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Multiparametric diffusion models for histological characterization of pancreatic cancer: Insights from animal and clinical studies
LI Jiali  LÜ Chenxi  MA Siyuan  LI Zhen 

Cite this article as: LI J L, LÜ C X, MA S Y, et al. Multiparametric diffusion models for histological characterization of pancreatic cancer: Insights from animal and clinical studies[J]. Chin J Magn Reson Imaging, 2025, 16(5): 22-29. DOI:10.12015/issn.1674-8034.2025.05.004.


[Abstract] Objective To investigate the histological relevance and potential clinical value of multiparametric diffusion-weighted imaging (DWI) parameters for pancreatic cancer through animal experiments and a prospective clinical study.Materials and Methods Twelve xenograft mouse models of pancreatic cancer and twenty-five patients with histologically confirmed pancreatic ductal adenocarcinoma were enrolled. Multi-model DWI analysis was performed using the mono-exponential model (Mono), intravoxel incoherent motion (IVIM) model, diffusion kurtosis imaging (DKI), stretched exponential model (SEM), fractional-order calculus (FROC) model, and continuous-time random walk (CTRW) model. The extracted parameters included Mono_ADC, IVIM_D, DKI_MD, SEM_D, FROC_D, CTRW_D, IVIM_D*, IVIM_f, DKI_MK, SEM_α, FROC_β, FROC_mμ, CTRW_α, and CTRW_β. Histological correlations with DWI parameters were evaluated using Masson and Ki-67 staining in the animal cohort. In the clinical study, differences in DWI parameters between pancreatic cancer and pancreatic normal tissue were assessed, and ROC analysis was used to evaluate discriminative ability.Results In animal studies, DKI_MD was significantly negatively correlated with the degree of fibrosis (r = -0.85, P < 0.001), while CTRW_β (r = -0.82, P = 0.001) and FROC_β (r = -0.78, P = 0.002) were closely associated with Ki-67 expression. In clinical data, DWI parameters including DKI_MD, IVIM_f, and FROC_β differed significantly between pancreatic cancer and normal tissues (P < 0.05). DKI_MD showed the highest diagnostic performance individually (AUC = 0.757, 95% CI: 0.615 to 0.867), while the combined model (DKI_MD + FROC_β) achieved improved accuracy (AUC = 0.866, 95% CI: 0.739 to 0.945) with significantly better sensitivity (76%) and specificity (88%).Conclusions Multiparametric analysis using non-Gaussian DWI models provides valuable insights into the microstructural features of pancreatic cancer. Among them, DKI_MD and FROC_β demonstrated significant advantages in quantifying fibrosis and heterogeneity, indicating their potential as imaging biomarkers.
[Keywords] pancreatic cancer；magnetic resonance imaging；diffusion-weighted imaging；diffusion kurtosis imaging

LI Jiali1   LÜ Chenxi2   MA Siyuan2   LI Zhen1*  

1 Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China

2 Second Clinical College, Huazhong University of Science and Technology, Wuhan 430030, China

Corresponding author: LI Z, E-mail: zhenli@hust.edu.cn

Conflicts of interest   None.

Received  2025-04-25
Accepted  2025-05-10
DOI: 10.12015/issn.1674-8034.2025.05.004
Cite this article as: LI J L, LÜ C X, MA S Y, et al. Multiparametric diffusion models for histological characterization of pancreatic cancer: Insights from animal and clinical studies[J]. Chin J Magn Reson Imaging, 2025, 16(5): 22-29. DOI:10.12015/issn.1674-8034.2025.05.004.

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