniques available that provide important information related to cervical cancer and the HPV natural history, including DNA micro-arrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. However, each one of these techniques has its own performance, advantages and limitations, thus a combinatorial approach via artificial intelligence methods could exploit the benefits of each method and produce more accurate results. In this paper we present a novel web-based clinical decision support system and its integration with underlying artificial neural networks, for the combination of the results of classic and ancillary techniques in order to increase the accuracy of diagnosis and thus identify women at true risk of developing cervical cancer. The presented system follows the MVC approach enabling it to easily adapt to any underlying data and structure to support clinical decisions for other domains as well.