The success of TYS will depend on the acceptance of TYS by all the relevant pathology and gynecologic oncology communities who, by their joint efforts, will adopt, critically evaluate, and optimize this method with the only aim of further improving the impact of endometrial cytology on patients' care.

OBJECTIVEThis study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics.STUDY DESIGNA systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted.RESULTSThe vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material.CONCLUSIONSCytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake.

Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV), including DNA microarrays 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. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS), composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%), high specificity (97.1%), high positive predictive value (89.4%), and high negative predictive value (97.1%), for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.

Fine-needle aspiration (FNA) cytology is an important diagnostic tool in patients with thyroid lesions. Several systems have been proposed for the cyropathologic diagnosis of the thyroid nodules. However cases with indeterminate cytological findings still remain a matter of debate. In this review we analyze all literature regarding Thyroid Cytopathology Reporting systems trying to identify the most suitable methodology to use in clinical practice for the preoperative diagnosis of thyroid nodules. A review of the English literature was conducted, and data were analyzed and summarized and integrated from the authors' perspective. The main purpose of thyroid FNA is to identify patients with higher risk for malignancy, and to prevent unnecessary surgeries for benign conditions. The Bethesda System for Reporting Thyroid Cytopathology is the most widely used system for the diagnosis of thyroid FNA specimens. This system also contains guidelines for the diagnosis and treatment of indeterminate or suspicious for malignancy cases. In conclusion, patients who require repeated FNAs for indeterminate diagnoses will be resolved by repeat FNA in a percentage of 72%-80%.

The DSSS based on an ANN of multilayer perceptron (MLP) type, can predict with the highest accuracy the histological diagnosis in women with abnormalities at cytology when compared with the use of tests alone. A user-friendly software based on this technology could be used to guide clinician decision making towards a more personalised care.

In a population with a high prevalence of Ureaplasma spp, there was an association of this pathogen with high-risk HPV infection, a finding that needs further elucidation.

We propose a clinically oriented classification scheme consisting of diagnostic categories with well determined criteria. Each diagnostic category is linked with an implied risk of malignancy; thus, clinicians may decide on patient management and eventually reduce unnecessary interventional diagnostic procedures. Diagn. Cytopathol. 2016;44:888-901. © 2016 Wiley Periodicals, Inc.

Computerized systems based on ANNs can aid the cytological classification of endometrial nuclei and lesions with sufficient sensitivity and specificity. Diagn. Cytopathol. 2017;45:202-211. © 2016 Wiley Periodicals, Inc.