Development and evaluation of a cervical cancer-specific restriction spectrum imaging (RSI) model
Year: 2026
Type: article
Abstract: Objective Cervical cancer is the fourth most common cause of cancer and cancerrelated mortality among women worldwide. MRI is standard of care for cervical cancer staging. The objective of this project is to evaluate the feasibility and performance of an advanced diffusion-weighted imaging (DWI) MRI technique for cervical cancer. Methods Forty-four patients with cervical cancer and 22 healthy volunteers underwent 3 T pelvic MRI with reduced field-of-view (FOV) multi-shell diffusion-weighted imaging (DWI; b = 0–3000 s/mm2). Patients were divided into model development and independent testing cohorts. Healthy volunteers provided reference distributions for Zscore mapping. RSI models with multi-exponential components were fitted to tumor signals. For each model, the outputs were voxel-wise compartmental signal contributions (Ci,N) from each water component within tissue. Model performance was assessed using the Bayesian Information Criterion (BIC), tumor contrast-to-noise ratio (CNR), and Z -scores. Conventional apparent diffusion coefficient (ADC) was estimated from full-FOV DWI (b = 50–1000 s/mm2). Results The tetra-exponential model achieved the highest CNR, with only a 6% BIC increase relative to the bi-exponential model. In an independent cohort, CNR was significantly higher in restricted compartments C1,3 (median 14.3) and C1,4 (median 26.2) compared with ADC. Tumor Z -scores were elevated in C1,3 (median 3.8) and C1,4 (median 8.0), whereas other compartments remained near zero. Conclusions RSI with Z-score mapping improved cervical cancer conspicuity compared with ADC, producing interpretable maps that localized tumor signal to restricted diffusion compartments. Based on BIC and CNR, the tetra-exponential RSI model was optimal, supporting RSI as a feasible non-contrast imaging strategy for cervical cancer evaluation.
Source: Magnetic Resonance Imaging
Authors: Ana E. Rodríguez-Soto, Christopher Conlin, Jingjing Zou, Sheida Ebrahimi, Alexandra Besser, Stephan Jordan, Alexandra Schlein, Elin Lundström, Joshua Kuperman, Anders Dale, Tyler Seibert, Michael Hahn, Cynthia S. Santillan, Christopher R. Weil, Gaiane M. Rauch, Jyoti Mayadev, Michael McHale, Rebecca Rakow-Penner
Language: English
Topic: MRI in cancer diagnosis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,380
OpenAlex ID: W7154597252
