Comparison of Mayo and Milwaukee Risk Stratification Models for Predicting Lymph Node Metastasis in Endometrial Cancer

Boyraz, Gökhan; Atalay, Fatma; Salman, Mehmet Çoşkun; Usubütün, Alp; Erturk, Anil; Gültekin, Murat; Özgül, Nejat; Yüce, K.

doi:10.1097/igc.0000000000001261

Cited by 8 publications

(7 citation statements)

References 16 publications

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“…Some models are inherently postoperative since they are based on tumor biomarker profiles derived from hysterectomy specimens [7][8][9], whereas proposed preoperative models combine preoperative imaging characteristics and biopsy/curettage and serum markers, e.g., cancer antigen (CA 125) [10][11][12]. When applied in independent patient cohorts, these models have been shown to have variable feasibilities [13][14][15], and at present, the best risk stratification model in endometrial cancer is not yet defined, and no uniform risk model is routinely used across centers. Furthermore, sentinel lymph node dissection (SLND) procedures have been increasingly advocated as a feasible alternative to full lymphadenectomy in endometrial cancer patients.…”

Section: Introductionmentioning

confidence: 99%

Preoperative 18F-FDG PET/CT tumor markers outperform MRI-based markers for the prediction of lymph node metastases in primary endometrial cancer

et al. 2020

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Objectives To compare the diagnostic accuracy of preoperative 18F-FDG PET/CT and MRI tumor markers for prediction of lymph node metastases (LNM) and aggressive disease in endometrial cancer (EC). Methods Preoperative whole-body 18F-FDG PET/CT and pelvic MRI were performed in 215 consecutive patients with histologically confirmed EC. PET/CT-based tumor standardized uptake value (SUV max and SUV mean), metabolic tumor volume (MTV), and PET-positive lymph nodes (LNs) (SUV max > 2.5) were analyzed together with the MRI-based tumor volume (V MRI), mean apparent diffusion coefficient (ADC mean), and MRI-positive LN (maximum short-axis diameter ≥ 10 mm). Imaging parameters were explored in relation to surgicopathological stage and tumor grade. Receiver operating characteristic (ROC) curves were generated yielding optimal cutoff values for imaging parameters, and regression analyses were used to assess their diagnostic performance for prediction of LNM and progression-free survival. Results For prediction of LNM, MTV yielded the largest area under the ROC curve (AUC) (AUC = 0.80), whereas V MRI had lower AUC (AUC = 0.72) (p = 0.03). Furthermore, MTV > 27 ml yielded significantly higher specificity (74%, p < 0.001) and accuracy (75%, p < 0.001) and also higher odds ratio (12.2) for predicting LNM, compared with V MRI > 10 ml (58%, 62%, and 9.7, respectively). MTV > 27 ml also tended to yield higher sensitivity than PET-positive LN (81% vs 50%, p = 0.13). Both V MRI > 10 ml and MTV > 27 ml were significantly associated with reduced progression-free survival. Conclusions Tumor markers from 18F-FDG PET/CT outperform MRI markers for the prediction of LNM. MTV > 27 ml yields a high diagnostic performance for predicting aggressive disease and represents a promising supplement to conventional PET/CT reading in EC. Key Points • Metabolic tumor volume (MTV) outperforms other 18F-FDG PET/CT and MRI markers for preoperative prediction of lymph node metastases (LNM) in endometrial cancer patients. • Using cutoff values for tumor volume for prediction of LNM, MTV > 27 ml yielded higher specificity and accuracy than V MRI > 10 ml. • MTV represents a promising supplement to conventional PET/CT reading for predicting aggressive disease in EC.

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Section: Introductionmentioning

confidence: 99%

Preoperative 18F-FDG PET/CT tumor markers outperform MRI-based markers for the prediction of lymph node metastases in primary endometrial cancer

et al. 2020

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“…Some research also has concentrated on comparing different models. Gokhan et al compare the Mayo criteria and Milwaukee risk strati cation models and found that the Mayo model was more accurate for predicting LNM than the Milwaukee model [26] . Tuomi et al compared the performance characteristics of three risk-strati cation models, (Mayo, Helsinki and Milwaukee models) and found that these models had similar accuracies for predicting lymphatic dissemination in EC patients [27] .…”

Section: Discussionmentioning

confidence: 99%

Development and validation of prognostic factors for lymph node metastasis in endometrial cancer: A SEER analysis

Li¹,

Cheng²,

Dong³

et al. 2020

Preprint

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Objective: The purpose of this study was to develop and validate a nomogram that can be used to predict lymph node metastasis (LNM) in patients with endometrial carcinoma (EC). Methods: Clinical data of EC patients diagnosed between 2004 and 2015 were retrieved from the Surveillance, Epidemiology, and End Results Program (SEER) registry. The nomogram was constructed using independent risk factors chosen using a multivariate logistic regression analysis. Accuracy was validated for both groups using discrimination analysis and calibration curves. The predictive accuracy and clinical value of the nomogram and Mayo criteria were compared using decision curve analysis (DCA). Results: The final study group consisted of 63,836 women that met specific inclusion criteria. The factors that were identified in the multivariate analysis to be notable predictors of LNM were age, tumor size, histological type, cervical stromal invasion, tumor grade, and myometrial invasion. These risk factors were included in the nomogram. Discriminations of the nomogram and Mayo criteria were 0.848 (95% CI: 0.843-0.853) and 0.806 (95%CI: 0.801-0.812), respectively. In the validation group, the AUC values were 0.847 (95%CI: 0.840-0.857) and 0.804 (95%CI: 0.796-0.813) for the nomogram and the Mayo criteria, respectively ( P <0.01). Calibration plots showed that training and validation cohorts were well-calibrated. DCA revelaed that by using the nomogram always had a positive net benefit compared to using the Mayo criteria. Conclusions: A nomogram was developed to predict LNM in EC patients based on a large population-based analysis. The nomogram showed good performance for predicting LNM in patients with EC.

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“…Currently, most risk stratification systems and prediction models are based on classical clinicopathological parameters ( 3 , 7 ). For example, the Mayo Risk Stratification model ( 8 ) defines patients with low-risk LNM in whom lymph node dissection could be omitted, which would include grade 1 or 2 endometrioid EC, tumor diameter (TD) < 20mm, and myometrial invasion (MI) < 50%. Sofiane et al.…”

Section: Introductionmentioning

confidence: 99%

Combining Clinicopathological Parameters and Molecular Indicators to Predict Lymph Node Metastasis in Endometrioid Type Endometrial Adenocarcinoma

Jiang

Huang

et al. 2021

Front. Oncol.

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BackgroundLymph node metastasis (LNM) is a critical unfavorable prognostic factor in endometrial cancer (EC). At present, models involving molecular indicators that accurately predict LNM are still uncommon. We addressed this gap by developing nomograms to individualize the risk of LNM in EC and to identify a low-risk group for LNM.MethodsIn all, 776 patients who underwent comprehensive surgical staging with pelvic lymphadenectomy at the First Affiliated Hospital of Chongqing Medical University were divided into a training cohort (used for building the model) and a validation cohort (used for validating the model) according to a predefined ratio of 7:3. Logistics regression analysis was used in the training cohort to screen out predictors related to LNM, after which a nomogram was developed to predict LNM in patients with EC. A calibration curve and consistency index (C-index) were used to estimate the performance of the model. A receiver operating characteristic (ROC) curve and Youden index were used to determine the optimal threshold of the risk probability of LNM predicted by the model proposed in this study. Then, the prediction performance of different models and their discrimination abilities for identifying low-risk patients were compared.ResultLNM occurred in 87 and 42 patients in the training and validation cohorts, respectively. Multivariate logistic regression analysis showed that histological grade (P=0.022), myometrial invasion (P=0.002), lymphovascular space invasion (LVSI) (P=0.001), serum CA125 (P=0.008), Ki67 (P=0.012), estrogen receptor (ER) (0.009), and P53 (P=0.003) were associated with LNM; a nomogram was then successfully established on this basis. The internal and external calibration curves showed that the model fits well, and the C-index showed that the prediction accuracy of the model proposed in this study was better than that of the other models (the C-index of the training and validation cohorts was 0.90 and 0.91, respectively). The optimal threshold of the risk probability of LNM predicted by the model was 0.18. Based on this threshold, the model showed good discrimination for identifying low-risk patients.ConclusionCombining molecular indicators based on classical clinical parameters can predict LNM of patients with EC more accurately. The nomogram proposed in this study showed good discrimination for identifying low-risk patients with LNM.

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Comparison of Mayo and Milwaukee Risk Stratification Models for Predicting Lymph Node Metastasis in Endometrial Cancer

Cited by 8 publications

References 16 publications

Preoperative 18F-FDG PET/CT tumor markers outperform MRI-based markers for the prediction of lymph node metastases in primary endometrial cancer

Preoperative 18F-FDG PET/CT tumor markers outperform MRI-based markers for the prediction of lymph node metastases in primary endometrial cancer

Development and validation of prognostic factors for lymph node metastasis in endometrial cancer: A SEER analysis

Combining Clinicopathological Parameters and Molecular Indicators to Predict Lymph Node Metastasis in Endometrioid Type Endometrial Adenocarcinoma

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