酰胺质子转移加权成像联合ZOOMit扩散峰度成像预测宫颈癌淋巴结转移
Amide Proton Transfer-Weighted Imaging Combined with ZOOMit Diffusion Kurtosis Imaging in Predicting Lymph Node Metastasis of Cervical Cancer.
作者信息
Huang Qiuhan, Wang Yanchun, Meng Xiaoyan, Li Jiali, Shen Yaqi, Hu Xuemei, Feng Cui, Li Zhen, Kamel Ihab
机构信息
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, MD 21218, USA.
出版信息
Bioengineering (Basel). 2023 Mar 6;10(3):331. doi: 10.3390/bioengineering10030331.
BACKGROUND
The aim of this study is to investigate the feasibility of amide proton transfer-weighted (APTw) imaging combined with ZOOMit diffusion kurtosis imaging (DKI) in predicting lymph node metastasis (LNM) in cervical cancer (CC).
MATERIALS AND METHODS
Sixty-one participants with pathologically confirmed CC were included in this retrospective study. The APTw MRI and ZOOMit diffusion-weighted imaging (DWI) were acquired. The mean values of APTw and DKI parameters including mean kurtosis (MK) and mean diffusivity (MD) of the primary tumors were calculated. The parameters were compared between the LNM and non-LNM groups using the Student's t-test or Mann-Whitney U test. Binary logistic regression analysis was performed to determine the association between the LNM status and the risk factors. The diagnostic performance of these quantitative parameters and their combinations for predicting the LNM was assessed with receiver operating characteristic (ROC) curve analysis.
RESULTS
Patients were divided into the LNM group (n = 17) and the non-LNM group (n = 44). The LNM group presented significantly higher APTw (3.7 ± 1.1% vs. 2.4 ± 1.0%, < 0.001), MK (1.065 ± 0.185 vs. 0.909 ± 0.189, = 0.005) and lower MD (0.989 ± 0.195 × 10 mm/s vs. 1.193 ± 0.337 ×10 mm/s, = 0.035) than the non-LNM group. APTw was an independent predictor (OR = 3.115, = 0.039) for evaluating the lymph node status through multivariate analysis. The area under the curve (AUC) of APTw (0.807) was higher than those of MK (AUC, 0.715) and MD (AUC, 0.675) for discriminating LNM from non-LNM, but the differences were not significant (all > 0.05). Moreover, the combination of APTw, MK, and MD yielded the highest AUC (0.864), with the corresponding sensitivity of 76.5% and specificity of 88.6%.
CONCLUSION
APTw and ZOOMit DKI parameters may serve as potential noninvasive biomarkers in predicting LNM of CC.
背景
本研究旨在探讨酰胺质子转移加权（APTw）成像联合ZOOMit扩散峰度成像（DKI）预测宫颈癌（CC）淋巴结转移（LNM）的可行性。
材料与方法
本回顾性研究纳入61例经病理证实的CC患者。采集APTw MRI和ZOOMit扩散加权成像（DWI）。计算原发肿瘤的APTw以及DKI参数的平均值，包括平均峰度（MK）和平均扩散率（MD）。使用Student's t检验或Mann-Whitney U检验比较LNM组和非LNM组之间的参数。进行二元逻辑回归分析以确定LNM状态与危险因素之间的关联。通过受试者操作特征（ROC）曲线分析评估这些定量参数及其组合预测LNM的诊断性能。
结果
患者分为LNM组（n = 17）和非LNM组（n = 44）。LNM组的APTw（3.7±1.1% vs. 2.4±1.0%，<0.001）、MK（1.065±0.185 vs. 0.909±0.189，=0.005）显著高于非LNM组，而MD（0.989±0.195×10⁻³mm²/s vs. 1.193±0.337×10⁻³mm²/s，=0.035）显著低于非LNM组。通过多因素分析，APTw是评估淋巴结状态的独立预测因子（OR = 3.115，=0.039）。在区分LNM与非LNM方面，APTw的曲线下面积（AUC）（0.807）高于MK（AUC，0.715）和MD（AUC，0.675），但差异无统计学意义（均>0.05）。此外，APTw、MK和MD的组合产生了最高的AUC（0.864），相应的灵敏度为76.5%，特异性为88.6%。
结论
APTw和ZOOMit DKI参数可能作为预测CC患者LNM的潜在非侵入性生物标志物。
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