Validation of an Endometrial Tumor Diameter Model for Risk Assessment in the Absence of Lymph Node Mapping

Association of Tumor Size With Myometrial Invasion, Lymphovascular Space Invasion, Lymph Node Metastasis, and Recurrence in Endometrial Cancer: A Meta-Analysis of 40 Studies With 53,276 Patients

Background

Myometrial invasion (MI), lymphovascular space invasion (LVSI), and lymph node metastasis (LNM) have been found to have independent prognostic factors in endometrial cancer. Tumor size has practical advantages in endometrial cancer. The cutoff values for tumor size conformed with current literature. More and more studies inferred that tumor size >20 mm showed a strong correlation. However, the relationship between tumor size >20 mm and MI, LVSI, LNM, recurrence, and overall survival (OS) remains controversial, and no meta-analysis has been conducted. Therefore, a systematic review and meta-analysis should be performed to discuss this issue later on.

Methods

Relevant articles were collected from PubMed, EMBASE, and Cochrane Library databases from January 1990 to June 2021. The predictive value of tumor size >20 mm in endometrial cancer was studied, and data were pooled for meta-analysis using Review Manager 5.1. Additionally, the odds ratio (OR) was analyzed, and cumulative analyses of hazard ratio (HR) and their corresponding 95% CI were conducted.

Results

A total of 40 articles with 53,276 endometrial cancer patients were included in the meta-analysis. It contained 7 articles for MI, 6 for LVSI, 21 for LNM, 7 for recurrence, and 3 for OS. Primary tumor size >20 mm was significantly associated with depth of MI (OR = 5.59, 95% CI [5.02, 6.23], p < 0.001), positive LVSI (OR = 3.35, 95% CI [2.34, 4.78], p < 0.001), positive LNM (OR = 4.11, 95% CI [3.63, 4.66], p < 0.001), and recurrence (OR = 3.52, 95% CI [2.39, 5.19], p < 0.001). Tumor size >20 mm was also related to OS via meta-synthesis of HR in univariate survival (HR 2.13, 95% CI [1.28, 3.53], p = 0.003). There was no significant publication bias in this study by funnel plot analysis.

Conclusion

Primary tumor size >20 mm was an independent predictive factor for the depth of MI, positive LVSI, positive LNM, recurrence, and poor OS. Therefore, it is more important to take into account the value of tumor size in the clinicopathological staging of endometrial carcinoma. Tumor size >20 mm should be integrated into the intraoperative algorithm for performing a full surgical staging. Well-designed and multicenter studies, with a larger sample size, are still required to verify the findings.

Multi-Parameter MR Radiomics Based Model to Predict 5-Year Progression-Free Survival in Endometrial Cancer

Background

Relapse is the major cause of mortality in patients with resected endometrial cancer (EC). There is an urgent need for a feasible method to identify patients with high risk of relapse.

Purpose

To develop a multi-parameter magnetic resonance imaging (MRI) radiomics-based nomogram model to predict 5-year progression-free survival (PFS) in EC.

Methods

For this retrospective study, 202 patients with EC followed up for at least 5 years after hysterectomy. A radiomics signature was extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) and a dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE). The radiomics score (RS) was calculated based on the least absolute shrinkage and selection operator (LASSO) regression. We have developed a radiomics based nomogram model (ModelN) incorporating the RS and clinical and conventional MR (cMR) risk factors. The performance was evaluated by the receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA).

Results

The ModelN demonstrated a good calibration and satisfactory discrimination, with a mean area under the curve (AUC) of 0.840 and 0.958 in the training and test cohorts, respectively. In comparison with clinical prediction model (ModelC), the discrimination ability of ModelN showed an improvement with P &lt; 0.001 for the training cohort and P=0.032 for the test cohort. Compared to the radiomics prediction model (ModelR), ModelN discrimination ability showed an improvement for the training cohort with P = 0.021, with no statistically significant difference in the test cohort (P = 0.106). Calibration curves suggested a good fit for probability (Hosmer–Lemeshow test, P = 0.610 and P = 0.956 for the training and test cohorts, respectively).

Conclusion

This multi-parameter nomogram model incorporating clinical and cMR findings is a valid method to predict 5-year PFS in patients with EC.