Identification of women for referral to colposcopy by neural networks: A preliminary study based on LBC and molecular biomarkers

Objective of this study is to investigate the potential of the learning vector quantizer neural network (LVQ-NN) classifier on various diagnostic variables used in the modern cytopathology laboratory and to build an algorithm that may facilitate the classification of individual cases. From all women included in the study, a liquid-based cytology sample was obtained; this was tested via HPV DNA test, E6/E7 HPV mRNA test, and p16 immunostaining. The data were classified by the LVQ-NN into two groups: CIN-2 or worse and CIN-1 or less. Half of the cases were used to train the LVQ-NN; the remaining cases (test set) were used for validation. Out of the 1258 cases, cytology identified correctly 72.90% of the CIN-2 or worst cases and 97.37% of the CIN-1 or less cases, with overall accuracy 94.36%. The application of the LVQ-NN on the test set allowed correct classification for 84.62% of the cases with CIN-2 or worse and 97.64% of the cases with CIN-1 or less, with overall accuracy of 96.03%. The use of the LVQ-NN with cytology and the proposed biomarkers improves significantly the correct classification of cervical precancerous lesions and/or cancer and may facilitate diagnosis and patient management

mRNA and DNA Detection of Human Papillomaviruses in Women of All Ages Attending Two Colposcopy Clinics

Objective: HPV infection is a common finding, especially in young women while the majority of infections are cleared within a short time interval. The aim of this study was to examine the efficacy of HPV DNA and mRNA testing in a population attending colposcopy units of two University hospitals. Methods: 1173 liquid based cervical samples from two colposcopy clinics were tested for HPV DNA positivity using a commercial typing kit and HPV E6/E7 mRNA positivity with a flow cytometry based commercial kit. Statistic measures were calculated for both molecular tests and morphological cytology and colposcopy diagnosis according to histology results. Results: HPV DNA, high-risk HPV DNA, HPV16 or 18 DNA and HPV mRNA was detected in 55.5%, 50.6%, 20.1% and 29.7% of the cervical smears respectively. Concordance between the DNA and the mRNA test was 71.6% with their differences being statistically significant. Both tests' positivity increased significantly as lesion grade progressed and both displayed higher positivity rates in samples from women under 30 years old. mRNA testing displayed similar NPV, slightly lower sensitivity but significantly higher specificity and PPV than DNA testing, except only when DNA positivity for either HPV16 or 18 was used. Conclusions: Overall mRNA testing displayed higher clinical efficacy than DNA testing, either when used as a reflex test or as an ancillary test combined with morphology. Due to enhanced specificity of mRNA testing and its comparable sensitivity in ages under 25 or 30 years old, induction of mRNA testing in young women could be feasible if a randomized trial verifies these results. © 2012 Spathis et al

Fine needle aspiration cytology of nodular thyroid lesions: a 2-year experience of the Bethesda system for reporting thyroid cytopathology in a large regional and a university hospital, with histological correlation

ObjectiveThyroid fine needle aspiration (FNA) contributes to the appropriate management of nodular thyroid lesions. The introduced categories in the Bethesda system for reporting thyroid cytopathology (TBSRTC) are associated with an implied cancer risk, providing a clinical management guideline. This study aims to evaluate the reproducibility of this implied risk and to compare the results from two different cytopathology departments. MethodsFive hundred histologically confirmed FNAs, studied since the introduction of TBSRTC, were obtained from 4208 and 3587 FNAs performed in a large regional hospital in Herakleion, Crete (group A) and a university hospital in Athens (group B), respectively. Reports were issued according to TBSRTC. Aspirates were prepared with ThinPrep((R)) and evaluated by two experienced cytopathologists. The reproducibility and accuracy were evaluated. ResultsThe proportion test for suspicious for malignancy (SFM) and malignant (M) cytology reports (P<0.0001), and the number of malignancies on histology (P<0.0001), were significantly higher in group A than in group B, consistent with a higher incidence of thyroid carcinomas in southern Greece. Although the malignancy rates were higher in group A than in group B for all categories, except M (A, 99.3%; B, 100%), the difference was only significant for benign aspirates (P=0.0303). Malignancy rates for all categories in group A were above the TBSRTC recommended range, but were consistent with an increased prevalence of malignancy in that centre, differences in reporting practice and the variable ranges reported in the literature. There was lower sensitivity (P=0.019) and overall accuracy (P=0.003) in group A relative to group B, but no difference in specificity. ConclusionsTBSRTC provides valuable information for the appropriate management of nodular thyroid lesions, both in a university and a large regional hospital

Identification of women for referral to colposcopy by neural networks: A preliminary study based on LBC and molecular biomarkers

Objective of this study is to investigate the potential of the learning vector quantizer neural network (LVQ-NN) classifier on various diagnostic variables used in the modern cytopathology laboratory and to build an algorithm that may facilitate the classification of individual cases. From all women included in the study, a liquid-based cytology sample was obtained; this was tested via HPV DNA test, E6/E7 HPV mRNA test, and p16 immunostaining. The data were classified by the LVQ-NN into two groups: CIN-2 or worse and CIN-1 or less. Half of the cases were used to train the LVQ-NN; the remaining cases (test set) were used for validation. Out of the 1258 cases, cytology identified correctly 72.90% of the CIN-2 or worst cases and 97.37% of the CIN-1 or less cases, with overall accuracy 94.36%. The application of the LVQ-NN on the test set allowed correct classification for 84.62% of the cases with CIN-2 or worse and 97.64% of the cases with CIN-1 or less, with overall accuracy of 96.03%. The use of the LVQ-NN with cytology and the proposed biomarkers improves significantly the correct classification of cervical precancerous lesions and/or cancer and may facilitate diagnosis and patient management. © 2012 Petros Karakitsos et al

Radial basis function artificial neural network for the investigation of thyroid cytological lesions

Objective. This study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions. Study Design. The study was performed on 447 patients who had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid-based cytology, and the histological result was based on subsequent surgical samples. Each specimen was digitized; on these images, nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant. Results. The RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed. Conclusion. AI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images. © 2020 Christos Fragopoulos et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited