Atypical Endometrial Hyperplasia, Low-grade: "Much ADO About Nothing"

Integrated histological parameters define prognostically relevant groups in atypical endometrial hyperplasia/endometrioid intraepithelial neoplasia

OBJECTIVE

To assess the risk of endometrial carcinoma following a diagnosis of atypical hyperplasia/endometrioid intraepithelial neoplasia by endometrial biopsy, stratified based on integrated histological parameters.

METHODS

All women with atypical hyperplasia/endometrioid intraepithelial neoplasia undergoing hysterectomy within 1 year of diagnosis without progestin treatment were included. Patients were subdivided into three study groups, based on two criteria: (a) grade of nuclear atypia and (b) foci (<2 mm) of confluent glands with no intervening stroma: low-grade, high-grade, and confluent glands. The rate of endometrial carcinoma on the subsequent hysterectomy was assessed in each study group, and differences between study groups were assessed using Fisher's exact test, with a significant p value <0.05. Reproducibility was assessed by using Cohen's κ.

RESULTS

Ninety-six patients were included. Overall, 36 of 96 patients (37.5%) had endometrial carcinoma on the subsequent hysterectomy. The number of endometrial carcinomas was 4 of 42 (9.5%) in the low-grade group, 14 of 28 (50.0%) in the high-grade group, and 18 of 26 (69.2%) in the confluent glands group. The rate of endometrial carcinoma was significantly higher in the high-grade group than in the low-grade group (p<0.001), whereas it did not significantly differ between the high-grade group and the confluent glands group (p=0.176). The reproducibility among pathologists was moderate for low-grade versus high-grade (κ=0.58) and substantial for confluent glands versus low-grade (κ=0.63) and high-grade (κ=0.63).

CONCLUSION

Atypical hyperplasia/endometrioid intraepithelial neoplasia can be stratified into prognostically relevant groups based on integrated histological parameters, with a possible major impact on patient management.

Factors associated with interobserver variation amongst pathologists in the diagnosis of endometrial hyperplasia: A systematic review

OBJECTIVE

Reproducible diagnoses of endometrial hyperplasia (EH) remains challenging and has potential implications for patient management. This systematic review aimed to identify pathologist-specific factors associated with interobserver variation in the diagnosis and reporting of EH.

METHODS

Three electronic databases, namely MEDLINE, Embase and Web of Science, were searched from 1st January 2000 to 25th March 2023, using relevant key words and subject headings. Eligible studies reported on pathologist-specific factors or working practices influencing interobserver variation in the diagnosis of EH, using either the World Health Organisation (WHO) 2014 or 2020 classification or the endometrioid intraepithelial neoplasia (EIN) classification system. Quality assessment was undertaken using the QUADAS-2 tool, and findings were narratively synthesised.

RESULTS

Eight studies were identified. Interobserver variation was shown to be significant even amongst specialist gynaecological pathologists in most studies. Few studies investigated pathologist-specific characteristics, but pathologists were shown to have different diagnostic styles, with some more likely to under-diagnose and others likely to over-diagnose EH. Some novel working practices were identified, such as grading the "degree" of nuclear atypia and the incorporation of objective methods of diagnosis such as semi-automated quantitative image analysis/deep learning models.

CONCLUSIONS

This review highlighted the impact of pathologist-specific factors and working practices in the accurate diagnosis of EH, although few studies have been conducted. Further research is warranted in the development of more objective criteria that could improve reproducibility in EH diagnostic reporting, as well as determining the applicability of novel methods such as grading the degree of nuclear atypia in clinical settings.

Postmenopausal endometrial non-benign lesion risk classification through a clinical parameter-based machine learning model

OBJECTIVE

This study aimed to develop and evaluate a machine learning model utilizing non-invasive clinical parameters for the classification of endometrial non-benign lesions, specifically atypical hyperplasia (AH) and endometrioid carcinoma (EC), in postmenopausal women.

METHODS

Our study collected clinical parameters from a cohort of 999 patients with postmenopausal endometrial lesions and conducted preprocessing to identify 57 relevant characteristics from these irregular clinical data. To predict the presence of postmenopausal endometrial non-benign lesions, including atypical hyperplasia and endometrial cancer, we employed various models such as eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), as well as two ensemble models. Additionally, a test set was performed on an independent dataset consisting of 152 patients. The performance evaluation of all models was based on metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and F1 score.

RESULTS

The RF model demonstrated superior recognition capabilities for patients with non-benign lesions compared to other models. In the test set, it attained a sensitivity of 88.1% and an AUC of 0.93, surpassing all alternative models evaluated in this study. Furthermore, we have integrated this model into our hospital's Clinical Decision Support System (CDSS) and implemented it within the outpatient electronic medical record system to continuously validate and optimize its performance.

CONCLUSIONS

We have trained a model and deployed a system with high discriminatory power that may provide a novel approach to identify patients at higher risk of postmenopausal endometrial non-benign lesions who may benefit from more tailored screening and clinical intervention.

The Advance and Correlation of KRAS Mutation With the Fertility-Preservation Treatment of Endometrial Cancer in the Background of Molecular Classification Application

The annually increasing incidence of endometrial cancer in younger women has created a growing demand for fertility preservation. However, the diverse therapeutic efficacy among patients under the same histological subtype and the same tumor grade suggests the potential interference of the innate molecular characteristics. The molecular classification has now been applied in clinical practice and might help to stratify the endometrial cancer patients and individualize the therapy, but the candidates for the fertility-spared treatment are most likely to be subdivided in the subgroup lacking the specific signature. KRAS mutation has been linked to the malignant transition of the endometrium, while its role in molecular classification and fertility preservation is vague. Here, we mainly review the advance of molecular classification and the role of KRAS in endometrial cancer, as well as their correlation with fertility-preservation treatment.