Lymph Node Metastasis In Endometrial Cancer Research Articles

Preoperative prediction of lymph node metastasis in endometrial cancer patients via an intratumoral and peritumoral multiparameter MRI radiomics nomogram

IntroductionWhile lymph node metastasis (LNM) plays a critical role in determining treatment options for endometrial cancer (EC) patients, the existing preoperative methods for evaluating the lymph node state are not always satisfactory. This study aimed to develop and validate a nomogram based on intra- and peritumoral radiomics features and multiparameter magnetic resonance imaging (MRI) features to preoperatively predict LNM in EC patients.MethodsThree hundred and seventy-four women with histologically confirmed EC were divided into training (n = 220), test (n = 94), and independent validation (n = 60) cohorts. Radiomic features were extracted from intra- and peritumoral regions via axial T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) mapping. A radiomics model (annotated as the Radscore) was established using the selected features from different regions. The clinical parameters were statistically analyzed. A nomogram was developed by combining the Radscore and the most predictive clinical parameters. Decision curve analysis (DCA) and the net reclassification index (NRI) were used to assess the clinical benefit of using the nomogram.ResultsNine radiomics features were ultimately selected from the intra- and peritumoral regions via ADC mapping and T2WI. The nomogram combining the Radscore, serum CA125 level, and tumor area ratio achieved the highest AUCs in the training, test and independent validation sets (nomogram vs. Radscore vs. clinical model: 0.878 vs. 0.850 vs. 0.674 (training), 0.877 vs. 0.838 vs. 0.668 (test), and 0.864 vs. 0.836 vs. 0.618 (independent validation)). The DCA and NRI results revealed the nomogram had greater diagnostic performance and net clinical benefits than the Radscore alone.ConclusionThe combined intra- and peritumoral region multiparameter MRI radiomics nomogram showed good diagnostic performance and could be used to preoperatively predict LNM in patients with EC.

Nodal infiltration in endometrial cancer: a prediction model using best subset regression.

To build preoperative prediction models with and without MRI for regional lymph node metastasis (r-LNM, pelvic and/or para-aortic LNM (PENM/PANM)) and for PANM in endometrial cancer using established risk factors. In this retrospective two-center study, 364 patients with endometrial cancer were included: 253 in the model development and 111 in the external validation. For r-LNM and PANM, respectively, best subset regression with ten-time fivefold cross validation was conducted using ten established risk factors (4 clinical and 6 imaging factors). Models with the top 10 percentile of area under the curve (AUC) and with the fewest variables in the model development were subjected to the external validation (11 and 4 candidates, respectively, for r-LNM and PANM). Then, the models with the highest AUC were selected as the final models. Models without MRI findings were developed similarly, assuming the cases where MRI was not available. The final r-LNM model consisted of pelvic lymph node (PEN) ≥ 6mm, deep myometrial invasion (DMI) on MRI, CA125, para-aortic lymph node (PAN) ≥ 6mm, and biopsy; PANM model consisted of DMI, PAN, PEN, and CA125 (in order of correlation coefficient β values). The AUCs were 0.85 (95%CI: 0.77-0.92) and 0.86 (0.75-0.94) for the external validation, respectively. The model without MRI for r-LNM and PANM showed AUC of 0.79 (0.68-0.89) and 0.87 (0.76-0.96), respectively. The prediction models created by best subset regression with cross validation showed high diagnostic performance for predicting LNM in endometrial cancer, which may avoid unnecessary lymphadenectomies. The prediction risks of lymph node metastasis (LNM) and para-aortic LNM can be easily obtained for all patients with endometrial cancer by inputting the conventional clinical information into our models. They help in the decision-making for optimal lymphadenectomy and personalized treatment. •Diagnostic performance of lymph node metastases (LNM) in endometrial cancer is low based on size criteria and can be improved by combining with other clinical information. •The optimized logistic regression model for regional LNM consists of lymph node ≥ 6mm, deep myometrial invasion, cancer antigen-125, and biopsy, showing high diagnostic performance. •Our model predicts the preoperative risk of LNM, which may avoid unnecessary lymphadenectomies.

Validation of Sentinel Lymph Node Biopsy in Robotic Endometrial Cancer Staging Surgery: Results From a High-Volume Center in India

PURPOSE Lymph node involvement is one of the most important factors influencing recurrence and survival in patients with endometrial cancer (EC). However, the therapeutic role of lymphadenectomy in early-stage disease has been called into question. Sentinel lymph node (SLN) mapping may be an acceptable alternative to omitting lymphadenectomy or performing a complete lymphadenectomy in patients with EC. To validate SLN biopsy (SLNB) using indocyanine green (ICG) dye and near-infrared imaging in the background of comprehensive lymphadenectomy in patients with EC undergoing robotic staging surgery at Tata Medical Center. METHODS This was a single-center, prospective observational study involving patients with EC undergoing robotic staging. Patients received a standardized cervical injection of ICG at the 3- and 9-o'clock positions, with the dye reinjected if mapping failed. Depending on preoperative histology and radiological staging, patients had SLNB or comprehensive systematic lymphadenectomy in addition to SLNB. RESULTS The study included 105 female patients, of whom 71 underwent SLN and full lymphadenectomy and 34 underwent only SLN. There was bilateral mapping in 92 (87.61%) patients, with no mapping in one patient. In 18 patients, ICG dye was reinjected. With the exception of one, the rest had successful mapping after reinjection. The sensitivity of the SLN-ICG algorithm was 92.3%, and the negative predictive value was 98.3%. Ultrastaging necessitated upstaging in 8.57% of patients. CONCLUSION With a very high negative predictive value, SLN mapping with ICG dye has a high degree of diagnostic accuracy in detecting lymph node metastases in EC.

Study of endometrial carcinoma on correlation between molecular typing and lymph node metastasis and construction of prediction model.

e17621 Background: TCGA molecular typing was proposed by The Cancer Genome Atlas (TCGA) in 2013 and recommended by the fifth edition of WHO, which has a strong prognositic value and is included in the risk stratification system, clinical diagnosis and treatment decision-making system of endometrial cancer (EC) patients. Meanwhile, Lymph node management in patients with EC is still controversial. Lymph node dissection (LND) and sentinel lymph node biopsy (SLNB) have their own advantages and limitations. This study attempts to establish a predictive model for lymph node metastasis (LNM) in patients with EC based on molecular typing and other clinical features and establish risk stratification, in order to provide a feasible method for predicting LNM in EC. Methods: In this study, SPSS19, R 4.2.1 and python 3.6 were used to establish a nomogram and evaluate it through a number of indicators, and the model was repeatedly verified by machine learning algorithms. With LNM as the outcome, risk stratification was set through the cut-off value of nomogram score to guide clinical treatment. Finally, an online LNM prediction website was designed to help patients. Results: A total of 325 EC patients were included, 35 (10.8%) with LNM and 290 (89.2%) without LNM. CA125 and cervical involvement were significantly correlated with LNM (p < 0.001), molecular typing, neutrophil to lymphocyte ratio (NLR), menstrual status were associated with LNM (p < 0.05). Lasso analysis screened out four independent factors: molecular typing, CA125, cervical involvement and menstrual status. The nomogram model was established by logistics regression (LR), with the prediction accuracy of the training set and validation set of 0.890 and 0.886, respectively. Machine learning algorithms in the training set also validates the model. Meanwhile, a risk stratification is established by the cut-off value of nomogram. We define high-risk group of LNM with more than the cut-off value and LND is recommended, while below the value is low-risk group and SLNB is recommended. Conclusions: We have established a nomogram clinical prediction model incorporating four factors, practicing stratified medical treatment about LND and SLNB, and formed a website for LNM prediction, genuinely providing patients recommendations.

A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer.

The current study aimed to develop a deep learning (DL) model for prediction of lymph node metastasis (LNM) based on hematoxylin and eosin (HE)-stained histopathological images of endometrial cancer (EC). The model was validated using external data. A total of 2,104 whole slide image (WSI) from 564 patients with pathologically confirmed LNM status were collated from West China Second University Hospital. An artificial intelligence (AI) model was built on the multiple instance-learning (MIL) framework for automatic prediction of the probability of LNM and its performance compared with "Mayo criteria". An additional external data source comprising 533 WSI was collected from two independent medical institutions to validate the model's robustness. Heatmaps were generated to demonstrate regions of the WSI that made the greatest contributions to the DL network output to improve understanding of these processes. The proposed MIL model achieved an area under the curve (AUC) of 0.938, a sensitivity of 0.830 and a specificity of 0.911 for LNM prediction to EC. The AUC according to Mayo criteria was 0.666 for the same test dataset. For types I, II and mixed EC, AUCs were 0.927, 0.979 and 0.929, respectively. The predictive performance of the MIL model also achieved an AUC of 0.921 for early staging. In external validation data, the proposed model achieved an AUC of 0.770, a sensitivity of 0.814 and a specificity of 0.520 for LNM prediction. AUCs were 0.783 for type I and 0.818 for early stage EC. The proposed MIL model generated from histopathological images of EC has a much better LNM predictive performance than that of Mayo criteria. A novel DL-based biomarker trained on different histological subtypes of EC slides was revealed to predict metastatic status with improved accuracy, especially for early staging patients. The current study proves the concept of MIL-based prediction of LNM in EC for the first time, and brought a new sight to improve the accuracy of LNM prediction. Multicenter prospective validation data is required to further confirm the clinical utility.

Incorporation of Tumor-Free Distance and Other Alternative Ultrasound Biomarkers into a Myometrial Invasion-Based Model Better Predicts Lymph Node Metastasis in Endometrial Cancer: Evidence and Future Prospects.

Myometrial invasion (MI) is a parameter currently used in transvaginal ultrasound (TVS) in endometrial cancer (EC) to determine local staging; however, without molecular diagnostics, it is insufficient for the selection of high-risk cases, i.e., those with a high risk of lymph node metastases (LNM). The study’s objective was to answer the question of which TVS markers, or their combination, reflecting the molecular changes in EC, can improve the prediction of LNM. Methods: The TVS examination was performed on 116 consecutive EC patients included in this prospective study. The results from the final histopathology were a reference standard. Univariate and multivariate logistic models of analyzed TVS biomarkers (tumor [T] size, T area [AREA], T volume [SPE-VOL], MI, T-free distance to serosa [TFD], endo-myometrial irregularity, [EMIR], cervical stromal involvement, CSI) were evaluated to assess the relative accuracy of the possible LNM predictors., Spline functions were applied to avoid a potential bias in assuming linear relations between LNM and continuous predictors. Calculations were made in R using libraries splines, glmulti, and pROC. Results: LNM was found in 20 out of the 116 (17%) patients. In univariate analysis, only uMI, EMIR, uCSI and uTFD were significant predictors of LNM. The accuracy was 0.707 (AUC 0.684, 95% CI 0.568−0.801) for uMI (p < 0.01), 0.672 (AUC 0.664, 95% CI 0.547−0.781) for EMIR (p < 0.01), 0.776 (AUC 0.647, 95% CI 0.529−0.765) for uCSI (p < 0.01), and 0.638 (AUC 0.683, 95% CI 0.563−0.803) for uTFD (p < 0.05). The cut-off value for uTFD was 5.2 mm. However, AREA and VOL revealed a significant relationship by nonlinear analysis as well. Among all possible multivariate models, the one comprising interactions of splines of uTFD with uMI and splines of SPE-VOL with uCSI showed the most usefulness. Accuracy was 0.802 (AUC 0.791, 95% CI 0.673−0.91) Conclusions: A combination of uTFD for patients with uMI > 50%, and SPE-VOL for patients with uCSI, allows for the most accurate prediction of LNM in EC, rather than uMI alone.

Cervical invasion and lymphovascular space invasion as the most associated risk factors for lymph node metastases in endometrial cancer

Background: Endometrial cancer is the second most common type of gynecologic malignancy. Studies on factors of lymphatic metastases are few. This study aimed to determine the clinical and pathological characteristics that can predict lymph node metastases.&#x0D; Purpose: To determine the correlation of clinicopathological characteristics with LNM (Lymph Node Metastases) in Endometrial Cancer (EC).&#x0D; Methods: This study used observational analysis and a cross-sectional methodology to examine medical records of endometrial cancer patients who underwent lymphadenectomy sampling surgery in 2018-2020. The risk factors are determined using multivariate analysis.&#x0D; Results: The stage IB and IIIC1 patients accounted for the most (17.27%) of the 63 research participants. Only one patient (1.1%) is in stage IIIB. The rests are stage IA and IIIA, each including eight patients (8.13%), and stage II, which has twelve people (12.19% ). There were 17 patients with endometrial cancer who had lymph node metastases (27% ). The incidence of LNM is 23.2% in endometrioid types, while the incidence of metastases is 57.1% in non-endometrioid kinds. The incidence of LNM in EC was not linked with the patient's age (p=0.628), BMI (p=0.190), parity (p=0.194), or menopausal status (p=0.970). The study found no correlation between LNM and histological kinds (p=0.078), histopathological degrees (p=0.514), or myometrium invasion (p=0.134) in EC. The risk factors most linked with LNM in EC are LVSI (+) (OR = 0.10; IK 95% 0.19–0.56; p = 0.009) and cervical invasion (OR = 0.07; IK95 percent 0.008–0.64; p = 0.019) according to the results of multivariate analysis.&#x0D; Conclusion: Cervical invasion and LVSI (+) are the risk factors most associated with the incidence of LNM.

Serum miRNA-204-5p as a potential non-invasive biomarker for the diagnosis of endometrial cancer with sentinel lymph node mapping.

The lymph node status is one of the most critical prognostic factors used in determining adjuvant treatment in endometrial cancer (EC). Lymphadenectomy is associated significant surgical and postoperative risks. The use of sentinel lymph node mapping (SLNM) has emerged as an alternative method to complete lymphadenectomy in EC. However, there remains controversy surrounding the use of SLNM in high-risk disease and its false-negative rate (3%). The authors previously identified miR-204-5p as a tumor-suppressor miRNA associated with lymph node metastasis in EC tissues. The present study demonstrated that serum miR-204-5p in patients with EC has the potential for use as an early diagnostic biomarker combined with SLNM to assess the lymph node status prior to surgery. The present study also aimed to identify the optimal cut-off value of serum miR-204-5p. The relative expression levels of miR-204-5p were detected using reverse transcription-quantitative PCR in the serum of 52 patients with EC (total SLNM). A total of 20 patients diagnosed with ovarian cysts, 20 patients diagnosed with myoma, and 20 participants diagnosed with endometrial polyps or endometrial hyperplasia were included as the control group. miR-204-5p expression was also detected in lymph node tissues using in situ hybridization. The results revealed that serum miR-204-5p expression was downregulated in patients with EC compared with its expression in patients with benign ovarian cysts, myoma and endometrial hyperplasia/polyps (P<0.01). In accordance with the final pathological evaluation, patients with EC with a positive SLN status had a significantly lower level of miR-204-5p compared with those with a negative SLN status (P<0.01). The area under the ROC curve of miR-204-5p was 0.923, 95% CI (0.847-1.000), and the diagnostic value had a sensitivity of 87.2% and specificity of 80.0%, with an optimal cut-off value of 0.253. On the whole, it was demonstrated that a lower miR-204-5p expression is associated with lymph node metastasis in these SLN(+) EC tissues, indicating that the downregulation of serum miR-204-5p in patients with EC has potential for use as an early diagnostic biomarker combined with SLNM. In addition, with a cut-off value of 0.253, it appeared optimal for the prediction of lymph node metastasis in EC.

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

Simple SummaryComputer-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.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.

Prospective Non-randomized Control Trial on Role of Systematic High Para-Aortic Lymphadenectomy in Endometrial Cancer: Indian Study

Endometrial cancer (EC) accounts for approximately 10% of all cancers in females and is the second most common gynecologic cancer. The primary goal of surgical staging in EC is to identify extrauterine disease. Pelvic and para-aortic lymph node dissection (PLND and PaLND) is the gold standard in the detection of lymph node metastases in EC. However, there is still no uniformity worldwide regarding the scope and extent of lymph node dissection in EC. The present prospective non-randomized study was conducted to establish the importance of systematic lymphadenectomy (LND), especially PaLND above the inferior mesenteric artery (IMA), and to identify the risk factors associated with lymph node metastasis. A total of 210 patients with EC were included. All patients underwent comprehensive surgical staging which included simultaneous PLND and PaLND. In addition for non-endometrioid, additional omental and peritoneal biopsies were also performed. PLND consisted of complete removal of common iliac, external iliac, internal iliac and obturator lymph nodes as per standard techniques. The extent of PaLND was up to the renal veins. Pelvic lymph nodes (PLN), para-aortic lymph nodes (PaLN) harvested below the IMA and PaLN dissected above the IMA were labeled separately and submitted for histopathological examination (HPE). Patient’s demographic factors, surgical outcomes and tumor histological findings were tabulated for analysis. Patients diagnosed with EC were most commonly in their seventh decade (59.5%). The mean time for LND was 35 min. In 66% of the cases with positive PLN, the PaLN was also positive. Twenty-nine (13.8%) patients had both PLN and PaLN involvement. In all cases of positive PaLN, the nodes between IMA and renal veins were the only positive nodes. In 15 (7.1%) patients, PaLN were the only positive nodes. The mean PaLN yield was 9.1. Predictors of PaLN positivity were pelvic node positivity, Grade III tumors and lymphovascular space invasion (LVSI). Major complications observed were wound infection, paralytic ileus and subacute intestinal obstruction. In oncology, a good loco-regional control has the greatest impact on survival. Systematic para-aortic lymphadenectomy done with good surgical technique results in minimal morbidity and mortality without any additional blood transfusion requirement. The trend toward greater use of complete surgical staging has been accompanied by a judicious use of adjuvant therapy. This has led to marked decrease in the use of pelvic radiation therapy and its complications and has a significant impact on the cost of treatment.

The significance of plasma D-dimer level in predicting high risk factors of endometrial cancer.

BackgroundActivated clotting-fibrinolytic system is associated with poor outcome of cancer patients. This retrospective study aimed to evaluate the significance of plasma D-dimer level in predicting high risk factors of endometrial cancer (EC) patients.MethodsTotal 176 EC patients who underwent radiotherapy between January 2018 and June 2019 at Shanghai First Maternity and Infant Hospital were retrospectively analyzed. Their preoperative and postoperative plasma D-dimer levels were measured as routine assessment in our hospital, and analyzed for their association with clinicopathological data retrieved from medical records of the patients.ResultsHigh risk group had significantly higher 1st day postoperative D-dimer levels. The 1st day postoperative D-dimer predicted higher grade EC with the specificity of 63.7% and the sensitivity of 63.4%; predicted late stage EC with the specificity of 83.7% and the sensitivity of 58.6%; predicted deeper myoinvasion of EC with the specificity of 84.9% and the sensitivity of 43.3%; predicted lymphovascular space invasion positive EC with the specificity of 84.0% and the sensitivity of 50.0%; predicted lymph node metastasis of EC with the specificity of 50.9% and the sensitivity of 100%; and predicted cervical invasion of EC with the specificity of 82.1% and the sensitivity of 65.0%.ConclusionsIncreased postoperative plasma D-dimer levels accurately predicted high risk factors in patients with EC.

Increased Serum Calcium Level Promotes the Risk of Lymph Node Metastasis in Endometrial Cancer.

PurposeThe early predictive values of diagnostic markers for lymph node metastasis (LNM) in endometrial cancer (EC) are still unclear at present. The purpose of this study is to explore the relationship between serum calcium and LNM in EC.MethodsWe identified all patients with EC who underwent surgery between January 2012 and December 2016. Patient characteristics and various preoperative clinicopathologic data were obtained from medical records and were reviewed retrospectively. These patients were divided into two groups according to the pathology of their lymph node. Logistic regression models analyzed the relationship between the ionized calcium and LNM of EC patients, while adjusting for the potential confounders.ResultsA total of 448 patients were assessed. Univariate analysis showed that ionized calcium, CA125 level, tumor grade, peritoneal cytology, FIGO stage, histological type, LVSI, and myometrial invasion were positively correlated with LNM (all P<0.05). The risk of LNM increased with the promotion of serum ionized calcium (P for trend <0.01). Ionized calcium level was significant before and after the adjustment of cofounders (unadjusted: OR=11.9, 95% CI: 4.8–29.6, P< 0.01; model I: OR=11.3, 95% CI: 4.5–28.8, P< 0.01; model II: OR=5.2, 95% CI: 1.6–17.2, P< 0.05). Additionally, the risk of ionized calcium was especially evident in patients whose age was older than 60, BMI<28 kg/m2, grade 3, negative peritoneal cytology and endometrioid endometrial adenocarcinoma.ConclusionIonized calcium level was highly associated with LNM in EC and acted as a potential biomarker in predicting the risk of LNM in EC.

Multiplanar MRI-Based Predictive Model for Preoperative Assessment of Lymph Node Metastasis in Endometrial Cancer.

Introduction: Assessment of lymph node metastasis (LNM) is crucial for treatment decision and prognosis prediction for endometrial cancer (EC). However, the sensitivity of the routinely used magnetic resonance imaging (MRI) is low in assessing normal-sized LNM (diameter, 0–0.8 cm). We aimed to develop a predictive model based on magnetic resonance (MR) images and clinical parameters to predict LNM in normal-sized lymph nodes (LNs).Materials and Methods: A total of 200 retrospective patients were enrolled and divided into a training cohort (n = 140) and a test cohort (n = 60). All patients underwent preoperative MRI and had pathological result of LNM status. In total, 4,179 radiomic features were extracted. Four models including a clinical model, a radiomic model, and two combined models were built. Area under the receiver operating characteristic (ROC) curves (AUC) and calibration curves were used to assess these models. Subgroup analysis was performed according to LN size. All patients underwent surgical staging and had pathological results.Results: All of the four models showed predictive ability in LNM. One of the combined models, ModelCR1, consisting of radiomic features, LN size, and cancer antigen 125, showed the best discrimination ability on the training cohort [AUC, 0.892; 95% confidence interval [CI], 0.834–0.951] and test cohort (AUC, 0.883; 95% CI, 0.786–0.980). The subgroup analysis showed that this model also indicated good predictive ability in normal-sized LNs (0.3–0.8 cm group, accuracy = 0.846; <0.3 cm group, accuracy = 0.849). Furthermore, compared with the routinely preoperative MR report, the sensitivity and accuracy of this model had a great improvement.Conclusions: A predictive model was proposed based on MR radiomic features and clinical parameters for LNM in EC. The model had a good discrimination ability, especially for normal-sized LNs.

Risk factors for pelvic lymph node metastasis in endometrial cancer.

To evaluate the risk factors for pelvic lymph node metastasis (LNM) in endometrial cancer (EC). Clinicopathological characteristics and preoperative laboratory results were retrospectively analyzed in 393 surgically staged patients with EC (January 2014-February 2019). Pelvic LNM was detected in 45 (11.5%) patients. Univariate analysis showed that increased preoperative levels of human epididymis protein 4 (HE4), carbohydrate antigen 125 (CA125), monocyte to lymphocyte ratio (MLR), platelet to lymphocyte ratio (PLR), and decreased absolute lymphocyte count (ALC), as well as non-endometrioid histology, grade 3 tumor, deep myometrial invasion, vaginal or para-uterine involvement, adnexal involvement, positive peritoneal cytology, and lymphovascular space invasion (LVSI) were risk factors for pelvic LNM (All p < 0.05). Multivariate analysis revealed that preoperative serum HE4 ≥ 132pmol/L (odds ratio (OR) 4.25, 95% confidence interval (CI) 1.65-10.94, p = 0.003), serum CA 125 ≥ 27.6U/mL (OR 6.10, 95% CI 2.31-16.07, p = 0.000), non-endometrioid histology (OR 16.64, 95% CI 5.96-46.47, p = 0.000), myometrial invasion ≥ 50% (OR 5.30, 95% CI 2.07-13.55, p = 0.001), positive peritoneal cytology (OR 4.70, 95% CI 1.21-18.27, p = 0.025), and LVSI (OR 3.11, 95% CI 1.09-8.92, p = 0.034) remained as independent risk factors for pelvic LNM in EC. With the increase of these independent risk factors, the rate of pelvic LNM was increased significantly. Higher preoperative levels of serum HE4 and CA125, non-endometrioid histology, deep myometrial invasion, positive peritoneal cytology, and LVSI are independent risk factors for pelvic LNM in EC, which can provide scientific basis for lymphadenectomy.

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

In endometrial carcinoma (EC), preoperative classification is based on histopathological criteria, with only moderate diagnostic performance for the risk of lymph node metastasis (LNM). So far, existing molecular classification systems have not been evaluated for prediction of LNM. Optimized use of clinical biomarkers as recommended by international guidelines might be a first step to improve tailored treatment, awaiting future molecular biomarkers. To determine the diagnostic accuracy of preoperative clinical biomarkers for the prediction of LNM in endometrial cancer. A systematic review was performed according to the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Studies identified in MEDLINE and EMBASE were selected by two independent reviewers. Included biomarkers were based on recommended guidelines (cancer antigen 125 [Ca-125], lymphadenopathy on magnetic resonance imaging, computed tomography, and 18F-fluorodeoxyglucose positron emission tomography/computed tomography [18FDG PET-CT]) or obtained by physical examination (body mass index, cervical cytology, blood cell counts). Pooled sensitivity, specificity, area under the curve (AUC), and likelihood ratios were calculated with bivariate random-effects meta-analysis. Likelihood ratios were classified into small (0.5-1.0 or 1-2.0), moderate (0.2-0.5 or 2.0-5.0) or large (0.1-0.2 or ≥ 5.0) impact. Eighty-three studies, comprising 18,205 patients, were included. Elevated Ca-125 and thrombocytosis were associated with a moderate increase in risk of LNM; lymphadenopathy on imaging with a large increase. Normal Ca-125, cytology, and no lymphadenopathy on 18FDG PET-CT were associated with a moderate decrease. AUCs were above 0.75 for these biomarkers. Other biomarkers had an AUC <0.75 and incurred only small impact. Ca-125, thrombocytosis, and imaging had a large and moderate impact on risk of LNM and could improve preoperative risk stratification. Routine lymphadenectomy in clinical early-stage endometrial carcinoma does not improve outcome and is associated with 15%-20% surgery-related morbidity, underlining the need for improved preoperative risk stratification. New molecular classification systems are emerging but have not yet been evaluated for the prediction of lymph node metastasis. This article provides a robust overview of diagnostic performance of all clinical biomarkers recommended by international guidelines. Based on these, at least measurement of cancer antigen 125 serum level, assessment of thrombocytosis, and imaging focused on lymphadenopathy should complement current preoperative risk stratification in order to better stratify these patients by risk.

Overexpression of microRNA-205 predicts lymph node metastasis and indicates an unfavorable prognosis in endometrial cancer

As an integral component of the surgical staging system, lymphadenectomy for patients with endometrial cancer (EC) remains controversial, particularly in clinical stage I disease that includes not only low-risk, but also high-risk subgroups. In order to maximize the therapeutic effect of lymph node excision for high-risk patients who can potentially obtain survival benefits from it while minimizing its reverse effects in low-risk patients, pre-operative risk stratification of lymph node metastasis is necessary. The upregulation of microRNA-205 (miR-205) in carcinoma of the endometrium has been consistently reported recently and has been found to correlate with poor survival. The current study aimed to investigate whether the overexpression of miR-205 in curettage samples of EC could identify patients who are at a high risk for lymph node metastasis prior to surgery and validate the role of miR-205 as a prognostic marker in EC. Relative quantification detection of miR-205 in curettage and hysterectomy specimens of patients with EC was performed. Prediction of lymph node metastasis based on miR-205 expression, as well as tumor type and grade in curettage samples, was performed for all EC patients and patients with clinical stage I disease. Moreover, survival analysis was conducted. It was observed that miR-205 was significantly and consistently elevated in the curettage and hysterectomy samples of EC relative to normal controls. Furthermore, the overexpression of miR-205 could predict lymph node metastasis with a high accuracy and was revealed again to be associated with a poor prognosis in EC. Prospective and multicentric studies are required to further clarify the value of miR-205 as a promising predictor to stratify risk for lymph node metastasis in EC.

A new diagnostic method for rapid detection of lymph node metastases using a one-step nucleic acid amplification (OSNA) assay in endometrial cancer.

To improve lymph node (LN) metastasis identification for patients with endometrial cancer (EC), this study assessed the usefulness of molecular biologic techniques using a one-step nucleic acid amplification (OSNA) assay. Using quantitative reverse transcription polymerase chain reaction (qRT-PCR), an optimal mRNA marker was selected, and its expression was compared between histopathologically positive and negative LNs using an OSNA assay. The authors determined copy number cutoff values and evaluated the diagnostic performance of this OSNA assay using sentinel lymph nodes (SLNs). They also investigated whether an OSNA assay could detect LN metastases with sensitivity and specificity equivalent to the 2-mm-interval histopathology method. For analysis of EC samples, cytokeratin 19 (CK19) was selected as a useful mRNA marker for the OSNA assay. When the cutoff value was set at 250 copies (using 215 LNs from 70 patients), an OSNA assay using CK19 mRNA had a sensitivity of 93.3%, a specificity of 99.5%, and a concordance rate of 99.1%. For performance evaluations using SLNs (120 histopathologically negative LNs and 17 histopathologically positive LNs from 35 patients), a OSNA assay using CK19 mRNA had a sensitivity of 82.4%, a specificity of 99.2%, a positive predictive value of 93.3%, and a concordance rate of 97.1%. Thus, an OSNA assay using CK19 mRNA provided results equivalent to those with the 2-mm-interval histopathology method. The study data demonstrated that an OSNA assay using CK19 mRNA was applicable for detecting LN metastases in EC. Combined analysis using an OSNA assay and SLNs may improve individualized treatments according to LN metastatic status.

Lymphovascular space involvement is the sole independent predictor of lymph node metastasis in clinical early stage endometrial cancer

To determine clinicopathological risk factors associated with lymph node metastasis in endometrial cancer (EC). Clinicopathological data of patients who underwent comprehensive surgical staging for clinical early stage EC between 2001 and 2010 at Hacettepe University Hospital was retrospectively reviewed. Two hundred and sixty-one patients were included. There were 26 patients (10.0%) with lymph node metastasis. Of these, 14 (5.4%) had pelvic lymph node metastasis, 8 (3.1%) had both pelvic and paraaortic lymph node metastasis, and 4 (1.5%) had isolated paraaortic metastasis. Univariate analysis revealed tumor size >2 cm, type II cancer, grade III histology, cervical stromal invasion, deep myometrial invasion, positive peritoneal cytology, adnexal involvement, serosal involvement, and presence of lymphovascular space involvement (LVSI) as significant clinicopathological factors associated with retroperitoneal lymph node metastasis. For paraaortic metastasis either isolated or with pelvic lymph node metastasis, significant factors were grade III disease, cervical stromal invasion, deep myometrial invasion, positive peritoneal cytology, adnexal involvement, serosal involvement, pelvic lymph node metastasis, and presence of LVSI. The only factor associated with isolated paraaortic lymph node metastasis was LVSI. Multivariate analysis revealed LVSI as the only independent factor for both retroperitoneal and paraaortic lymph node metastasis (odds ratio 14.9; 95% confidence interval 3.8-59.0; p < 0.001, and odds ratio 20.9; 95% confidence interval 1.9-69.9; p = 0.013, respectively). Lymphovascular space involvement is the sole predictor of lymph node metastasis in EC. Therefore, LVSI status should be requested from the pathologist during frozen examination whenever possible to consider when a decision to perform or omit lymphadenectomy is made.

Redefining the risk of pelvic and paraaortic lymph node metastasis in endometrial cancer: A large prospective cohort study with a uniform surgical approach

Objective: Definition of the risk of pelvic and paraaortic (PA) lymph node metastasis (LNM) is a priority in the management of endometrial cancer (EC). The objective of this study is to prospectively define the prevalence of LNM in EC, based on histologic grade and subtype, myometrial invasion (MI) and gross extrauterine disease.

Evaluation of preoperative criteria used to predict lymph node metastasis in endometrial cancer

To evaluate whether we could accurately predict lymph node (LN) metastasis with preoperative tests in endometrial cancer. Design. Retrospective study. Seoul National University Hospital, South Korea. Population. Three hundred patients with endometrial cancer who underwent surgical staging including lymphadenectomy between January 1999 and July 2007. We reviewed the medical records of 300 patients with endometrial cancer. The preoperative factors used to predict LN metastasis were as follows: old age (> or = 55 years), serum CA-125 level [level > or = 20 U/mL (if age < 50 years), level > or = 28 U/mL (if age > or = 50 years)], non-endometrioid histologic type and Grade 3, metastatic LN assessed by pelvic MRI or CT, and deep myometrial invasion assessed by pelvic MRI only. Logistic regression analysis was used to determine the significant predictive factors. Sensitivity/specificity and false positive/negative rates. Thirty patients had LN metastasis. Although LN evaluation by pelvic MRI or CT and high CA-125 level were the significant independent predictors for LN metastasis, the sensitivity/specificity and false positive/negative rates for LN metastasis by these two combined preoperative tests were 86.7%/71.4% and 68.7%/2.7%, respectively. However, the sensitivity/specificity and false positive/negative rates for LN metastasis by six combined preoperative tests were 100%/28.9% and 84.6%/0%, respectively. The six combined preoperative tests are useful in selecting patients without LN metastasis in endometrial cancer. Lymphadenectomy could be avoided in about 29% of patients with endometrial cancer who have no LN metastasis by using six combined preoperative tests.