Department of Radiology, the Second Affiliated Hospital of Nanchang University, Minde Road No. 1, 330006, Nanchang, Jiangxi Province, China.
Department of Radiology, Jiangxi Provincial People's Hospital, Nanchang, Jiangxi, China.
BMC Cancer. 2024 Mar 6;24(1):307. doi: 10.1186/s12885-024-12037-8.
Preoperative prediction of International Federation of Gynecology and Obstetrics (FIGO) stage in patients with epithelial ovarian cancer (EOC) is crucial for determining appropriate treatment strategy. This study aimed to explore the value of contrast-enhanced CT (CECT) radiomics in predicting preoperative FIGO staging of EOC, and to validate the stability of the model through an independent external dataset.
A total of 201 EOC patients from three centers, divided into a training cohort (n = 106), internal (n = 46) and external (n = 49) validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening radiomics features. Five machine learning algorithms, namely logistic regression, support vector machine, random forest, light gradient boosting machine (LightGBM), and decision tree, were utilized in developing the radiomics model. The optimal performing algorithm was selected to establish the radiomics model, clinical model, and the combined model. The diagnostic performances of the models were evaluated through receiver operating characteristic analysis, and the comparison of the area under curves (AUCs) were conducted using the Delong test or F-test.
Seven optimal radiomics features were retained by the LASSO algorithm. The five radiomics models demonstrate that the LightGBM model exhibits notable prediction efficiency and robustness, as evidenced by AUCs of 0.83 in the training cohort, 0.80 in the internal validation cohort, and 0.68 in the external validation cohort. The multivariate logistic regression analysis indicated that carcinoma antigen 125 and tumor location were identified as independent predictors for the FIGO staging of EOC. The combined model exhibited best diagnostic efficiency, with AUCs of 0.95 in the training cohort, 0.83 in the internal validation cohort, and 0.79 in the external validation cohort. The F-test indicated that the combined model exhibited a significantly superior AUC value compared to the radiomics model in the training cohort (P < 0.001).
The combined model integrating clinical characteristics and radiomics features shows potential as a non-invasive adjunctive diagnostic modality for preoperative evaluation of the FIGO staging status of EOC, thereby facilitating clinical decision-making and enhancing patient outcomes.
预测上皮性卵巢癌（EOC）患者的国际妇产科联合会（FIGO）分期对于确定适当的治疗策略至关重要。本研究旨在探讨对比增强 CT（CECT）放射组学在预测 EOC 术前 FIGO 分期中的价值，并通过独立的外部数据集验证模型的稳定性。
本研究纳入了来自三个中心的 201 名 EOC 患者，分为训练队列（n=106）、内部验证队列（n=46）和外部验证队列（n=49）。采用最小绝对值收缩和选择算子（LASSO）回归算法筛选放射组学特征。采用逻辑回归、支持向量机、随机森林、LightGBM 和决策树五种机器学习算法建立放射组学模型。选择最佳表现算法建立放射组学模型、临床模型和联合模型。采用受试者工作特征分析评估模型的诊断性能，并采用 Delong 检验或 F 检验比较曲线下面积（AUC）。
LASSO 算法筛选出 7 个最优放射组学特征。五个放射组学模型中，LightGBM 模型表现出较高的预测效率和稳健性，在训练队列、内部验证队列和外部验证队列中的 AUC 分别为 0.83、0.80 和 0.68。多因素 logistic 回归分析表明，癌抗原 125 和肿瘤位置是 EOC FIGO 分期的独立预测因素。联合模型具有最佳的诊断效率，在训练队列、内部验证队列和外部验证队列中的 AUC 分别为 0.95、0.83 和 0.79。F 检验表明，在训练队列中，联合模型的 AUC 值显著优于放射组学模型（P<0.001）。
联合临床特征和放射组学特征的模型有望成为 EOC 术前 FIGO 分期评估的非侵入性辅助诊断方法，从而为临床决策提供支持，改善患者预后。
