Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Department of Obstetrics & Gynecology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
J Xray Sci Technol. 2022;30(6):1185-1199. doi: 10.3233/XST-221244.
To investigate the value of nomogram analysis based on conventional features and radiomics features of computed tomography (CT) venous phase to differentiate metastatic ovarian tumors (MOTs) from epithelial ovarian tumors (EOTs).
A dataset involving 286 patients pathologically confirmed with EOTs (training cohort: 133 cases, validation cohort: 68 cases) and MOTs (training cohort: 54 cases, validation cohort: 31 cases) is assembled in this study. Radiomics features are extracted from the venous phase of CT images. Logistic regression is employed to build models based on conventional features (model 1), radiomics features (model 2), and the combination of model 1 and model 2 (model 3). Diagnostic performance is assessed and compared. Additionally, a nomogram is plotted for model 3, and decision curve analysis is applied for clinical use.
Age, abdominal metastasis, para-aortic lymph node metastasis, location, and septation are chosen to build Model 1. Ten optimal radiomics features are ultimately selected and radiomics score (rad-score) is calculated to build Model 2. Nomogram score is calculated to build model 3 that shows optimal diagnostic performance in both the training (AUC = 0.952) and validation cohorts (AUC = 0.720), followed by model 1 (AUC = 0.872 for training cohort and AUC = 0.709 for validation cohort) and model 2 (AUC = 0.833 for training cohort and AUC = 0.620 for validation cohort). Additionally, Model 3 achieves accuracy, sensitivity, and specificity of 0.893, 0.880, and 0.926 in the training cohort and 0.737, 0.853, and 0.613 in the validation cohort.
Model 3 demonstrates the best diagnostic performance for preoperative differentiation of MOTs from EOTs. Thus, nomogram analysis based on Model 3 may be used as a biomarker to differentiate MOTs from EOTs.
探讨基于 CT 静脉期常规特征和影像组学特征的列线图分析在鉴别转移性卵巢肿瘤（MOTs）与上皮性卵巢肿瘤（EOTs）中的价值。
本研究纳入了 286 例经病理证实的 EOTs（训练队列：133 例，验证队列：68 例）和 MOTs（训练队列：54 例，验证队列：31 例）患者的数据集。从 CT 静脉期图像中提取影像组学特征。采用逻辑回归分别基于常规特征（模型 1）、影像组学特征（模型 2）以及模型 1 和模型 2 的组合（模型 3）构建模型。评估并比较诊断性能。此外，为模型 3 绘制列线图，并进行决策曲线分析以评估其临床应用价值。
选择年龄、腹部转移、腹主动脉旁淋巴结转移、肿瘤位置和分隔来构建模型 1。最终选择了 10 个最优的影像组学特征，并计算影像组学评分（rad-score）以构建模型 2。计算列线图评分以构建模型 3，该模型在训练队列（AUC=0.952）和验证队列（AUC=0.720）中均表现出最佳的诊断性能，其次是模型 1（训练队列 AUC=0.872，验证队列 AUC=0.709）和模型 2（训练队列 AUC=0.833，验证队列 AUC=0.620）。此外，模型 3 在训练队列中的准确率、敏感度和特异度分别为 0.893、0.880 和 0.926，在验证队列中的准确率、敏感度和特异度分别为 0.737、0.853 和 0.613。
模型 3 在上皮性卵巢肿瘤与转移性卵巢肿瘤的术前鉴别诊断中具有最佳的诊断性能。因此，基于模型 3 的列线图分析可能可作为鉴别 MOTs 与 EOTs 的生物标志物。
