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Time-dependent diffusion MRI for differentiating cervical cancer subtypes: impact of ROI delineation strategies on
Xuelin Ma1, Yanwan Li1, Siqi Yi1
1Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Objectives:
To investigate the impact of different ROI delineation strategies on the utility of Time-dependent diffusion MRI (TDD-MRI)-derived microstructural parameters for distinguishing adenocarcinoma (AC) from squamous cell carcinoma (SCC).
Methods:
In this prospective study, patients with pathologically confirmed cervical cancer who underwent TDD-MRI between August 2024 and June 2025 were enrolled. Three region-of-interest (ROI) delineation strategies were used: small solid ROI (ROIs), single-slice ROI (ROIss), and whole-volume ROI (ROIwt). Microstructural parameters including intracellular volume fraction (fin), extracellular diffusion coefficient (Dex), diameter, and cellularity, along with three apparent diffusion coefficient (ADC) measures were investigated. The intraclass correlation coefficient (ICC) was used to determine inter- and intra-readers reproducibility. Logistic regression was performed to predict pathological subtypes. Diagnostic performance was quantified by area under the receiver operating characteristic curve (AUC). Pearson's correlation analysis validated the relationship between TDD-MRI parameters and pathological measurements.
Results:
A total of 92 women (79 with SCC and 13 with AC) with cervical cancer (mean age, 55.5 ± 10.4 years) were included. For TDD-MRI-derived microstructural parameters and ADCs, intra- and inter-reader ICCs were 0.910-0.971 and 0.904-0.981, respectively. The ROIss strategy showed significant differences between AC and SCC in four parameters (all P < 0.05); ROIwt showed significance only for cellularity, and ROIs showed none. The ROIss-derived parameters achieved relatively favorable performance for distinguishing histologic subtypes (AUC = 0.821). The combined Dex, cellularity, and ADC40Hz model based on the ROIss approach achieved an AUC of 0.917. TDD-MRI-derived parameters showed correlations with histopathologic measurements (n = 15; r = 0.698-0.794; P < 0.01).
Conclusion:
TDD-MRI-based microstructural parameters derived from the ROIss show promise as effective imaging biomarkers to assist in differentiating histologic subtypes in cervical cancer.
