Assessment of lymph node metastases in patients with ovarian high-grade serous carcinoma: Incremental diagnostic value of dual-energy CT combined with morphologic parameters

IF 3.2 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Pub Date : 2025-04-09 DOI:10.1016/j.ejrad.2025.112107

Aining Zhang , Wenhui Shao , Jiacheng Song , Weiling Zhai , Shushen Lin , Wenjun Cheng , Feiyun Wu , Ting Chen

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引用次数: 0

Abstract

Objective

To explore the feasibility of Dual-Energy Computed Tomography (DECT) in distinguishing metastatic from non-metastatic lymph nodes (LNs) in ovarian High-Grade Serous Carcinoma (HGSC), and to assess the incremental diagnostic value of combining DECT with morphologic parameters in differentiating metastatic and non-metastatic LNs.

Methods

From October 2021 to May 2024, 141 LNs from 39 patients with HGSC who underwent DECT were retrospectively enrolled. LNs were matched with the pathological report. Five morphologic parameters and nine DECT parameters were assessed. DECT parameters were obtained from both the arterial and venous phases, including the attenuation at 40 and 70 keV, slope of the spectral Hounsfield unit curve (λ_Hu), Virtual Non-Contrast (VNC), Iodine Concentration (IC), Normalized Iodine Concentration (NIC), electron density (Rho), effective atomic number (Z_eff) and Dual-Energy Index (DEI). Independent-sample Student’s t test was used to compare continuous variables, while multivariable binary logistic regression analyses was applied to identify independent predictors for LN metastasis in the morphology, DECT, and combined models. Receiver Operating Characteristic (ROC) analysis was performed to evaluate the diagnostic performance of these three models in differentiating metastatic from non-metastatic LNs.

Results

86 metastatic LNs and 55 non-metastatic LNs were finally enrolled in our study. The short diameter (S), long diameter (L), and S/L ratio were significantly larger in metastatic LNs compared to non-metastatic LNs (9.69 ± 4.06 vs. 6.37 ± 1.24 mm, P < 0.001; 13.99 ± 5.36 vs.9.61 ± 2.30 mm, P < 0.001; 0.70 ± 0.15 vs. 0.67 ± 0.12, P = 0.023). In the venous phase, λ_HU, VNC and Rho were significantly higher in metastatic LNs compared to non-metastatic LNs (−3.596 ± 1.115 vs. −4.234 ± 1.077, P = 0.001; 24.242 ± 9.867 vs. 15.826 ± 11.830, P < 0.001; 32.557 ± 8.023 vs. 26.936 ± 9.420, P < 0.001), while IC, NIC, Z_eff, DEI were significant lower in metastatic LNs than non-metastatic LNs (1.872 ± 0.678 vs. 2.404 ± 1.140, P = 0.001; 38.309 ± 14.443 vs. 47.247 ± 22.270, P = 0.005; 8.513 ± 0.320 vs. 8.719 ± 0.360, P = 0.001; 0.014 ± 0.006 vs. 0.018 ± 0.007, P = 0.045). The Area Under the Curve (AUC) of morphology model and DECT model were 0.793 (95 %CI: 0.721–0.862) and 0.762(95 %CI: 0.690–0.825), respectively. The combination of the morphology model and DECT model revealed optimal diagnostic performance (AUC = 0.845; 95 %CI: 0.780–0.896), which was significantly higher than that of the individual models (P = 0.015, P = 0.006, respectively).

Conclusion

DECT parameters provide incremental diagnostic value in assessing metastatic LNs in patients with HGSC. The combination of the morphology and DECT models significantly improves diagnostic performance compared to the standalone morphology model.

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卵巢高级别浆液性癌淋巴结转移的评估：双能CT结合形态学参数的增量诊断价值

目的探讨双能量计算机断层扫描（DECT）在区分卵巢高级别浆液性癌（HGSC）转移性和非转移性淋巴结（LNs）方面的可行性，并评估将 DECT 与形态学参数相结合在区分转移性和非转移性 LNs 方面的增量诊断价值。方法从 2021 年 10 月到 2024 年 5 月，回顾性纳入了 39 例接受 DECT 的 HGSC 患者的 141 个 LNs。LN与病理报告相匹配。评估了五个形态学参数和九个 DECT 参数。DECT 参数从动脉和静脉阶段获得，包括 40 和 70 keV 的衰减、频谱 Hounsfield 单位曲线斜率 (λHu)、虚拟非对比度 (VNC)、碘浓度 (IC)、归一化碘浓度 (NIC)、电子密度 (Rho)、有效原子序数 (Zeff) 和双能指数 (DEI)。比较连续变量时采用独立样本学生 t 检验，而在形态学、DECT 和组合模型中，则采用多变量二元逻辑回归分析来确定 LN 转移的独立预测因素。我们还进行了受者操作特征（ROC）分析，以评估这三种模型在区分转移性和非转移性 LN 方面的诊断性能。与非转移性 LN 相比，转移性 LN 的短径（S）、长径（L）和 S/L 比值明显增大（9.69 ± 4.06 vs. 6.37 ± 1.24 mm，P < 0.001；13.99 ± 5.36 vs. 9.61 ± 2.30 mm，P < 0.001；0.70 ± 0.15 vs. 0.67 ± 0.12，P = 0.023）。在静脉期，转移性 LN 的 λHU、VNC 和 Rho 明显高于非转移性 LN（-3.596±1.115 vs. -4.234±1.077，P = 0.001；24.242±9.867 vs. 15.826±11.830，P <0.001；32.557±8.023 vs. 26.936±9.420，P <0.001），而转移 LN 的 IC、NIC、Zeff、DEI 显著低于非转移 LN（1.872 ± 0.678 vs. 2.404 ± 1.140，P = 0.001; 38.309 ± 14.443 vs. 47.247 ± 22.270, P = 0.005; 8.513 ± 0.320 vs. 8.719 ± 0.360, P = 0.001; 0.014 ± 0.006 vs. 0.018 ± 0.007, P = 0.045）。形态学模型和 DECT 模型的曲线下面积（AUC）分别为 0.793（95 %CI：0.721-0.862）和 0.762（95 %CI：0.690-0.825）。形态学模型和 DECT 模型的组合显示出最佳诊断性能（AUC = 0.845；95 %CI：0.780-0.896），明显高于单个模型（分别为 P = 0.015 和 P = 0.006）。与独立的形态学模型相比，形态学模型和 DECT 模型的组合能显著提高诊断效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

European Journal of Radiology 医学-核医学

CiteScore

6.70

自引率

3.00%

发文量

398

审稿时长

42 days

期刊介绍： European Journal of Radiology is an international journal which aims to communicate to its readers, state-of-the-art information on imaging developments in the form of high quality original research articles and timely reviews on current developments in the field. Its audience includes clinicians at all levels of training including radiology trainees, newly qualified imaging specialists and the experienced radiologist. Its aim is to inform efficient, appropriate and evidence-based imaging practice to the benefit of patients worldwide.