Purpose To examine whether time-dependent diffusion MRI (Td-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 Td-dMRI, MPF mapping, and pulsed gradient spin-echo diffusion-weighted imaging (DWIPGSE) examinations between October 2023 and June 2025, authors calculated Td-dMRI-derived parameters (cellularity, diameter, intracellular volume fraction Vin, and extracellular diffusivity Dex), MPF, and DWIPGSE-derived parameter (pulsed gradient spin-echo apparent diffusion coefficient ADCPGSE). 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, Vin, and MPF but lower diameter, Dex, and ADCPGSE than their counterparts who were LNM negative (P 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 DWIPGSE (ADCPGSE), and MPF, as well as any individual parameter, including cellularity, Vin, diameter, and Dex. Conclusion Td-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.
Meng et al. (Fri,) studied this question.