Diagnostic Accuracy of Clinical Biomarkers for Preoperative Prediction of Lymph Node Metastasis in Endometrial Carcinoma: A Systematic Review and Meta-Analysis

Risk factors for pelvic and para-aortic lymph node metastasis in non-endometrioid endometrial cancer

PURPOSE

The aim of this study was to evaluate the risk factors for pelvic lymph node metastasis (LNM) and para-aortic LNM in non-endometrioid endometrial cancer (non-EEC).

METHODS

A total of 283 patients with non-EEC hospitalized in the First Affiliated Hospital of Zhengzhou University from January 2012 to December 2020 were included. Various characteristics were retrospectively analyzed in relation to LNM.

RESULTS

Univariable and multivariable logistic regression analysis revealed cervical stromal invasion (OR = 3.441, 95% CI = 1.558-7.6, p = 0.002), myometrial invasion ≥1/2 (OR = 2.661, 95% CI = 1.327-5.337, p < 0.006), lymphovascular space involvement (LVSI) (OR = 4.118, 95% CI = 1.919-8.837, p < 0.001), positive peritoneal cytology (OR = 2.962, 95% CI = 1.344-6.530, p = 0.007), CA125 (OR = 1.002, 95% CI = 1-1.004, p = 0.026) were the independent risk factors for pelvic LNM. And myometrial invasion ≥1/2 (OR = 5.881, 95% CI = 2.056-16.427, p = 0.001), LVSI (OR = 4.962, 95% CI = 1.933-12.740, p = 0.001), adnexal (OR = 5.921, 95% CI = 2.003-17.502, p = 0.001) were the independent risk factors for para-aortic LNM. With the increase of independent risk factors, the rates of LNM were increased significantly.

CONCLUSIONS

Cervical stromal invasion, myometrial invasion ≥1/2, LVSI, positive peritoneal cytology, and CA125 were risk factors for pelvic LNM. Myometrial invasion ≥1/2, LVSI and involvement of the adnexa were risk factors for para-aortic LNM which could provide a good basis to help predict which non-EEC patients are at higher risk for LNM.

The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis

Background

The early identification of lymph node metastasis status in endometrial cancer (EC) is a serious challenge in clinical practice. Some investigators have introduced machine learning into the early identification of lymph node metastasis in EC patients. However, the predictive value of machine learning is controversial due to the diversity of models and modeling variables. To this end, we carried out this systematic review and meta-analysis to systematically discuss the value of machine learning for the early identification of lymph node metastasis in EC patients.

Methods

A systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science until March 12, 2023. PROBAST was used to assess the risk of bias in the included studies. In the process of meta-analysis, subgroup analysis was performed according to modeling variables (clinical features, radiomic features, and radiomic features combined with clinical features) and different types of models in various variables.

Results

This systematic review included 50 primary studies with a total of 103,752 EC patients, 12,579 of whom had positive lymph node metastasis. Meta-analysis showed that among the machine learning models constructed by the three categories of modeling variables, the best model was constructed by combining radiomic features with clinical features, with a pooled c-index of 0.907 (95%CI: 0.886-0.928) in the training set and 0.823 (95%CI: 0.757-0.890) in the validation set, and good sensitivity and specificity. The c-index of the machine learning model constructed based on clinical features alone was not inferior to that based on radiomic features only. In addition, logistic regression was found to be the main modeling method and has ideal predictive performance with different categories of modeling variables.

Conclusion

Although the model based on radiomic features combined with clinical features has the best predictive efficiency, there is no recognized specification for the application of radiomics at present. In addition, the logistic regression constructed by clinical features shows good sensitivity and specificity. In this context, large-sample studies covering different races are warranted to develop predictive nomograms based on clinical features, which can be widely applied in clinical practice.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023420774.

A Nomogram Based on Nutrition-Related Indicators and Computed Tomography Imaging Features for Predicting Preoperative Lymph Node Metastasis in Curatively Resected Esophagogastric Junction Adenocarcinoma

BACKGROUNDS

Preoperative noninvasive tools to predict pretreatment lymph node metastasis (PLNM) status accurately for esophagogastric junction adenocarcinoma (EJA) are few. Thus, the authors aimed to construct a nomogram for predicting PLNM in curatively resected EJA.

METHODS

This study enrolled 638 EJA patients who received curative surgery resection and divided them randomly (7:3) into training and validation groups. For nomogram construction, 26 candidate parameters involving 21 preoperative clinical laboratory blood nutrition-related indicators, computed tomography (CT)-reported tumor size, CT-reported PLNM, gender, age, and body mass index were screened.

RESULTS

In the training group, Lasso regression included nine nutrition-related blood indicators in the PLNM-prediction nomogram. The PLNM prediction nomogram yielded an area under the receiver operating characteristic (ROC) curve of 0.741 (95 % confidence interval [CI], 0.697-0.781), which was better than that of the CT-reported PLNM (0.635; 95% CI 0.588-0.680; p < 0.0001). Application of the nomogram in the validation cohort still gave good discrimination (0.725 [95% CI 0.658-0.785] vs 0.634 [95% CI 0.563-0.700]; p = 0.0042). Good calibration and a net benefit were observed in both groups.

CONCLUSIONS

This study presented a nomogram incorporating preoperative nutrition-related blood indicators and CT imaging features that might be used as a convenient tool to facilitate the preoperative individualized prediction of PLNM for patients with curatively resected EJA.

A novel nomogram based on clinical blood indicators for prognosis prediction in curatively resected esophagogastric junction adenocarcinoma patients

Background: The incidence of esophagogastric junction adenocarcinoma (EJA) patients was increasing but their prognoses were poor. Blood-based predictive biomarkers were associated with prognosis. This study was to build a nomogram based on preoperative clinical laboratory blood biomarkers for predicting prognosis in curatively resected EJA. Methods: Curatively resected EJA patients, recruited between 2003 and 2017 in the Cancer Hospital of Shantou University Medical College, were divided chronologically into the training (n=465) and validation groups (n=289). Fifty markers, involving sociodemographic characteristics and preoperative clinical laboratory blood indicators, were screened for nomogram construction. Independent predictive factors were selected using Cox regression analysis and then were combined to build a nomogram to predict overall survival (OS). Results: Composed of 12 factors, including age, body mass index, platelets, aspartate aminotransferase-to-alanine transaminase ratio, alkaline phosphatase, albumin, uric acid, IgA, IgG, complement C3, complement factor B and systemic immune-inflammation index, we constructed a novel nomogram for OS prediction. In the training group, when combined with TNM system, it acquired a C-index of 0.71, better than using TNM system only (C-index: 0.62, p < 0.001). When applied in the validation group, the combined C-index was 0.70, also better than using TNM system (C-index: 0.62, p < 0.001). Calibration curves exhibited that the nomogram-predicted probabilities of 5-year OS were both in consistency with the actual 5-year OS in both groups. Kaplan-Meier analysis exhibited that patients with higher nomogram scores contained poorer 5-year OS than those with lower scores (p < 0.0001). Conclusions: In conclusion, the novel nomogram built based on preoperative blood indicators might be the potential prognosis prediction model of curatively resected EJA.

Preoperative CA125 Significantly Improves Risk Stratification in High-Grade Endometrial Cancer

Patients with high-grade endometrial carcinoma (EC) have an increased risk of tumor spread and lymph node metastasis (LNM). Preoperative imaging and CA125 can be used in work-up. As data on cancer antigen 125 (CA125) in high-grade EC are limited, we aimed to study primarily the predictive value of CA125, and secondarily the contributive value of computed tomography (CT) for advanced stage and LNM. Patients with high-grade EC (n = 333) and available preoperative CA125 were included retrospectively. The association of CA125 and CT findings with LNM was analyzed by logistic regression. Elevated CA125 ((>35 U/mL), (35.2% (68/193)) was significantly associated with stage III-IV disease (60.3% (41/68)) compared with normal CA125 (20.8% (26/125), [p < 0.001]), and with reduced disease-specific-(DSS) (p < 0.001) and overall survival (OS) (p < 0.001). The overall accuracy of predicting LNM by CT resulted in an area under the curve (AUC) of 0.623 (p < 0.001) independent of CA125. Stratification by CA125 resulted in an AUC of 0.484 (normal), and 0.660 (elevated). In multivariate analysis elevated CA125, non-endometrioid histology, pathological deep myometrial invasion ≥50%, and cervical involvement were significant predictors of LNM, whereas suspected LNM on CT was not. This shows that elevated CA125 is a relevant independent predictor of advanced stage and outcome specifically in high-grade EC.

Clinical Significance of Lymphatic Infiltration Detected by Immunohistochemical Double Staining in Patients with Endometrial Cancer

Background

The presence of lymph-vascular space invasion is a powerful predictor of lymph node metastasis. However, most studies do not distinguish lymph vessel invasion (LVI) and blood vessel invasion (BVI). The aim of this study was to distinguish the role of LVI and BVI in lymphatic metastasis and recurrence in patients with endometrial cancer.

Methods

We examined 171 patients with endometrial cancer. Immunohistochemical double staining was used to distinguish lymphatic invasion and vascular invasion. First, the relationship between lymphatic/vascular invasion and clinicopathological features and lymphatic metastasis was studied. Then, the expression of D2-40/LVI and CD31/BVI in patients with recurrence was analyzed.

Results

Pathological grading (G3) and D2-40/LVI were independent high-risk factors for lymph node metastasis of endometrial cancer. The area under the receiver operating characteristic curve values for predicting lymphatic metastasis using pathological grading (G3) or D2-40/LVI alone were .642 and .680, respectively, and the area under the curve value for the combined detection of pathological grading (G3) and D2-40/LVI was .726, which was greater than the values obtained for the abovementioned independent variables. Among the 15 recurrent patients, 5 (33.3%) were D2-40/LVI positive, 2 (13.3%) were CD31/BVI positive, and 8 (53.3%) were both D2-40/LVI and CD31/BVI positive.

Conclusion

D2-40/LVI combined with G3 can effectively predict lymph node metastasis of endometrial carcinoma.

Predictive model for the preoperative assessment and prognostic modeling of lymph node metastasis in endometrial cancer

Lymph node metastasis (LNM) is a well-established prognostic factor in endometrial cancer (EC). We aimed to construct a model that predicts LNM and prognosis using preoperative factors such as myometrial invasion (MI), enlarged lymph nodes (LNs), histological grade determined by endometrial biopsy, and serum cancer antigen 125 (CA125) level using two independent cohorts consisting of 254 EC patients. The area under the receiver operating characteristic curve (AUC) of the constructed model was 0.80 regardless of the machine learning techniques. Enlarged LNs and higher serum CA125 levels were more significant in patients with low-grade EC (LGEC) and LNM than in patients without LNM, whereas deep MI and higher CA125 levels were more significant in patients with high-grade EC (HGEC) and LNM than in patients without LNM. The predictive performance of LNM in the HGEC group was higher than that in the LGEC group (AUC = 0.84 and 0.75, respectively). Patients in the group without postoperative pathological LNM and positive LNM prediction had significantly worse relapse-free and overall survival than patients with negative LNM prediction (log-rank test, P < 0.01). This study showed that preoperative clinicopathological factors can predict LNM with high precision and detect patients with poor prognoses. Furthermore, clinicopathological factors associated with LNM were different between HGEC and LGEC patients.

Feasibility and clinical applicability of genomic profiling based on cervical smear samples in patients with endometrial cancer

Purpose

Cervical smear samples are easily obtainable and may effectively reflect the tumor microenvironment in gynecological cancers. Therefore, we investigated the feasibility of genomic profiling based on tumor DNA analysis from cervical smear samples from endometrial cancer patients.

Materials and methods

Preoperative cervical smear samples were obtained via vaginal sampling in 50 patients, including 39 with endometrial cancer and 11 with benign uterine disease. Matched blood samples were obtained simultaneously. Genomic DNA (gDNA) from cervical smear and/or cell-free DNA from whole blood were extracted and sequenced using the Pan100 panel covering 100 endometrial cancer-related genes.

Results

Cervical swab-based gDNA analysis detected cancer with 67% sensitivity and 100% specificity, showing a superior performance compared to that of the matched blood or Pap smear tests. Cervical swab-based gDNA effectively identified patients with loss of MSH2 or MSH6 and aberrant p53 expression based on immunohistochemistry. Genomic landscape analysis of cervical swab-based gDNA identified PTEN, PIK3CA, TP53, and ARID1A as the most frequently altered genes. Furthermore, 26 endometrial cancer patients could be classified according to the Proactive Molecular Risk Classifier for Endometrial Cancer.

Conclusion

Cervical swab-based gDNA test showed an improved detection potential and allowed the classification of patients, which has both predictive and prognostic implications.

Diagnostic Accuracy of FEC-PET/CT, FDG-PET/CT, and Diffusion-Weighted MRI in Detection of Nodal Metastases in Surgically Treated Endometrial and Cervical Carcinoma

PURPOSE

Preoperative nodal staging is important for planning treatment in cervical cancer and endometrial cancer, but remains challenging. We compare nodal staging accuracy of ¹⁸F-ethyl-choline-(FEC)-PET/CT, ¹⁸F-fluoro-deoxy-glucose-(FDG)-PET/CT, and diffusion-weighted-MRI (DW-MRI) with conventional morphologic MRI.

EXPERIMENTAL DESIGN

A prospective, multicenter observational study of diagnostic accuracy for nodal metastases was undertaken in 5 gyne-oncology centers. FEC-PET/CT, FDG-PET/CT, and DW-MRI were compared with nodal size and morphology on MRI. Reference standard was strictly correlated nodal histology. Eligibility included operable cervical cancer stage ≥ 1B1 or endometrial cancer (grade 3 any stage with myometrial invasion or grade 1-2 stage ≥ II).

RESULTS

Among 162 consenting participants, 136 underwent study DW-MRI and FDG-PET/CT and 60 underwent FEC-PET/CT. In 118 patients, 267 nodal regions were strictly correlated at histology (nodal positivity rate, 25%). Sensitivity per patient (n = 118) for nodal size, morphology, DW-MRI, FDG- and FEC-PET/CT was 40%*, 53%, 53%, 63%*, and 67% for all cases (*, P = 0.016); 10%, 10%, 20%, 30%, and 25% in cervical cancer (n = 40); 65%, 75%, 70%, 80% and 88% in endometrial cancer (n = 78). FDG-PET/CT outperformed nodal size (P = 0.006) and size ratio (P = 0.04) for per-region sensitivity. False positive rates were all <10%.

CONCLUSIONS

All imaging techniques had low sensitivity for detection of nodal metastases and cannot replace surgical nodal staging. The performance of FEC-PET/CT was not statistically different from other techniques that are more widely available. FDG-PET/CT had higher sensitivity than size in detecting nodal metastases. False positive rates were low across all methods. The low false positive rate demonstrated by FDG-PET/CT may be helpful in arbitration of challenging surgical planning decisions.

Risk Stratification of Endometrial Cancer Patients: FIGO Stage, Biomarkers and Molecular Classification

Endometrial cancer (EC) is the most common gynaecologic malignancy in developed countries. The main challenge in EC management is to correctly estimate the risk of metastases at diagnosis and the risk to develop recurrences in the future. Risk stratification determines the need for surgical staging and adjuvant treatment. Detection of occult, microscopic metastases upstages patients, provides important prognostic information and guides adjuvant treatment. The molecular classification subdivides EC into four prognostic subgroups: POLE ultramutated; mismatch repair deficient (MMRd); nonspecific molecular profile (NSMP); and TP53 mutated (p53abn). How surgical staging should be adjusted based on preoperative molecular profiling is currently unknown. Moreover, little is known whether and how other known prognostic biomarkers affect prognosis prediction independent of or in addition to these molecular subgroups. This review summarizes the factors incorporated in surgical staging (i.e., peritoneal washing, lymph node dissection, omentectomy and peritoneal biopsies), and its impact on prognosis and adjuvant treatment decisions in an era of molecular classification of EC. Moreover, the relation between FIGO stage and molecular classification is evaluated including the current gaps in knowledge and future perspectives.

Immunohistochemical biomarkers are prognostic relevant in addition to the ESMO-ESGO-ESTRO risk classification in endometrial cancer

OBJECTIVE

Pre-operative immunohistochemical (IHC) biomarkers are not incorporated in endometrial cancer (EC) risk classification. We aim to investigate the added prognostic relevance of IHC biomarkers to the ESMO-ESGO-ESTRO risk classification and lymph node (LN) status in EC.

METHODS

Retrospective multicenter study within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), analyzing pre-operative IHC expression of p53, L1 cell-adhesion molecule (L1CAM), estrogen receptor (ER) and progesterone receptor (PR), and relate to ESMO-ESGO-ESTRO risk groups, LN status and outcome.

RESULTS

A total of 763 EC patients were included with a median follow-up of 5.5-years. Abnormal IHC expression was present for p53 in 112 (14.7%), L1CAM in 79 (10.4%), ER- in 76 (10.0%), and PR- in 138 (18.1%) patients. Abnormal expression of p53/L1CAM/ER/PR was significantly related with higher risk classification groups, and combined associated with the worst outcome within the 'high and advanced/metastatic' risk group. In multivariate analysis p53-abn, ER/PR- and ESMO-ESGO-ESTRO 'high and advanced/metastatic' were independently associated with reduced disease-specific survival (DSS). Patients with abnormal IHC expression and lymph node metastasis (LNM) had the worst outcome. Patients with LNM and normal IHC expression had comparable outcome with patients without LNM and abnormal IHC expression.

CONCLUSION

The use of pre-operative IHC biomarkers has important prognostic relevance in addition to the ESMO-ESGO-ESTRO risk classification and in addition to LN status. For daily clinical practice, p53/L1CAM/ER/PR expression could serve as indicator for surgical staging and refine selective adjuvant treatment by incorporation into the ESMO-ESGO-ESTRO risk classification.

Computer-Aided Segmentation and Machine Learning of Integrated Clinical and Diffusion-Weighted Imaging Parameters for Predicting Lymph Node Metastasis in Endometrial Cancer

Simple Summary

Computer-aided segmentation and machine learning added values of clinical parameters and diffusion-weighted imaging radiomics for predicting nodal metastasis in endometrial cancer, with a diagnostic performance superior to criteria based on lymph node size or apparent diffusion coefficient.

Abstract

Precise risk stratification in lymphadenectomy is important for patients with endometrial cancer (EC), to balance the therapeutic benefit against the operation-related morbidity and mortality. We aimed to investigate added values of computer-aided segmentation and machine learning based on clinical parameters and diffusion-weighted imaging radiomics for predicting lymph node (LN) metastasis in EC. This prospective observational study included 236 women with EC (mean age ± standard deviation, 51.2 ± 11.6 years) who underwent magnetic resonance (MR) imaging before surgery during July 2010–July 2018, randomly split into training (n = 165) and test sets (n = 71). A decision-tree model was constructed based on mean apparent diffusion coefficient (ADC) value of the tumor (cutoff, 1.1 × 10⁻³ mm²/s), skewness of the relative ADC value (cutoff, 1.2), short-axis diameter of LN (cutoff, 1.7 mm) and skewness ADC value of the LN (cutoff, 7.2 × 10⁻²), as well as tumor grade (1 vs. 2 and 3), and clinical tumor size (cutoff, 20 mm). The sensitivity and specificity of the model were 94% and 80% for the training set and 86%, 78% for the independent testing set, respectively. The areas under the receiver operating characteristics curve (AUCs) of the decision-tree was 0.85—significantly higher than the mean ADC model (AUC = 0.54) and LN short-axis diameter criteria (AUC = 0.62) (both p < 0.0001). We concluded that a combination of clinical and MR radiomics generates a prediction model for LN metastasis in EC, with diagnostic performance surpassing the conventional ADC and size criteria.

Prediction of Lymph-Node Metastasis in Cancers Using Differentially Expressed mRNA and Non-coding RNA Signatures

Accurate prediction of lymph-node metastasis in cancers is pivotal for the next targeted clinical interventions that allow favorable prognosis for patients. Different molecular profiles (mRNA and non-coding RNAs) have been widely used to establish classifiers for cancer prediction (e.g., tumor origin, cancerous or non-cancerous state, cancer subtype). However, few studies focus on lymphatic metastasis evaluation using these profiles, and the performance of classifiers based on different profiles has also not been compared. Here, differentially expressed mRNAs, miRNAs, and lncRNAs between lymph-node metastatic and non-metastatic groups were identified as molecular signatures to construct classifiers for lymphatic metastasis prediction in different cancers. With this similar feature selection strategy, support vector machine (SVM) classifiers based on different profiles were systematically compared in their prediction performance. For representative cancers (a total of nine types), these classifiers achieved comparative overall accuracies of 81.00% (67.96-92.19%), 81.97% (70.83-95.24%), and 80.78% (69.61-90.00%) on independent mRNA, miRNA, and lncRNA datasets, with a small set of biomarkers (6, 12, and 4 on average). Therefore, our proposed feature selection strategies are economical and efficient to identify biomarkers that aid in developing competitive classifiers for predicting lymph-node metastasis in cancers. A user-friendly webserver was also deployed to help researchers in metastasis risk determination by submitting their expression profiles of different origins.

HE4 and CA125 as preoperative risk stratifiers for lymph node metastasis in endometrioid carcinoma of the endometrium: A retrospective study in a cohort with histological proof of lymph node status

OBJECTIVES

To investigate whether HE4 and CA125 could identify endometrioid adenocarcinoma patients who might most benefit from full staging surgery with lymphadenectomy.

METHODS

Sequential patients with a preoperative banked serum and histology of endometrioid adenocarcinoma of endometrium who had undergone surgical staging with lymph node dissection over a 5-year period between 2011 and 2016 were included from a tertiary Gynaecological Cancer Centre, Dublin, Ireland. Preoperative serum HE4 and CA125 were measured using ELISA, with the cut-offs HE4 81 pmol/L and CA125 35 U/ml. Predictive values were estimated using AUC, sensitivity, specificity and odds ratios.

RESULTS

9.5% of the cohort had lymph node metastases. A HE4 cut-off of 81 pmol/L yielded a sensitivity of 78.6% and specificity of 53.4% for predicting lymph node metastases. Sensitivity of CA125 at 35 U/ml was 57% and specificity 91.4%. The AUC was 0.66 (0.52-0.80) for HE4 and 0.74 (0.58-0.91) for CA125. Sensitivity was 92.8% and specificity 51.1% when an elevation of either HE4 or CA125 was included, AUC was 0.72 (0.61-0.83), this combination yielded the highest NPV of 98.6%. Sensitivity was 42.9% and specificity 93.8% if both markers were elevated simultaneously, AUC was 0.68 (0.51-0.86). Preoperative clinical predictors of high-grade preoperative histology and radiology had sensitivities of 21.4% and 41.7%, respectively. Patients with a HE4 above 81 pmol/L had an odds ratio of 4.2 (1.12-15.74), p < 0.05, of lymph node metastases and CA125 had an odds ratio of 14.2 (4.16-48.31), p < 0.001.

CONCLUSIONS

Serum HE4 and CA125 improved on existing methods for risk stratification of endometrioid carcinomas and warrant further investigation.

Whole-Volume Tumor MRI Radiomics for Prognostic Modeling in Endometrial Cancer

Background

In endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, while final tumor stage and grade are established by surgery and pathology. MRI‐based radiomic tumor profiling may aid in preoperative risk‐stratification and support clinical treatment decisions in EC.

Purpose

To develop MRI‐based whole‐volume tumor radiomic signatures for prediction of aggressive EC disease.

Study Type

Retrospective.

Population

A total of 138 women with histologically confirmed EC, divided into training (n_T = 108) and validation cohorts (n_V = 30).

Field Strength/Sequence

Axial oblique T₁‐weighted gradient echo volumetric interpolated breath‐hold examination (VIBE) at 1.5T (71/138 patients) and DIXON VIBE at 3T (67/138 patients) at 2 minutes postcontrast injection.

Assessment

Primary tumors were manually segmented by two radiologists with 4 and 8 years' of experience. Radiomic tumor features were computed and used for prediction of surgicopathologically‐verified deep (≥50%) myometrial invasion (DMI), lymph node metastases (LNM), advanced stage (FIGO III + IV), nonendometrioid (NE) histology, and high‐grade endometrioid tumors (E3). Corresponding analyses were also conducted using radiomics extracted from the axial oblique image slice depicting the largest tumor area.

Statistical Tests

Logistic least absolute shrinkage and selection operator (LASSO) was applied for radiomic modeling in the training cohort. The diagnostic performances of the radiomic signatures were evaluated by area under the receiver operating characteristic curve in the training (AUC_T) and validation (AUC_V) cohorts. Progression‐free survival was assessed using the Kaplan–Meier and Cox proportional hazard model.

Results

The whole‐tumor radiomic signatures yielded AUC_T/AUC_V of 0.84/0.76 for predicting DMI, 0.73/0.72 for LNM, 0.71/0.68 for FIGO III + IV, 0.68/0.74 for NE histology, and 0.79/0.63 for high‐grade (E3) tumor. Single‐slice radiomics yielded comparable AUC_T but significantly lower AUC_V for LNM and FIGO III + IV (both P < 0.05). Tumor volume yielded comparable AUC_T to the whole‐tumor radiomic signatures for prediction of DMI, LNM, FIGO III + IV, and NE, but significantly lower AUC_T for E3 tumors (P < 0.05). All of the whole‐tumor radiomic signatures significantly predicted poor progression‐free survival with hazard ratios of 4.6–9.8 (P < 0.05 for all).

Data Conclusion

MRI‐based whole‐tumor radiomic signatures yield medium‐to‐high diagnostic performance for predicting aggressive EC disease. The signatures may aid in preoperative risk assessment and hence guide personalized treatment strategies in EC.

Level of Evidence

4

Technical Efficacy Stage

2

Mammaglobin B may be a prognostic biomarker of uterine corpus endometrial cancer

Mammaglobin B, also referred to as secretoglobin family 2A member 1 (SCGB2A1), has been reported to be highly expressed in uterine corpus endometrial cancer (UCEC) compared with in the normal endometrium. However, the prognostic value of SCGB2A1 in UCEC remains unclear. The Oncomine, The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium databases were used to explore the differential expression of SCGB2A1. Furthermore, data of patients with UCEC were downloaded from TCGA, and logistic regression analysis, survival analysis, univariate and multivariate analyses, and nomogram construction were performed to identify its prognostic value in UCEC. Additionally, gene set enrichment analysis (GSEA) was utilized to estimate the mechanisms of SCGB2A1 in UCEC. Finally, immune infiltration of SCGB2A1 in UCEC was analyzed using the Tumor Immune Estimation Resource. Decreased mRNA and protein expression levels of SCGB2A1 were significantly associated with poor prognostic clinicopathological characteristics (all P<0.05). Additionally, low expression levels of SCGB2A1 were associated with decreased survival of patients with UCEC compared with high expression levels of SCGB2A1. Furthermore, the independent prognostic value of SCGB2A1 in UCEC was identified by univariate and multivariate analyses. A nomogram based on 6 variables, including SCGB2A1 expression, was developed for the estimation of the 1-, 3-, and 5-year survival probability in UCEC. Additionally, GSEA suggested that the vascular endothelial growth factor, PTEN, platelet-derived growth factor, DNA repair, KRAS signaling, and PI3K-AKT-mTOR signaling pathways were differentially enriched in the low SCGB2A1 expression phenotype. Finally, high infiltration levels of CD8⁺ T cells were associated with SCGB2A1 in UCEC and this was associated with prognosis. The present results indicated that SCGB2A1 may be a promising independent prognostic factor in UCEC. These signaling pathways may be crucial for the regulation of UCEC via SCGB2A1.

The apparent diffusion coefficient (ADC) on 3-T MRI differentiates myometrial invasion depth and histological grade in patients with endometrial cancer

BACKGROUND

Diffusion-weighted magnetic resonance imaging (DW-MRI) with apparent diffusion coefficient (ADC) measurement provides additional information about tumor microstructure with potential relevance for staging and predicting aggressive disease in patients with endometrial cancer (EC).

PURPOSE

To determine whether ADC values in EC diverge according to the tumor's histologic grade and myometrial invasion depth.

MATERIAL AND METHODS

A sample of 48 pathologically confirmed cases of EC were reviewed retrospectively. The sample was distributed as follows: G1 (n = 9); G2 (n = 18); G3 (n = 21); with myometrial invasion <50% (n = 31); and with myometrial invasion ≥50% (n = 17). DW images were performed at 3.0T with b factors of 0-1000/mm². The region of interest (ROI) was defined within the tumor with T1-weighted and T2-weighted imaging and copied manually to an ADC map. The tumor's grade and myometrial invasion's depth were determined by postoperative histopathological tests.

RESULTS

The means of ADC_min and ADC_mean values were significantly lower for patients with G2 and G3 endometrial tumors than G1. The same tendency was observed in myometrial invasion, as both ADC_min and ADC_mean values were lower for patients with deep than for those with superficial myometrial invasion. The cut-off values of the ADC_min and ADC_mean that predicted high-grade tumors were 0.69 × 10^-3 mm²/s and 0.82 × 10^-3 mm²/s, respectively, while those for myometrial infiltration were 0.70 × 10^-3 mm²/s (ADC_min) and 0.88 × 10^-3 mm²/s (ADC_mean).

CONCLUSION

ADC_min and ADC_mean values correlated with histologic tumor grade and myometrial invasion depth; therefore, it is suggested that ADC on MRI may be a useful indicator to predict malignancy of ECs.

Establishment and evaluation of a risk-scoring system for lymph node metastasis in early-stage endometrial carcinoma: Achieving preoperative risk stratification

AIM

To establish a risk-scoring system for lymph node metastasis (LNM) of early-stage endometrial carcinoma (EC), and to stratify the preoperative risk of LNM.

METHODS

We retrospectively analyzed the clinical data of 507 patients diagnosed with the early-stage EC (i.e., confined to the uterine corpus). We determined the risk factors for LNM by logistic regression analysis; then constructed a simple logistic scoring system, and an additive scoring system based on the regression coefficient (β), and odds ratio, of each variable, respectively.

RESULTS

The overall rate of LNM was 9.1% (46/507). Multivariate analysis showed that preoperative serum cancer antigen 125 (CA125) ≥35 U/mL, histopathology of grade 3 and/or type II, depth of myometrial invasion ≥1/2 and positive immunostaining for Ki-67 ≥50%, were independent risk factors for LNM (P < 0.05). The simple logistic and additive scoring systems exhibited good predictive ability (area under the curve [AUC] >0.8). Based on the additive scoring system, the risk of LNM in patients with early-stage EC was classified into three groups: a low-risk group (total score: <5), an intermediate-risk group (total score: 5-10) and a high-risk group (total score: >10). The incidence of LNM differed significantly across these three groups (P < 0.05).

CONCLUSION

The risk-scoring system constructed in this study can effectively predict the risk of LNM in patients with early-stage EC, achieve preoperative risk stratification and provide a reference guideline for the use of lymphadenectomy.

Decreased secretoglobin family 2A member 1expression is associated with poor outcomes in endometrial cancer

Endometrial cancer is the most common malignancies in developed countries. The present study aimed to identify the role of secretoglobin family 2A member 1 (SCGB2A1) expression in uteri corpus endometrial carcinoma (UCEC) from The Cancer Genome Atlas (TCGA) database, and determine the SCGB2A1-associated downstream signaling pathways. The clinicopathological characteristics and gene expression data were downloaded from TCGA database. The Kaplan-Meier method and Cox multivariate model were used for survival analysis. Logistic regression was used to analyze the association between the clinicopathological features and SCGB2A1 expression. For validation, data of SCGB2A1 mRNA expression and protein expression were obtained and then survival analysis was performed for 47 patients with endometrial cancer from the Fudan University Shanghai Cancer Center (FUSCC). In TCGA dataset, SCGB2A1 expression was significantly higher in tumor tissues (n=528) compared with normal tissues (n=23, P<0.001). The decrease in SCGB2A1 expression in UCEC was significantly associated with age at diagnosis, high tumor grade, residual tumor, positive peritoneal cytology, pelvic lymph node metastasis, para-aortic lymph node metastasis and advanced clinical stage with P<0.05. In the multivariate analysis, SCGB2A1 expression was identified as an independent prognostic factor. In the FUSCC validation set, low SCGB2A1 expression was also associated with worse survival compared with high expression in endometrial cancer (P<0.001). Gene Set Enrichment Analysis revealed that SCGB2A1 may be involved in tumor proliferation and cell cycle regulation. In conclusion, SCGB2A1 may have an important role in the prognosis of UCEC, and has value as a new target for novel therapeutic strategies.

Fertility-sparing treatment in early endometrial cancer: current state and future strategies

Endometrial cancer (EC) is the fifth most common cancer in women worldwide. Global estimates show rising incidence rates in both developed and developing countries. Most women are diagnosed postmenopausal, but 14–25% of patients are premenopausal and 5% are under 40 years of age. Established risk factors include age and hyperestrogenic status associated with nulliparity, obesity, and metabolic syndrome. Standard treatment for EC, which involves total hysterectomy and bilateral salpingo-oophorectomy, has excellent survival outcomes, particularly for low-grade endometrioid tumors. However, it leads to permanent loss of fertility among women who wish to preserve their reproductive potential. With current trends of reproductive-age women delaying childbearing, rising EC incidence rates, and a growing epidemic of obesity, particularly in developed countries, research on conservative non-surgical treatment approaches remains a top priority. Fertility-sparing treatment predominantly involves the use of oral progestins and levonorgestrel-releasing intrauterine devices, which have been shown to be feasible and safe in women with early stage EC and minimal or no myometrial invasion. However, data on the efficacy and safety of conservative management strategies are primarily based on retrospective studies. Randomized clinical trials in younger women and high-risk obese patients are currently underway. Here, we have presented a comprehensive review of the current literature on conservative, fertility-sparing approaches, defining the optimal candidates and evaluating tumor characteristics, reproductive and oncologic outcomes, and ongoing clinical trials. We have also summarized current guidelines and recommendations based on the published literature.

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

BACKGROUND

Bayesian 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 FINDINGS

Within 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.

CONCLUSIONS

In 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.

Imaging of Preclinical Endometrial Cancer Models for Monitoring Tumor Progression and Response to Targeted Therapy

Endometrial cancer is the most common gynecologic malignancy in industrialized countries. Most patients are cured by surgery; however, about 15% of the patients develop recurrence with limited treatment options. Patient-derived tumor xenograft (PDX) mouse models represent useful tools for preclinical evaluation of new therapies and biomarker identification. Preclinical imaging by magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), single-photon emission computed tomography (SPECT) and optical imaging during disease progression enables visualization and quantification of functional tumor characteristics, which may serve as imaging biomarkers guiding targeted therapies. A critical question, however, is whether the in vivo model systems mimic the disease setting in patients to such an extent that the imaging biomarkers may be translatable to the clinic. The primary objective of this review is to give an overview of current and novel preclinical imaging methods relevant for endometrial cancer animal models. Furthermore, we highlight how these advanced imaging methods depict pathogenic mechanisms important for tumor progression that represent potential targets for treatment in endometrial cancer.