Lymph Node Metastases Prediction in Cervical Cancer Using Time-Dependent Diffusion MRI and Macromolecular Proton Fraction Imaging

利用时变扩散磁共振成像和高分子质子分数成像预测宫颈癌淋巴结转移

阅读:1

Abstract

Purpose To examine whether time-dependent diffusion MRI (T(d)-dMRI) and macromolecular proton fraction (MPF) mapping-derived quantitative metrics can effectively distinguish between cervical cancer with and without lymph node metastasis (LNM) before treatment. Materials and Methods In this prospective study of adults with clinically suspected cervical cancer who underwent T(d)-dMRI, MPF mapping, and pulsed gradient spin-echo diffusion-weighted imaging (DWI(PGSE)) examinations between October 2023 and June 2025, authors calculated T(d)-dMRI-derived parameters (cellularity, diameter, intracellular volume fraction [V(in)], and extracellular diffusivity [D(ex)]), MPF, and DWI(PGSE)-derived parameter (pulsed gradient spin-echo apparent diffusion coefficient [ADC(PGSE)]). Through Ridge regression analysis, the authors identified independent predictors of LNM and developed a composite diagnostic tool using logistic regression analysis. To evaluate tool performance, the area under the receiver operating characteristic curve was determined. Results Among 98 female individuals with cervical cancer (mean age, 56.69 years ± 11.63 [SD]), participants who were LNM positive exhibited higher cellularity, V(in), and MPF but lower diameter, D(ex), and ADC(PGSE) than their counterparts who were LNM negative (P < .001 to P = .007). Cellularity, maximum tumor diameter, and MPF were independent predictors of LNM status, with their combination yielding the best diagnostic performance (area under the receiver operating characteristic curve, 0.95; 95% CI: 0.89, 0.98). The performance of this combination surpassed that of individual imaging modality, including DWI(PGSE) (ADC(PGSE)), and MPF, as well as any individual parameter, including cellularity, V(in), diameter, and D(ex). Conclusion T(d)-dMRI and MPF mapping were effective for predicting LNM in cervical cancer, with the combination of cellularity, maximum tumor diameter, and MPF showing the best diagnostic performance. Keywords: Time-Dependent Diffusion MRI, Macromolecular Proton Fraction, Cervical Cancer, Lymph Node Metastases © RSNA, 2026.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。