Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China.
Clinical and Technical Support, Philips Healthcare, Shanghai, 200072, Shanghai, China.
Sci Rep. 2024 May 30;14(1):12456. doi: 10.1038/s41598-024-63369-1.
To develop and validate an enhanced CT-based radiomics nomogram for evaluating preoperative metastasis risk of epithelial ovarian cancer (EOC). One hundred and nine patients with histologically confirmed EOC were retrospectively enrolled. The volume of interest (VOI) was delineated in preoperative enhanced CT images, and 851 radiomics features were extracted. The radiomics features were selected by the least absolute shrinkage and selection operator (LASSO), and the rad-score was calculated using the formula of the radiomics label. A clinical model, radiomics model, and combined model were constructed using the logistic regression classification algorithm. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance of the models. Seventy-five patients (68.8%) were histologically confirmed to have metastasis. Eleven optimal radiomics features were retained by the LASSO algorithm to develop the radiomic model. The combined model for evaluating metastasis of EOC achieved area under the curve (AUC) values of 0.929 (95% CI 0.8593-0.9996) in the training cohort and 0.909 (95% CI 0.7921-1.0000) in the test cohort. To facilitate clinical use, a radiomic nomogram was built by combining the clinical characteristics with rad-score. The DCA indicated that the nomogram had the most significant net benefit when the threshold probability exceeded 15%, surpassing the benefits of both the treat-all and treat-none strategies. Compared with clinical model and radiomics model, the radiomics nomogram has the best diagnostic performance in evaluating EOC metastasis. The nomogram is a useful and convenient tool for clinical doctors to develop personalized treatment plans for EOC patients.
开发和验证一种基于 CT 的增强放射组学列线图，用于评估上皮性卵巢癌（EOC）的术前转移风险。回顾性纳入 109 例经组织学证实的 EOC 患者。在术前增强 CT 图像中描绘感兴趣区（VOI），提取 851 个放射组学特征。通过最小绝对收缩和选择算子（LASSO）选择放射组学特征，并使用放射组学标签公式计算 rad-score。使用逻辑回归分类算法构建临床模型、放射组学模型和联合模型。使用受试者工作特征（ROC）曲线分析和决策曲线分析（DCA）评估模型的诊断性能。75 例（68.8%）患者经组织学证实存在转移。LASSO 算法保留了 11 个最佳放射组学特征，用于开发放射组学模型。评估 EOC 转移的联合模型在训练队列中获得了 0.929（95%置信区间 0.8593-0.9996）的曲线下面积（AUC）值，在测试队列中获得了 0.909（95%置信区间 0.7921-1.0000）的 AUC 值。为了便于临床应用，通过将临床特征与 rad-score 相结合构建了放射组学列线图。DCA 表明，当阈值概率超过 15%时，列线图具有最大的净获益，超过了治疗所有和治疗无的策略的获益。与临床模型和放射组学模型相比，放射组学列线图在评估 EOC 转移方面具有最佳的诊断性能。该列线图是临床医生为 EOC 患者制定个性化治疗计划的有用且方便的工具。
