MRI Radiomics Combined With Pathomics on the Prediction of Molecular Classification and Prognosis of Endometrial Cancer

Endometrial Neoplasms

Endometrial Neoplasms — a type of gynecological cancer.

Multisequence magnetic resonance imaging-based radiomics models for the prediction of microsatellite instability in endometrial cancer

To evaluate the performance of multisequence magnetic resonance imaging (MRI)-based radiomics models in the assessment of microsatellite instability (MSI) status in endometrial cancer (EC). This retrospective multicentre study included 338 EC patients with available MSI status and preoperative MRI scans, divided into training (37 MSI, 123 microsatellite stability [MSS]), internal validation (15 MSI, 52 MSS), and external validation cohorts (30 MSI, 81 MSS). Radiomics features were extracted from T2-weighted images, diffusion-weighted images, and contrast-enhanced T1-weighted images. The ComBat harmonisation method was applied to remove intrascanner variability. The Boruta wrapper algorithm was used for key feature selection. Three classification algorithms, logistic regression (LR), random forest (RF), and support vector machine (SVM), were applied to build the radiomics models. The area under the receiver operating characteristic curve (AUC) was calculated to compare the diagnostic performance of the models. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the models. Among the 1980 features, Boruta finally selected nine radiomics features. A higher MSI prediction performance was achieved after running the ComBat harmonisation method. The SVM algorithm had the best performance, with AUCs of 0.921, 0.903, and 0.937 in the training, internal validation, and external validation cohorts, respectively. The DCA results showed that the SVM algorithm achieved higher net benefits than the other classifiers over a threshold range of 0.581-0.783. The multisequence MRI-based radiomics models showed promise in preoperatively predicting the MSI status in EC in this multicentre setting.

Molecular Profiling of Endometrial Cancer From TCGA to Clinical Practice

Molecular classification provides an objective, reproducible framework for categorization of endometrial cancers (ECs), informing prognosis and selection of therapy. Currently, the uptake of molecular classification, integration in to EC management algorithms, and enrollment in molecular subtype-specific clinical trials lags behind what it could be. Access to molecular testing is not uniform, and subsequent management (surgical, adjuvant therapy) is unacceptably variable. We are in the midst of a critical landscape change in this disease site, with increasing emphasis on the integration of molecular features in EC care that can potentially improve standard of care globally. This article summarizes the rationale for molecular classification of ECs, strategies for implementation in low and high resource settings, and actionable opportunities based on this information.

Endometrial cancer

Endometrial cancer is the most common gynaecological cancer in high income countries and its incidence is rising globally. Although an ageing population and fewer benign hysterectomies have contributed to this trend, the growing prevalence of obesity is the major underlying cause. Obesity poses challenges for diagnosis and treatment and more research is needed to offer primary prevention to high-risk women and to optimise endometrial cancer survivorship. Early presentation with postmenopausal bleeding ensures most endometrial cancers are cured by hysterectomy but those with advanced disease have a poor prognosis. Minimally invasive surgical staging and sentinel-lymph-node biopsy provides a low morbidity alternative to historical surgical management without compromising oncological outcomes. Adjuvant radiotherapy reduces loco-regional recurrence in intermediate-risk and high-risk cases. Advances in our understanding of the molecular biology of endometrial cancer have paved the way for targeted chemotherapeutic strategies, and clinical trials will establish their benefit in adjuvant, advanced, and recurrent disease settings in the coming years.