Gynecology, Hospital of Cardiovascular and Cerebrovascular Diseases, General Hospital of Ningxia Medical University, Yinchuan, China.
Obstetrics and Gynecology Center Functional Examination Department, General Hospital of Ningxia Medical University, Yinchuan, China.
BMC Womens Health. 2024 Sep 27;24(1):539. doi: 10.1186/s12905-024-03374-8.
Although clinical guidelines exist for diagnosing abnormal uterine bleeding, there is a significant lack of agreement on the best management strategies for women presenting with symptom, particularly in diagnosing endometrial cancer. This study aimed to develop a preoperative risk model that utilizes demographic factors and transvaginal ultrasonography of the endometrium to assess and predict the risk of malignancy in females with endometrial cancer.
In this retrospective study, a logistic regression model was developed to predict endometrial carcinoma using data from 356 postmenopausal women with endometrial lesions and an endometrial thickness (ET) of 5 mm or more. These patients had undergone transvaginal ultrasonography prior to surgery, with findings including 247 benign and 109 malignant cases. The model's predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis and compared with post-surgical pathological diagnoses.
Our model incorporates several predictors for endometrial carcinoma, including age, history of hypertension, history of diabetes, body mass index (BMI), duration of vaginal bleeding, endometrial thickness, completeness of the endometrial line, and endometrial vascularization. It demonstrated a strong prediction with an area under the curve (AUC) of 0.905 (95% CI, 0.865-0.945). At the optimal risk threshold of 0.33, the model achieved a sensitivity of 82.18% and a specificity of 92.80%.
The established model, which integrates ultrasound evaluations with demographic data, provides a specific and sensitive method for assessing and predicting endometrial carcinoma.
尽管存在诊断异常子宫出血的临床指南,但对于出现症状的女性(尤其是在诊断子宫内膜癌时),最佳管理策略仍存在很大的不一致。本研究旨在开发一种术前风险模型,该模型利用人口统计学因素和阴道超声子宫内膜评估和预测患有子宫内膜癌的女性的恶性肿瘤风险。
在这项回顾性研究中,我们使用 356 名绝经后子宫内膜病变且子宫内膜厚度(ET)≥5mm 的女性的数据,建立了一个逻辑回归模型来预测子宫内膜癌。这些患者在手术前均进行了阴道超声检查,其中 247 例为良性,109 例为恶性。通过接受者操作特征(ROC)曲线分析评估模型的预测性能,并与术后病理诊断进行比较。
我们的模型纳入了几个子宫内膜癌的预测因素,包括年龄、高血压病史、糖尿病病史、体重指数(BMI)、阴道出血持续时间、子宫内膜厚度、子宫内膜线完整性和子宫内膜血管化。它具有很强的预测能力,曲线下面积(AUC)为 0.905(95%CI,0.865-0.945)。在最佳风险阈值为 0.33 时,该模型的灵敏度为 82.18%,特异性为 92.80%。
该模型将超声评估与人口统计学数据相结合,为评估和预测子宫内膜癌提供了一种特异性和敏感性的方法。