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Preoperative prediction of VETC in hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging at high b values: a pilot study

Chenhui Li, Wen Yan, Jinhuan Xie, Qianjuan Chen, Yi‐Wu Dang, Huiting Zhang, Hu Guo, Liling Long

2023Frontiers in Oncology12 citationsDOIOpen Access PDF

Abstract

Background: Vessels encapsulating tumor clusters (VETC) have been considered an important cause of hepatocellular carcinoma (HCC) metastasis. Purpose: To compare the potential of various diffusion parameters derived from the monoexponential model and four non-Gaussian models (DKI, SEM, FROC, and CTRW) in preoperatively predicting the VETC of HCC. Methods: 86 HCC patients (40 VETC-positive and 46 VETC-negative) were prospectively enrolled. Diffusion-weighted images were acquired using six b-values (range from 0 to 3000 s/mm2). Various diffusion parameters derived from diffusion kurtosis (DK), stretched-exponential (SE), fractional-order calculus (FROC), and continuous-time random walk (CTRW) models, together with the conventional apparent diffusion coefficient (ADC) derived from the monoexponential model were calculated. All parameters were compared between VETC-positive and VETC-negative groups using an independent sample t-test or Mann-Whitney U test, and then the parameters with significant differences between the two groups were combined to establish a predictive model by binary logistic regression. Receiver operating characteristic (ROC) analyses were used to assess diagnostic performance. Results: Among all studied diffusion parameters, only DKI_K and CTRW_α significantly differed between groups (P=0.002 and 0.004, respectively). For predicting the presence of VETC in HCC patients, the combination of DKI_K and CTRW_α had the larger area under the ROC curve (AUC) than the two parameters individually (AUC=0.747 vs. 0.678 and 0.672, respectively). Conclusion: DKI_K and CTRW_α outperformed traditional ADC for predicting the VETC of HCC.

Topics & Concepts

Hepatocellular carcinomaDiffusion MRIMedicineGaussianCarcinomaDiffusionRadiologyOncologyNuclear medicineInternal medicineMagnetic resonance imagingPhysicsThermodynamicsQuantum mechanicsMRI in cancer diagnosisHepatocellular Carcinoma Treatment and PrognosisRadiomics and Machine Learning in Medical Imaging
Preoperative prediction of VETC in hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging at high b values: a pilot study | Litcius