Preoperative prediction of lymph node metastasis in endometrial carcinoma based on MRI radiomic, clinical, and sonographic features: a clinical dual-center study.

PURPOSE: To explore MRI-based radiomic, clinical, and sonographic features for constructing models to predict lymph node metastasis (LNM) in endometrial carcinoma (EC) preoperatively and noninvasively.

METHODS

From January 2016 to December 2024, clinical, imaging and pathological information of patients with EC were retrospectively collected at center 1 and 2. Radiomic features extracted from preoperative MRI were screened, a radiomic model was developed and radscore was calculated. Univariate and multivariate analyses were performed with all features to identify independent LNM predictors. Another three models were constructed: a clinical model based on clinical features, an ultrasound model based on features defined by International Endometrial Tumor Analysis, and a combined model integrating all predictors. Receiver operator characteristic curve and DeLong test were employed for evaluating model performance. Clinical decision curve (CDC) and calibration curve were used for assessing the clinical benefit of the models.

RESULTS

225 patients with EC were included, in which 44 patients were with LNM and 181 patients were without LNM. Statistical analyses showed that radscore, age, CA125, and endometrium thickness (EMT) were predictors for LNM. DeLong test demonstrated that CombinedModel had the superior predictive performance, which was presented as a nomogram, with area under the curve values of 0.895 (95%CI:0.828,0.961),0.870 (95%CI:0.739,1.000), 0.890 (95%CI: 0.766,1.000) for training, internal and external validation set, respectively. CDC and calibration curve of the nomogram demonstrated higher clinical benefit than other models.

CONCLUSION

The nomogram comprising age, CA125, radscore, and EMT exhibited the best performance in LNM prediction of patients with EC, making it a valuable tool for decision-making.