The value of multiparameter MRI of early cervical cancer combined with SCC-Ag in predicting its pelvic lymph node metastasis

IF 3.5 3区医学 Q2 ONCOLOGY

Frontiers in Oncology Pub Date : 2024-09-11 DOI:10.3389/fonc.2024.1417933

Xiaoqian Xu, Fenghai Liu, Xinru Zhao, Chao Wang, Da Li, Liqing Kang, Shikai Liu, Xiaoling Zhang

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Abstract

PurposeTo investigate the value of multiparameter MRI of early cervical cancer (ECC) combined with pre-treatment serum squamous cell carcinoma antigen (SCC-Ag) in predicting its pelvic lymph node metastasis (PLNM).Material and methods115 patients with pathologically confirmed FIGO IB1~IIA2 cervical cancer were retrospectively included and divided into the PLNM group and the non-PLNM group according to pathological results. Quantitative parameters of the primary tumor include Ktrans, Kep, Ve from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), ADCmean, ADCmin, ADCmax, D, D* and f from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) were measured. Pre-treatment serum SCC-Ag was obtained. The difference of the above parameters between the two groups were compared using the student t-test or Mann-Whitney U test. Multivariate Logistic regression analysis was performed to determine independent risk factors. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic efficacy of individual parameters and their combination in predicting PLNM from ECC.ResultsThe PLNM group presented higher SCC-Ag [14.25 (6.74,36.75) ng/ml vs.2.13 (1.32,6.00) ng/ml, P<0.001] and lower Ktrans (0.51 ± 0.20 min-1 vs.0.80 ± 0.33 min-1, P < 0.001), ADCmean (0.85 ± 0.09 mm/s2 vs.1.06 ± 0.35 mm/s2, P<0.001), ADCmin [0.67 (0.61,0.75) mm/s2 vs. 0.75 (0.64,0.90) mm/s2, P = 0.012] and f (0.91 ± 0.09 vs. 0.27 ± 0.14, P = 0.001) than the non-LNM group. Multivariate analysis showed that SCC-Ag (OR = 1.154, P = 0.007), Ktrans (OR=0.003, P < 0.001) and f (OR = 0.001, P=0.036) were independent risk factors of PLNM. The combination of SCC-Ag, Ktrans and f possessed the best predicting efficacy for PLNM with an area under curve (AUC) of 0.896, which is higher than any individual parameter: SCC-Ag (0.824), Ktrans (0.797), and f (0.703). The sensitivity and specificity of the combination were 79.1% and 94.0%, respectively.ConclusionsQuantitative parameters Ktrans and f derived from DCE-MRI and IVIM-DWI of primary tumor and SCC-Ag have great value in predicting PLNM. The diagnostic efficacy of their combination has been further improved.

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早期宫颈癌合并 SCC-Ag 的多参数磁共振成像在预测其盆腔淋巴结转移中的价值

目的探讨早期宫颈癌（ECC）多参数磁共振成像结合治疗前血清鳞状细胞癌抗原（SCC-Ag）预测盆腔淋巴结转移（PLNM）的价值。测量原发肿瘤的定量参数，包括动态对比增强磁共振成像（DCE-MRI）的Ktrans、Kep、Ve，体外非相干运动扩散加权成像（IVIM-DWI）的ADCmean、ADCmin、ADCmax、D、D*和f。检测治疗前的血清 SCC-Ag。采用学生 t 检验或 Mann-Whitney U 检验比较两组患者上述参数的差异。进行多变量逻辑回归分析以确定独立的风险因素。结果 PLNM 组的 SCC-Ag[14.25 (6.74,36.75) ng/ml vs. 2.13 (1.32,6.75) ng/ml] 高于 ECC 组的 SCC-Ag[14.25 (6.74,36.75) ng/ml vs. 2.13 (1.32,6.75) ng/ml] 。.13 (1.32,6.00) ng/ml，P<0.001]，Ktrans（0.51 ± 0.20 min-1 vs.0.80 ± 0.33 min-1，P<0.001）、ADCmean（0.85 ± 0.09 mm/s2 vs.10.67 (0.61,0.75) mm/s2 vs. 0.75 (0.64,0.90) mm/s2, P = 0.012]和f (0.91 ± 0.09 vs. 0.27 ± 0.14, P = 0.001)。多变量分析显示，SCC-Ag（OR=1.154，P=0.007）、Ktrans（OR=0.003，P < 0.001）和 f（OR=0.001，P=0.036）是 PLNM 的独立危险因素。SCC-Ag、Ktrans 和 f 的组合对 PLNM 的预测效果最好，其曲线下面积（AUC）为 0.896，高于任何单个参数：SCC-Ag（0.824）、Ktrans（0.797）和 f（0.703）。结论由原发肿瘤的 DCE-MRI 和 IVIM-DWI 以及 SCC-Ag 得出的定量参数 Ktrans 和 f 在预测 PLNM 方面具有重要价值。结论DCE-MRI和IVIM-DWI得出的Ktrans和f定量参数在预测PLNM方面具有重要价值，两者结合的诊断效果也进一步提高。

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来源期刊

Frontiers in Oncology Biochemistry, Genetics and Molecular Biology-Cancer Research

CiteScore

6.20

自引率

10.60%

发文量

6641

审稿时长

14 weeks

期刊介绍： Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.

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