Intravoxel Incoherent Motion Improves the Accuracy of Preoperative Prediction of Vessels Encapsulating Tumor Clusters

Min Li1,2, Ge Zhang2, Jing Li2

  • 1Department of Radiology, Chengdu Sixth People's Hospital, Chengdu, Sichuan, People's Republic of China.

Insights

Intravoxel incoherent motion (IVIM) imaging can accurately predict the vessels encapsulating tumor clusters (VETC) pattern in hepatocellular carcinoma (HCC). This noninvasive method aids in stratifying recurrence risk for HCC patients.

Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Hepatocellular carcinoma (HCC) with a vessels encapsulating tumor clusters (VETC) pattern indicates a higher risk of recurrence and metastasis.
  • The unique vascular structure of VETC may influence perfusion and diffusion characteristics, detectable by intravoxel incoherent motion (IVIM) imaging.

Purpose of the Study:

  • To utilize preoperative IVIM to predict the VETC pattern in HCC.
  • To perform noninvasive, preoperative risk stratification for HCC recurrence based on IVIM findings.

Main Methods:

  • Prospective inclusion of 116 patients with suspicious HCC.
  • Radiologists independently assessed radiologic features and measured IVIM parameters (ADC, D, D*, f).
  • Logistic regression and ROC curve analyses identified predictors of VETC pattern; Kaplan-Meier analysis assessed recurrence-free survival.

Main Results:

  • The VETC pattern was identified in 25% of HCC cases.
  • The pseudo-diffusion fraction (f) value, elevated serum alpha-fetoprotein, and intratumor necrosis were independent predictors of the VETC pattern.
  • A combined model incorporating these factors achieved an AUC of 0.854, significantly improving predictive performance over conventional models and correlating with higher early recurrence risk.

Conclusions:

  • Preoperative IVIM imaging improves the accuracy of VETC pattern prediction in HCC.
  • IVIM enables noninvasive preoperative risk stratification for HCC recurrence, particularly for VETC-positive cases.
Abstract