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

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.