Atypical Glandular Cells of Endometrial Origin and the Risk of Endometrial Cancer

Physical Modalities for the Treatment of Localized Provoked Vulvodynia: A Scoping Review of the Literature from 2010 to 2023

Introduction

Localized provoked vulvodynia (LPV) is a prevalent sexual health condition with significant negative impacts on quality of life. There is a lack of consensus regarding effective management.

Methods

We used Arksey and O'Malley's five-step method to identify, collate, and evaluate literature published between 2010 and 2023. The scoping review investigated the efficacy or effectiveness of interventions in the management of LPV. The aim of this paper is to map the literature on the efficacy or effectiveness of physical interventions.

Results

The review produced 19 primary studies of physical interventions for LPV. These include acupuncture, laser therapy, physiotherapy, transcutaneous electrical nerve stimulation, low-intensity shockwave therapy, transcranial direct current stimulation, and vestibulectomy.

Conclusion

Published studies that investigated a range of physical treatments for LPV showed some positive effects, except for transcranial direct-current stimulation. The remaining modalities demonstrated improved sexual pain and treatment satisfaction, when measured. Findings were mixed for non-sexual pain. There was insufficient evidence to draw conclusions regarding other outcomes. Researchers are encouraged to conduct larger, high-quality studies that sample more diverse patient populations and use patient-oriented outcomes to assess effectiveness of physical modalities.

Do High Rates of Atypical Glandular Cells Correlate With Higher Incidence of Disease in a Large Safety Net Hospital

OBJECTIVE

The aim of the study was to describe the incidence and correlates of atypical glandular cell (AGC) Pap tests in a low socioeconomic status, underserved population.

MATERIALS AND METHODS

Medical records of patients with AGC Pap tests at a single institution were reviewed from January 2013 to August 2019. Baseline characteristics were extracted including age, body mass index, birth control, abnormal uterine bleeding, and human papillomavirus (HPV). All colposcopy and endometrial biopsies were classified into negative/low-risk (polyps, tubular metaplasia, microglandular hyperplasia, cervical intraepithelial neoplasia 1) and high-risk (HR) lesions (cervical intraepithelial neoplasia 2/3, adenocarcinoma in situ, endometrial hyperplasia, cervical cancer, endometrial cancer). Logistic regression identified significant associations. Sixty-eight randomly selected AGC cytology slides from the cohort and 32 non-AGC slides outside the cohort were blindly reviewed by 6 pathologists. Fleiss κ interrater agreement was assessed.

RESULTS

Seven hundred forty patients with AGC Pap tests were identified (0.8% of all Pap tests performed during this time). After excluding for incomplete data, 478 patients were included. Sixty-three patients had HR lesions (13.3%). Patients with HR lesions had increased odds of abnormal uterine bleeding (odds ratio = 4.32, p < .001) and HPV positivity (odds ratio = 10.89, p < .001) when compared with patients with low-risk lesions. The κ agreement was 0.21 for all cases and 0.18 for AGC alone.

CONCLUSIONS

This population falls within the national averages for AGC Pap tests. There was an increased risk of HR lesions in patients with abnormal uterine bleeding and HPV positivity. The rate of HR lesions among AGC Pap tests was at the lower end of values in the literature. After blinded pathologist review, interobserver κ agreement was low for AGC Pap tests.

Subcategorization of atypical glandular cells is useful to identify lesion site

BACKGROUND

In the subcategorization of atypical glandular cells (AGCs), origin of cells should be mentioned to estimate lesion sites for diagnosis. However, cases without subcategorization are often encountered due to limited reproducibility. We evaluated whether the subcategorization of AGC based on the Bethesda terminology can estimate lesion sites.

METHODS

We retrospectively investigated cases whose cervical smears were interpreted as AGC and underwent pathological assessment at our institution between June 2009 and September 2017. AGC was subcategorized based on the Bethesda System. Not-otherwise-specified (NOS) was subcategorized into endocervical cells (NOS-EC), endometrial cells (NOS-EM), or glandular cells (NOS-G). Favor neoplastic (FN) was subcategorized into endocervical cells (FN-EC) or glandular cells (FN-G). FN-G was further subcategorized into endometrial cells (FN-EM) or unknown origin (FN-UO). Clinicopathological data were retrieved from the medical records.

RESULTS

Of 88 AGC cases, there were 30 NOS-EC (34.1%), 2 NOS-EM (2.3%), 25 FN-EC (28.4%), 22 FN-EM (25.0%), and 9 FN-UO (10.2%). A significantly higher proportion of neoplastic lesions occurred in FN than in NOS (P <.001). The concordance of AGC subclass and lesion site was 88.0%, 70.7%, and 77.3% in FN-EC, FN-G, and FN-EM, respectively. The concordance of FN-EM and lesion site increased to 88.9% in patients aged >50 years. Of nine cases of FN-UO, six experienced nonendometrioid endometrial cancer and extrauterine malignancy.

CONCLUSION

Subcategorization of NOS and FN would be useful in estimating neoplastic lesions. Further subcategorization into FN-EC, FN-EM, and FN-UO would similarly be beneficial in estimating the lesion site, especially for small endometrial and extrauterine lesions.

Metabolomic biomarkers in cervicovaginal fluid for detecting endometrial cancer through nuclear magnetic resonance spectroscopy

INTRODUCTION

Endometrial cancer (EC) is one of the most common gynecologic neoplasms in developed countries but lacks screening biomarkers.

OBJECTIVES

We aim to identify and validate metabolomic biomarkers in cervicovaginal fluid (CVF) for detecting EC through nuclear magnetic resonance (NMR) spectroscopy.

METHODS

We screened 100 women with suspicion of EC and benign gynecological conditions, and randomized them into the training and independent testing datasets using a 5:1 study design. CVF samples were analyzed using a 600-MHz NMR spectrometer equipped with a cryoprobe. Four machine learning algorithms-support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), random forest (RF), and logistic regression (LR), were applied to develop the model for identifying metabolomic biomarkers in cervicovaginal fluid for EC detection.

RESULTS

A total of 54 women were eligible for the final analysis, with 21 EC and 33 non-EC. From 29 identified metabolites in cervicovaginal fluid samples, the top-ranking metabolites chosen through SVM, RF and PLS-DA which existed in independent metabolic pathways, i.e. phosphocholine, malate, and asparagine, were selected to build the prediction model. The SVM, PLS-DA, RF, and LR methods all yielded area under the curve values between 0.88 and 0.92 in the training dataset. In the testing dataset, the SVM and RF methods yielded the highest accuracy of 0.78 and the specificity of 0.75 and 0.80, respectively.

CONCLUSION

Phosphocholine, asparagine, and malate from cervicovaginal fluid, which were identified and independently validated through models built using machine learning algorithms, are promising metabolomic biomarkers for the detection of EC using NMR spectroscopy.