Risk Stratification In Endometrial Cancer Research Articles

The impact of integrated genomic analysis on molecular classifications and prognostic risk stratification in endometrial cancer

The impact of integrated genomic analysis on molecular classifications and prognostic risk stratification in endometrial cancer

Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer.

Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC. To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI. The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney U test, Pearson test, and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features, which were subsequently utilized to generate a radiomic signature. Seven machine learning strategies were used to construct radiomic models that relied on the screening features. The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators. Having an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature. The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.

Endometrial cancer risk stratification using MRI radiomics: corroborating with choline metabolism

Background and purposeRadiomics offers little explainability. This study aims to develop a radiomics model (Rad-Score) using diffusion-weighted imaging (DWI) to predict high-risk patients for nodal metastasis or recurrence in endometrial cancer (EC) and corroborate with choline metabolism.Materials and methodsFrom August 2015 to July 2018, 356 EC patients were enrolled. Rad-Score was developed using LASSO regression in a training cohort (n = 287) and validated in an independent test cohort (n = 69). MR spectroscopy (MRS) was also used in 230 patients. Nuclear MRS measured choline metabolites in 70 tissue samples. The performance was compared against European Society for Medical Oncology (ESMO) risk groups. A P < .05 denoted statistical significance.ResultsRad-Score achieved 71.1% accuracy in the training and 71.0% in the testing cohorts. Incorporating clinical parameters of age, tumor type, size, and grade, Rad-Signature reached accuracies of 73.2% in training and 75.4% in testing cohorts, closely matching the performance to the post-operatively based ESMO's 70.7% and 78.3%. Rad-Score was significantly associated with increased total choline levels on MRS (P = .034) and tissue levels (P = .019).ConclusionsDevelopment of a preoperative radiomics risk score, comparable to ESMO clinical standard and associated with altered choline metabolism, shows translational relevance for radiomics in high-risk EC patients.Trial registrationThis study was registered in ClinicalTrials.gov on 2015–08-01 with Identifier NCT02528864.

Predictive models for lymph node metastasis in endometrial cancer: A systematic review and bibliometric analysis.

Lymph node metastasis is associated with a poorer prognosis in endometrial cancer. The objective was to synthesize and critically appraise existing predictive models for lymph node metastasis risk stratification in endometrial cancer. This study is a systematic review. We searched the Web of Science for articles reporting models predicting lymph node metastasis in endometrial cancer, with a systematic review and bibliometric analysis conducted based upon which. Risk of bias was assessed by the Prediction model Risk Of BiAS assessment Tool (PROBAST). A total of 64 articles were included in the systematic review, published between 2010 and 2023. The most common articles were "development only." Traditional clinicopathological parameters remained the mainstream in models, for example, serum tumor marker, myometrial invasion and tumor grade. Also, models based upon gene-signatures, radiomics and digital histopathological images exhibited an acceptable self-reported performance. The most frequently validated models were the Mayo criteria, which reached a negative predictive value of 97.1%-98.2%. Substantial variability and inconsistency were observed through PROBAST, indicating significant between-study heterogeneity. A further bibliometric analysis revealed a relatively weak link between authors and organizations on models predicting lymph node metastasis in endometrial cancer. A number of predictive models for lymph node metastasis in endometrial cancer have been developed. Although some exhibited promising performance as they demonstrated adequate to good discrimination, few models can currently be recommended for clinical practice due to lack of independent validation, high risk of bias and low consistency in measured predictors. Collaborations between authors, organizations and countries were weak. Model updating, external validation and collaborative research are urgently needed. None.

SP-1011 Risk stratification in endometrial cancer: Evidence, future steps, and how to implement in everyday clinic

SP-1011 Risk stratification in endometrial cancer: Evidence, future steps, and how to implement in everyday clinic

Risk prediction models for endometrial cancer: development and validation in an international consortium.

Endometrial cancer risk stratification may help target interventions, screening, or prophylactic hysterectomy to mitigate the rising burden of this cancer. However, existing prediction models have been developed in select cohorts and have not considered genetic factors. We developed endometrial cancer risk prediction models using data on postmenopausal White women aged 45-85 years from 19 case-control studies in the Epidemiology of Endometrial Cancer Consortium (E2C2). Relative risk estimates for predictors were combined with age-specific endometrial cancer incidence rates and estimates for the underlying risk factor distribution. We externally validated the models in 3 cohorts: Nurses' Health Study (NHS), NHS II, and the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. Area under the receiver operating characteristic curves for the epidemiologic model ranged from 0.64 (95% confidence interval [CI] = 0.62 to 0.67) to 0.69 (95% CI = 0.66 to 0.72). Improvements in discrimination from the addition of genetic factors were modest (no change in area under the receiver operating characteristic curves in NHS; PLCO = 0.64 to 0.66). The epidemiologic model was well calibrated in NHS II (overall expected-to-observed ratio [E/O] = 1.09, 95% CI = 0.98 to 1.22) and PLCO (overall E/O = 1.04, 95% CI = 0.95 to 1.13) but poorly calibrated in NHS (overall E/O = 0.55, 95% CI = 0.51 to 0.59). Using data from the largest, most heterogeneous study population to date (to our knowledge), prediction models based on epidemiologic factors alone successfully identified women at high risk of endometrial cancer. Genetic factors offered limited improvements in discrimination. Further work is needed to refine this tool for clinical or public health practice and expand these models to multiethnic populations.

Molecular Monitoring in Endometrial Cancer-Ready for Prime Time?

Efforts are under way to define the role of minimally invasive strategies for molecular monitoring and risk stratification in endometrial cancer. A recent publication aims to define the association between circulating tumor DNA level and disease stage in patients with newly diagnosed endometrial cancer and determine whether sequencing of longitudinal cell-free DNA samples can be used for disease monitoring and detection of progression or recurrence. These results accelerate the current knowledge of molecular follow-up in endometrial cancer. See related article by Ashley et al., p. 410.

Identification of miR-499a-5p as a Potential Novel Biomarker for Risk Stratification in Endometrial Cancer

IntroductionThe Cancer Genome Atlas (TCGA) project identified four distinct prognostic groups in endometrial cancer (EC), among which two are correlated with an intermediate prognosis: the MisMatch Repair-deficient (MMRd) and the No Specific Molecular Profile (NSMP) groups. The two groups represent a heterogeneous subset of patients frequently harboring CTNNB1 alterations with distinctive clinicopathologic features. The study aimed to evaluate the miRNA expression in ECs to identify potential biomarkers of prognosis.MethodsWe analyzed miRNA expression in 72 ECs classified as MMRd or NSMP including 15 ECs with CTNNB1 mutations. In the discovery step, miRNA expression was evaluated in 30 cases through TaqMan miRNA arrays. Subsequently, four miRNAs were validated in the total cohort of ECs. The data were further tested in the TCGA cohort, and correlations with overall survival (OS) and progression-free interval (PFI) were evaluated.ResultsmiR-499a-3p and miR-499a-5p resulted to be overexpressed in CTNNB1 mutant EC patients at intermediate risk. Similarly, in the TCGA cohort, miR-499a-3p and miR-499a-5p were differentially expressed between CTNNB1 mutant and wild-type patients (p < 0.0001). NSMP patients with low miR-499a-5p expression showed longer OS (p = 0.03, log-rank test). By combining miR-499a-3p or -5p expression levels with the CTNNB1 status, ECs with CTNNB1 mutation and lower miR-499a-5p expression showed better OS compared with the other subgroups (p = 0.03, log-rank test), among the NSMP patients. Moreover, in a multivariate analysis, combination of wild type CTNNB1 status and high miR-499a-5p expression was indipendently associated with high risk of death [HR (95%CI): 3.53 (1.1-10.5), p = 0.02].ConclusionOur results suggest that the combination of CTNNB1 status and miR-499a-5p allows a better stratification of NSMP patients and could promote a personalization of the treatment in intermediate-risk patients.

Abstract IA015: Molecular stratification: Beyond TCGA

Abstract The molecular endometrial cancer (EC) classification defined by TCGA has improved both understanding of disease biology and clinical risk stratification. In my talk, I will review data for other biomarkers with proven or potential utility to further refine risk stratification in EC. I will also present results of analysis of whole genome sequence data from the large cohort of ECs recruited to the Genomics England 100,000 Genomes Project, and highlight the promise of this initiative to define new tumour subsets and therapeutic targets. Citation Format: David Church. Molecular stratification: Beyond TCGA [abstract]. In: Proceedings of the AACR Virtual Special Conference: Endometrial Cancer: New Biology Driving Research and Treatment; 2020 Nov 9-10. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(3_Suppl):Abstract nr IA015.

Positive peritoneal cytology is an independent risk factor in endometrial cancer.

AbstractObjectiveWe aimed to investigate whether positive peritoneal cytology could serve as a prognostic factor in endometrial cancer (EC).MethodsFrom patients with EC surgically treated at Peking University People's Hospital between 2006 and 2016, 520 patients were selected for retrospective investigation. Clinical and pathologic factors associated with positive peritoneal cytology were assessed using univariate and multivariate logistic regressions model. Disease‐free survival (DFS) and overall survival (OS) were evaluated using Kaplan–Meier estimation and multivariate Cox regressions model.Results(i) The incidence of positive peritoneal cytology in EC patients was 9.0% (47/520). (ii) Multivariate analysis showed that the independent risk factors of positive peritoneal cytology were nonendometrioid carcinoma (odds ratio [OR] = 4.0, 95% confidence interval [CI], 1.8–8.8, P = 0.001), tumor diameter ≥2 cm (OR = 3.2, 95% CI, 1.2–8.7, P = 0.023), cervical stromal invasion (OR = 2.9, 95% CI, 1.3–6.6, P = 0.009) and distant metastasis (OR = 6.5, 95% CI, 1.7–25.4, P = 0.007). (iii) The 5‐year DFS and 5‐year OS of the positive peritoneal cytology group were significantly lower compared with the negative group (54.0% vs 93.2%, P &lt; 0.001; 63.6% vs 94.6%, P &lt; 0.001). (iv) After other contributing factors were adjusted in the multivariable model, positive peritoneal cytology remained an independent predictor of decreased DFS (OR = 2.8, 95% CI, 1.3–6.0, P = 0.007) and OS (OR = 2.8, 95% CI, 1.0–7.2, P = 0.035).ConclusionPositive peritoneal cytology was an independent risk factor in EC patients. Even though it is no longer an indicator according to the current International Federation of Gynecology and Obstetrics staging system, the peritoneal cytology status should still be considered for accurate risk stratification in EC.

Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study.

BackgroundBayesian networks (BNs) are machine-learning–based computational models that visualize causal relationships and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. Preoperative identification of patients at risk for lymph node metastasis (LNM) is challenging in endometrial cancer, and although several biomarkers are related to LNM, none of them are incorporated in clinical practice. The aim of this study was to develop and externally validate a preoperative BN to predict LNM and outcome in endometrial cancer patients.Methods and findingsWithin the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), we performed a retrospective multicenter cohort study including 763 patients, median age 65 years (interquartile range [IQR] 58–71), surgically treated for endometrial cancer between February 1995 and August 2013 at one of the 10 participating European hospitals. A BN was developed using score-based machine learning in addition to expert knowledge. Our main outcome measures were LNM and 5-year disease-specific survival (DSS). Preoperative clinical, histopathological, and molecular biomarkers were included in the network. External validation was performed using 2 prospective study cohorts: the Molecular Markers in Treatment in Endometrial Cancer (MoMaTEC) study cohort, including 446 Norwegian patients, median age 64 years (IQR 59–74), treated between May 2001 and 2010; and the PIpelle Prospective ENDOmetrial carcinoma (PIPENDO) study cohort, including 384 Dutch patients, median age 66 years (IQR 60–73), treated between September 2011 and December 2013. A BN called ENDORISK (preoperative risk stratification in endometrial cancer) was developed including the following predictors: preoperative tumor grade; immunohistochemical expression of estrogen receptor (ER), progesterone receptor (PR), p53, and L1 cell adhesion molecule (L1CAM); cancer antigen 125 serum level; thrombocyte count; imaging results on lymphadenopathy; and cervical cytology. In the MoMaTEC cohort, the area under the curve (AUC) was 0.82 (95% confidence interval [CI] 0.76–0.88) for LNM and 0.82 (95% CI 0.77–0.87) for 5-year DSS. In the PIPENDO cohort, the AUC for 5-year DSS was 0.84 (95% CI 0.78–0.90). The network was well-calibrated. In the MoMaTEC cohort, 249 patients (55.8%) were classified with <5% risk of LNM, with a false-negative rate of 1.6%. A limitation of the study is the use of imputation to correct for missing predictor variables in the development cohort and the retrospective study design.ConclusionsIn this study, we illustrated how BNs can be used for individualizing clinical decision-making in oncology by incorporating easily accessible and multimodal biomarkers. The network shows the complex interactions underlying the carcinogenetic process of endometrial cancer by its graphical representation. A prospective feasibility study will be needed prior to implementation in the clinic.

A Novel Approach to Preoperative Risk Stratification in Endometrial Cancer: The Added Value of Immunohistochemical Markers.

Background: The current model used to preoperatively stratify endometrial cancer (EC) patients into low- and high-risk groups is based on histotype, grade, and imaging method and is not optimal. Our study aims to prove whether a new model incorporating immunohistochemical markers, L1CAM, ER, PR, p53, obtained from preoperative biopsy could help refine stratification and thus the choice of adequate surgical extent and appropriate adjuvant treatment.Materials and Methods: The following data were prospectively collected from patients operated for EC from January 2016 through August 2018: age, pre- and post-operative histology, grade, lymphovascular space invasion, L1CAM, ER, PR, p53, imaging parameters obtained from ultrasound, CT chest/abdomen, final FIGO stage, and current decision model (based on histology, grade, imaging method).Results: In total, 132 patients were enrolled. The current model revealed 48% sensitivity and 89% specificity for high-risk group determination. In myometrial invasion >50%, lower levels of ER (p = 0.024), PR (0.048), and higher levels of L1CAM (p = 0.001) were observed; in cervical involvement a higher expression of L1CAM (p = 0.001), lower PR (p = 0.014); in tumors with positive LVSI, higher L1CAM (p = 0.014); in cases with positive LN, lower expression of ER/PR (p < 0.001), higher L1CAM (p = 0.002) and frequent mutation of p53 (p = 0.008).Cut-offs for determination of high-risk tumors were established: ER <78% (p = 0.001), PR <88% (p = 0.008), and L1CAM ≥4% (p < 0.001). The positive predictive values (PPV) for ER, PR, and L1CAM were 87% (60.8–96.5%), 63% (52.1–72.8%), 83% (70.5–90.8%); the negative predictive values (NPV) for each marker were as follows: 59% (54.5–63.4%), 65% (55.6–74.0%), and 77% (67.3–84.2%). Mutation of p53 revealed PPV 94% (67.4–99.1%) and NPV 61% (56.1–66.3%). When immunohistochemical markers were included into the current diagnostic model, sensitivity improved (48.4 vs. 75.8%, p < 0.001). PPV was similar for both methods, while NPV (i.e., the probability of extremely low risk in negative test cases) was improved (66 vs. 78.9%, p < 0.001).Conclusion: We proved superiority of new proposed model using immunohistochemical markers over standard clinical practice and that new proposed model increases accuracy of prognosis prediction. We propose wider implementation and validation of the proposed model.

Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer.

Improved methods for preoperative risk stratification in endometrial cancer are highly requested by gynecologists. Texture analysis is a method for quantification of heterogeneity in images, increasingly reported as a promising diagnostic tool in various cancer types, but largely unexplored in endometrial cancer. To explore whether tumor texture parameters from preoperative MRI are related to known prognostic features (deep myometrial invasion, cervical stroma invasion, lymph node metastases, and high-risk histological subtype) and to outcome in endometrial cancer patients. Prospective cohort study. In all, 180 patients with endometrial carcinoma were included from April 2009 to November 2013 and studied until January 2017. Preoperative pelvic MRI including contrast-enhanced T1 -weighted (T1 c), T2 -weighted, and diffusion-weighted imaging at 1.5T. Tumor regions of interest (ROIs) were manually drawn on the slice displaying the largest cross-sectional tumor area, using the proprietary research software TexRAD for analysis. With a filtration-histogram technique, the texture parameters standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis were calculated. Associations between texture parameters and histological features were assessed by uni- and multivariable logistic regression, including models adjusting for preoperative biopsy status and conventional MRI findings. Multivariable Cox regression analysis was used for survival analysis. High tumor entropy in apparent diffusion coefficient (ADC) maps independently predicted deep myometrial invasion (odds ratio [OR] 3.2, P lt 0.001), and high MPP in T1 c images independently predicted high-risk histological subtype (OR 1.01, P = 0.004). High kurtosis in T1 c images predicted reduced recurrence- and progression-free survival (hazard ratio [HR] 1.5, P lt 0.001) after adjusting for MRI-measured tumor volume and histological risk at biopsy. MRI-derived tumor texture parameters independently predicted deep myometrial invasion, high-risk histological subtype, and reduced survival in endometrial carcinomas, and thus, represent promising imaging biomarkers providing a more refined preoperative risk assessment that may ultimately enable better tailored treatment strategies in endometrial cancer. 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1637-1647.

Invivo MR spectroscopy predicts high tumor grade in endometrial cancer.

Background Invivo magnetic resonance spectroscopy (MRS) enables non-invasive measurements of tumor metabolites. Choline-containing metabolites play a key role in tumor metabolism. Purpose To explore whether preoperative MRS-derived tumor choline levels are associated with clinical and histological features in endometrial carcinomas. Material and Methods Preoperative pelvic magnetic resonance imaging (MRI) (1.5T), including structural and diffusion-weighted imaging and localized multivoxel proton MR (1H-MR) spectroscopy, was performed in 77 prospectively included patients with histologically confirmed endometrial carcinomas. Relative levels of total choline-containing metabolites (tCho) in tumor and myometrium were measured using the ratios: tCho/Creatine; tCho/Water; and tCho/Noise. MRS parameters were analyzed in relation to histological subtype and grade, surgicopathological staging parameters, MRI-measured tumor volume, and tumor apparent diffusion coefficient (ADC) value and clinical outcome. Results Tumor tissue had significantly higher ratios for tCho/Creatine, tCho/Water, and tCho/Noise than normal myometrial tissue ( P < 0.001 for all). High tumor tCho/Water ratio was significantly associated with high tumor grade in endometrioid tumors ( P = 0.02). Tumor tCho/Creatine ratio was positively correlated to MRI-measured tumor volume (rs = 0.25; P = 0.03). Conclusion High choline levels in tumor are associated with high-risk features. Invivo MRS may potentially aid in the preoperative risk stratification in endometrial cancer.

Prognostic significance of POLE proofreading mutations in endometrial cancer.

Background:Current risk stratification in endometrial cancer (EC) results in frequent over- and underuse of adjuvant therapy, and may be improved by novel biomarkers. We examined whether POLE proofreading mutations, recently reported in about 7% of ECs, predict prognosis.Methods:We performed targeted POLE sequencing in ECs from the PORTEC-1 and -2 trials (n = 788), and analyzed clinical outcome according to POLE status. We combined these results with those from three additional series (n = 628) by meta-analysis to generate multivariable-adjusted, pooled hazard ratios (HRs) for recurrence-free survival (RFS) and cancer-specific survival (CSS) of POLE-mutant ECs. All statistical tests were two-sided.Results: POLE mutations were detected in 48 of 788 (6.1%) ECs from PORTEC-1 and-2 and were associated with high tumor grade (P < .001). Women with POLE-mutant ECs had fewer recurrences (6.2% vs 14.1%) and EC deaths (2.3% vs 9.7%), though, in the total PORTEC cohort, differences in RFS and CSS were not statistically significant (multivariable-adjusted HR = 0.43, 95% CI = 0.13 to 1.37, P = .15; HR = 0.19, 95% CI = 0.03 to 1.44, P = .11 respectively). However, of 109 grade 3 tumors, 0 of 15 POLE-mutant ECs recurred, compared with 29 of 94 (30.9%) POLE wild-type cancers; reflected in statistically significantly greater RFS (multivariable-adjusted HR = 0.11, 95% CI = 0.001 to 0.84, P = .03). In the additional series, there were no EC-related events in any of 33 POLE-mutant ECs, resulting in a multivariable-adjusted, pooled HR of 0.33 for RFS (95% CI = 0.12 to 0.91, P = .03) and 0.26 for CSS (95% CI = 0.06 to 1.08, P = .06).Conclusion: POLE proofreading mutations predict favorable EC prognosis, independently of other clinicopathological variables, with the greatest effect seen in high-grade tumors. This novel biomarker may help to reduce overtreatment in EC.

Standard 1.5-T MRI of endometrial carcinomas: modest agreement between radiologists

To evaluate pelvic magnetic resonance imaging (MRI) interobserver agreement for the detection of deep myometrial invasion, cervical stroma invasion and lymph node metastases in endometrial carcinoma patients in relation to surgical staging. Fifty-seven patients with histologically confirmed endometrial carcinoma were prospectively included in a study of preoperative 1.5-T MRI. Four radiologists, blinded to patient data, independently reviewed the images for the presence of deep myometrial invasion, cervical stroma invasion and lymph node metastases. Kappa coefficients for interobserver agreement and diagnostic performances for each observer were calculated using final surgical staging results (FIGO 09) as reference standard. Overall agreement among all observers was moderate for cervical stroma invasion (κ = 0.50 [95% CI 0.27-0.73]) and lymph node metastases (κ = 0.56 [0.09-0.80]) and fair for deep myometrial invasion (κ = 0.39 [0.26-0.55]). Sensitivity (specificity) values for the four observers were 72-92% (44-63%) for deep myometrial invasion, 38-63% (82-94%) for cervical stroma invasion and 25-38% (90-100%) for lymph node metastases. Conventional MRI showed only modest interobserver agreement and diagnostic accuracy for detection of deep myometrial invasion, cervical stroma invasion and lymph node metastases. Improved methods are needed for preoperative imaging in the staging of endometrial carcinomas. MRI is an important tool for preoperative endometrial cancer staging. • Staging agreement based on pelvic MRI was modest among different observers. • Preoperative MRI alone was suboptimal in identifying high-risk patients. • Improved imaging and biomarkers may refine preoperative risk stratification in endometrial cancer.

Endometrial cancer risk stratification criteria applied in a unique population

Endometrial cancer risk stratification criteria applied in a unique population