Time-dependent diffusion magnetic resonance imaging for the analysis of parotid gland tumors

Di Geng; Liu-Ning Zhu; Jun Liu; Xian-Ce Zhao; Yi-Shi Wang; Xiao-Quan Xu; Fei-Yun Wu

doi:10.1177/02841851241313108

Time-dependent diffusion magnetic resonance imaging for the analysis of parotid gland tumors

Acta Radiol. 2025 Jan 21:2841851241313108. doi: 10.1177/02841851241313108. Online ahead of print.

Authors

Di Geng^{1

2}, Liu-Ning Zhu³, Jun Liu², Xian-Ce Zhao⁴, Yi-Shi Wang⁵, Xiao-Quan Xu², Fei-Yun Wu²

Affiliations

¹ Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, PR China.
² Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China.
³ Department of Stomatology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China.
⁴ Philips Healthcare, Shanghai, PR China.
⁵ MR Collaboration, Siemens Healthineers Ltd, Beijing, PR China.

PMID: 39835432
DOI: 10.1177/02841851241313108

Abstract

Background: Different parotid tumors differ in terms of treatment strategies due to their distinct biological behaviors. Time-dependent diffusion magnetic resonance imaging (t_d-dMRI) can characterize and quantify the cytological indexes, and then aid the differential diagnosis of various tumors. However, the value of t_d-dMRI in the analysis of parotid gland tumors remains unclear.

Purpose: To investigate the value of quantitative parameters derived from t_d-dMRI in the analysis of parotid gland tumors.

Material and methods: In total, 39 patients with parotid gland tumors were prospectively enrolled, including 24 patients with polymorphic adenomas (PAs), eight with Warthin's tumors (WTs), and seven with malignant tumors (MTs). T_d-dMRI was performed for preoperative evaluation. Intracellular volume fraction (V_in), mean cell size (d), extracellular diffusion coefficient (D_ex), and cellularity were obtained based on the Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion model, and compared among the three groups. One-way ANOVA, Kruskal-Wallis test, and receiver operating characteristic (ROC) curve analysis were performed for further statistical analysis as appropriate.

Results: Significant differences were found in all t_d-dMRI-derived indexes among PAs, WTs, and MTs (all P < 0.05). V_in was the sole parameter with significant differences for all sub-group comparisons (PAs vs. WTs, P < 0.001; PAs vs. MTs, P = 0.031; WTs vs. MTs, P = 0.047). With V_in values of 0.267, 0.231, and 0.260 as threshold, respectively, optimal performance levels were obtained for differentiating PAs from WTs (area under the ROC curve [AUC]=0.932, sensitivity=0.917, and specificity=0.875), PAs from MTs (AUC=0.744, sensitivity=0.833, and specificity=0.714), and WTs from MTs (AUC=0.750, sensitivity=0.875, and specificity=0.714).

Conclusion: Microstructural parameters derived from t_d-dMRI, especially V_in, might be promising imaging biomarkers for characterizing parotid gland tumors.

Keywords: Parotid neoplasm; cellularity; extracellular diffusion coefficient; intracellular volume fraction; time-dependent diffusion magnetic resonance imaging.