Numerous ancillary techniques detecting HPV DNA or mRNA are viewed as competitors or ancillary techniques to test Papanicolaou. However, no technique is perfect because sensitivity increases at the cost of specificity. Various methods have been applied to resolve this issue by using many examination results, such as classification and regression trees and supervised artificial neural networks. In this article, 1258 cases with results from test Pap, HPV DNA, HPV mRNA, and p16 were used to evaluate the performance of the self-organizing map (SOM). An artificial neural network has three advantages: it is unsupervised, can tolerate missing data, and produces topographical maps. The results of the SOM application were encouraging and produced maps depicting the important tests.

ThinPrep ® Pap test is a Liquid Based Cytology (LBC) method, representing the first, after 50 years, evolution of classical Papanicolaou test (Conventional Cytology-CC). This method initiates changes in the way of fixation and production of slides which enhance dramatically the smear quality. Due to this reason, ThinPrep Pap test was authorized in 1996 by the Food and Drug Administration (FDA) of U.S.A. as a replacement to the conventional Papanicolaou test. In ThinPrep, the smears, instead of layering on the glass slides immediately after their extraction form the cervix, they are collected using a sampling device which is rinsed into a vial containing a fixative solution (PreserveCyt ®). The vial is then transported to the cytopathology laboratory where a slide is prepared by specialized modalities that create a single layer of cells on the slide with total area less than 50% comparing to the area of a conventional slide. The remaining biological material in the LBC vial can be used for molecular techniques.

The COVID-19 pandemic has challenged health systems worldwide by decreasing their reserves and effectiveness. In this changing landscape, the urge for reallocation of financial and human resources represents a top priority. In screening, effectiveness and efficiency are most relevant. In the quest against cervical cancer, numerous molecular ancillary techniques detecting HPV DNA or mRNA or other related biomarkers complement morphological assessment by the Papanicolaou test. However, no technique is perfect as sensitivity increases at the cost of specificity. Various approaches try to resolve this issue by incorporating several examination results, such as artificial intelligence are proposed. In this study, 1,258 cases with a complete result dataset for cytology, HPV DNA, HPV mRNA, and p16 were used to evaluate the performance of a self-organizing map (SOM), an unsupervised artificial neural network. The results of the SOM application were encouraging since it is capable of producing maps discriminating the necessary tests and has improved performance.

During the last decade, there is an increasing need for quality improvement of medical laboratories via the use of quality related standards. Recently regulatory bodies suggest and sometimes enforce the application of ISO 15189, which is designed especially for medical laboratories. Despite the standard does oblige the application of Laboratory Information Systems (LISs), it is evident that without a LIS it is difficult for laboratories to operate efficiently. Modern cytopathology laboratories form complex systems composed of a multidisciplinary human team coupled with medical modalities and capabilities. Hopefully, such laboratories have well standardized and defined workflow. The adoption of the standard, creates numerous management requirements, introduces new functions and associated overhead. In this paper, we present design and implementation issues of an enhanced LIS to support ISO 15189 in a cytopathology laboratory. The LIS designed around ISO 15189 management requirements can improve, enhance and facilitate the standard application and adoption.