Irene de la Calle

ENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessment

ENDORISK is a Bayesian network that can assist in preoperative risk estimation of lymph node metastasis (LNM) risk in endometrial cancer (EC) with consistent performance in external validations. To reflect state of the art care, ENDORISK was optimized by integrating molecular classification and preoperative assessment of myometrial invasion (MI). Variables for POLE, MSI, and preoperative assessment of MI, either by expert transvaginal ultrasound or pelvic magnetic resonance imaging (MRI), were added to develop ENDORISK-2. The p53 biomarker, part of the molecular classification, was already included in ENDORISK. External validation of ENDORISK-2 for LNM prediction was performed in two independent cohorts from: Brno (CZ), (n = 581) and Tübingen (DE), (n = 247). ENDORISK-2 yielded AUCs of 0·85 (95 % CI 0·80-0·90) (CZ) and 0·86 (95 % CI 0·77-0·96) (DE) for predicting LNM. In patients with low-grade histology, 83 % (CZ) and 89 % (DE) were estimated having less than 10 % risk of LNM, with false negative rates (FNR) of 4·3 % (CZ) and 2·2 % (DE). The previously defined set of minimally required variables, i.e.: preoperative tumor grade, three of the four immunohistochemical (IHC) markers, and one clinical marker, could be interchanged with the new variables, with comparable validation metrics, including AUC values of 0·79-0·87 for LNM prediction. Incorporation of molecular data and preoperative MI improved the flexibility of ENDORISK with comparable diagnostic accuracy for estimating LNM as when based on low-cost immunohistochemical biomarkers. In addition, the high diagnostic accuracy in patients with low-grade EC demonstrates how ENDORISK-2 could aid clinicians in identifying patients in whom surgical lymph node assessment may safely be omitted. These results underline its power for clinical use in both high and low resource countries.

Current challenges and emerging tools in endometrial cancer diagnosis

The diagnostic process of endometrial cancer includes imaging methods such as trans-vaginal ultrasound, along with procedures to obtain endometrial tissue for histologic evaluation. Common techniques for tissue sampling include Pipelle endometrial biopsy, hysteroscopy, and dilation and curettage, which are used to confirm the diagnosis, determine tumor histology, grade, and molecular profile. However, diagnostic algorithms for endometrial cancer differ significantly across countries, influenced by local resources, protocols, and the availability of diagnostic methods. These variations include differences in the endometrial thickness threshold for recommending a biopsy and the choice of the initial diagnostic test. Moreover, patients often have multiple tests and appointments before a definitive diagnosis, although only 5%-10% of women with post-menopausal bleeding are diagnosed with endometrial cancer. Current diagnostic techniques have limitations. Pipelle endometrial biopsy has a significant false-negative rate (10%-20%) and may fail to provide adequate diagnostic material in up to 30% of cases. Hysteroscopy, while useful, is associated with pain in up to 65% of patients and can delay diagnosis because of limited availability. Dilation and curettage is an invasive procedure requiring general anesthesia and has a higher complication rate. In response to these challenges, there is growing interest in developing new diagnostic tools that are less invasive and provide 1-step diagnoses, including liquid biopsies from urine, blood, cervico-vaginal and endometrial fluid samples by means of genomics and proteomics. This review will examine the current diagnostic algorithms in European and American guidelines, evaluate the sensitivity, specificity, and accuracy of current techniques, and explore new diagnostic tools under development.