Xie Wenting, Wang Yaoqin, Du Zhongshi, Chen Yijie, Ke Xiaohui, Wu Tingfan, Wang Zhilan, Tang Lina
Department of Ultrasound, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian Province, China.
Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, People's Republic of China.
Sci Rep. 2025 Jun 2;15(1):19279. doi: 10.1038/s41598-025-02776-4.
We aimed to develop and validate a nomogram for diagnosing ovarian cancer from ovarian masses based on clinical information, O-RADS US, and radiomics. A total of 981 patients with ovarian masses from two centers were randomly divided into the training cohort (n = 686) and the validation cohort (n = 295). We defined the region of interest (ROI) of the tumor by manually drawing the tumor contour on the ultrasound image of the lesion. The radiomics features were extracted from ultrasound images, and the radiomics score was then calculated. O-RADS US characteristics, radiomics score, and clinical features selected using the LASSO algorithm were used to develop O-RADS US + Radscore + Clinical, Radscore + Clinical, and O-RADS US + Clinical models, respectively. Receiver operating characteristic (ROC), decision curve analysis, and calibration curve were used to evaluate the performance of the nomogram models. Age, CA125, O-RADS US, and radiomics score were related to ovarian malignancy through univariate and multivariate logistic regression analyses. In the training and validation datasets, the areas under the ROC curve (AUC) of O-RADS US + Clinical model were 0.830 and 0.815, respectively, and those for the Radscore + Clinical model were 0.876 and 0.867, respectively. The O-RADS US + Radscore + Clinical nomogram model presented improved AUC values of 0.967 in the training group and 0.951 in the validation group, significantly higher than that of Radscore + Clinical and O-RADS US + Clinical models. The calibration curve and the clinical decision curve analysis demonstrated that the nomogram models had high clinical benefits. The O-RADS US + Radscore + Clinical model had the highest net return. Combination nomogram model that integrates clinical features, O-RADS US, and radiomics based on ultrasound image analysis could predict ovarian malignancy with high diagnostic accuracy, indicating that this model might have a role in preoperative diagnosis for differentiating benign and malignant ovarian tumors.
我们旨在基于临床信息、O-RADS US及放射组学开发并验证一种用于从卵巢肿块诊断卵巢癌的列线图。来自两个中心的981例卵巢肿块患者被随机分为训练队列（n = 686）和验证队列（n = 295）。我们通过在病变的超声图像上手动绘制肿瘤轮廓来定义肿瘤的感兴趣区域（ROI）。从超声图像中提取放射组学特征，然后计算放射组学评分。使用LASSO算法选择的O-RADS US特征、放射组学评分和临床特征分别用于开发O-RADS US+Radscore+临床、Radscore+临床和O-RADS US+临床模型。采用受试者操作特征（ROC）、决策曲线分析和校准曲线来评估列线图模型的性能。通过单因素和多因素逻辑回归分析发现年龄、CA125、O-RADS US和放射组学评分与卵巢恶性肿瘤相关。在训练和验证数据集中，O-RADS US+临床模型的ROC曲线下面积（AUC）分别为0.830和0.815，Radscore+临床模型的分别为0.876和0.867。O-RADS US+Radscore+临床列线图模型在训练组中的AUC值为0.967，在验证组中为0.951，均显著高于Radscore+临床和O-RADS US+临床模型。校准曲线和临床决策曲线分析表明列线图模型具有较高的临床效益。O-RADS US+Radscore+临床模型的净收益最高。基于超声图像分析整合临床特征、O-RADS US和放射组学的联合列线图模型能够以较高的诊断准确性预测卵巢恶性肿瘤，表明该模型可能在术前鉴别卵巢良恶性肿瘤中发挥作用。
