Accurate preoperative assessment of lymph node metastasis in patients with epithelial ovarian cancer is of great significance for formulating treatment plans and assessing prognosis. Radiomics technology has been used as a non-invasive means to predict lymph node metastasis in various cancers before surgery, but there are few research reports on its application effects in the field of gynecological cancer.

To construct a predictive model for lymph node metastasis in patients with epithelial ovarian cancer using radiomics technology based on ¹⁸F-fluorodeoxyglucose positron emission tomography with integrated computed tomography (¹⁸F-FDG PET/CT) .

A total of 98 patients with epithelial ovarian cancer admitted to the Department of Gynecology, the Second Xiangya Hospital of Central South University from September 2020 to December 2022 were selected, according to the lymph node metastasis status, they were divided into lymph node metastasis group (n=65) and non-lymph node metastasis group (n=33), into training set of 68 cases and validation set of 30 cases in a 7∶3 ratio at the same time. All patients' clinical characteristics were analyzed, and the lymph node metastasis status was used as the result label for model construction.

The lymph node metastasis rate in patients with epithelial ovarian cancer was 66.3% (65/98) in this study. There was a statistically significant difference in the level of human epididymal protein 4 (HE4) and location of the primary lesion between lymph node metastasis group and non-lymph node metastasis group (P<0.05). Multivariate Logistic regression analysis showed that, HE4 level, location of the primary lesion, and Radscore were predictive factors for lymph node metastasis in patients with epithelial ovarian cancer (P<0.05). The clinical predictive model was constructed using HE4 level and location of the primary lesion, the combined predictive model was constructed using HE4 levels, location of the primary lesion, and Radscore. Delong's test showed that, the combined predictive model had an AUC of 0.80 (95%CI=0.70-0.90) for predicting lymph node metastasis in the training set of patients with epithelial ovarian cancer, which was higher than that of the clinical predictive model (AUC=0.73, 95%CI=0.61-0.85) (P=0.042). The calibration curve showed that, the combined predictive model passed the calibration test (P=0.990), had good discrimination ability. Decision curve (DCA) analysis showed that, both the clinical and combined predictive models had good predictive performance, but the net benefit of the combined predictive model was higher.

We successfully constructed the combined predictive model for lymph node metastasis in patients with epithelial ovarian cancer using radiomics technology based on ¹⁸F-FDG PET/CT, with high robustness, good discrimination ability, and relatively high net benefit, which can be used for clinical doctors to formulate individualized treatment plans for patients and assess patients' prognosis.

术前准确评估上皮性卵巢癌患者淋巴结转移情况对于制订治疗方案、评估预后等具有重要意义，影像组学技术已被作为多种癌症术前预测淋巴结转移情况的无创手段，但关于其在妇科癌症领域应用效果的研究报道较少。

采用基于¹⁸F-氟代脱氧葡萄糖正电子发射计算机体层摄影-CT（¹⁸F-FDG PET/CT）的影像组学技术构建上皮性卵巢癌患者淋巴结转移预测模型。

选取2020年9月—2022年12月在中南大学湘雅二医院妇科住院的上皮性卵巢癌患者98例，根据其淋巴结转移情况分为淋巴结转移组65例与非淋巴结转移组33例，同时按照7∶3比例进行随机抽样后分为训练集68例与验证集30例。分析所有患者临床特征，以淋巴结转移情况作为结果标签进行模型构建。

本研究上皮性卵巢癌患者淋巴结转移率为66.3%（65/98）。淋巴结转移组与非淋巴结转移组患者人附睾蛋白4（HE4）水平、原发灶位置比较，差异有统计学意义（P<0.05）。多因素Logistic回归分析结果显示，HE4水平、原发灶位置、影像组学评分（Radscore）是上皮性卵巢癌患者淋巴结转移的预测因素（P<0.05）。以HE4水平、原发灶位置构建临床预测模型，以HE4水平、原发灶位置、Radscore构建联合预测模型。Delong's检验结果显示，联合预测模型预测训练集中上皮性卵巢癌患者淋巴结转移受试者工作特征曲线下面积（AUC）为0.80（95%CI=0.70~0.90），高于临床预测模型的0.73（95%CI=0.61~0.85，P=0.042）；校准曲线显示，联合预测模型通过校准度检验（P=0.990），具有良好的区分能力；决策曲线（DCA）分析结果显示，临床预测模型、联合预测模型的预测效能良好，但联合预测模型的净效益较高。

采用基于¹⁸F-FDG PET/CT的影像组学技术成功构建了上皮性卵巢癌患者淋巴结转移的联合预测模型，且模型稳健性较高、区分能力良好、净效益较高，可为临床医生制订患者个体化治疗方案、评估患者预后等提供参考。