Application of Intravoxel Incoherent Motion in the Evaluation of Hepatocellular Carcinoma after Transarterial Chemoembolization.
Xiaofei YueYuting LuQiqi JiangXiangjun DongXuefeng KanJiawei WuXiang-Chuang KongPing HanJie YuQian LiPublished in: Current oncology (Toronto, Ont.) (2022)
(1) Background: To assess the efficacy of the quantitative parameters of intravoxel incoherent motion (IVIM) diffusion-weighted imaging for hepatocellular carcinoma (HCC) diagnosis after transarterial chemoembolization (TACE). (2) Methods: Fifty HCC patients after TACE were included and underwent MRI. All of the patients were scanned with the IVIM-DWI sequence and underwent TACE retreatment within 1 week. Referring to digital subtraction angiography (DSA) and MR enhanced images, two readers measured the f , D, and D* values of the tumor active area (TAA), tumor necrotic area (TNA), and adjacent normal hepatic parenchyma (ANHP). Then, the distinctions of the TAA, TNA, and ANHP were compared and we analyzed the differential diagnosis of the parameters in three tissues. (3) Results: For values of f and D, there were significant differences between any of the TAA, TNA, and ANHP ( p < 0.05). The values of f and D were the best indicators for identifying the TAA and TNA, with AUC values of 0.959 and 0.955, respectively. The values of f and D performed well for distinguishing TAA from ANHP, with AUC values of 0.835 and 0.753, respectively. (4) Conclusions: Quantitative IVIM-DWI was effective for evaluating tumor viability in HCC patients treated with TACE and may be helpful for non-invasive monitoring of the tumor viability.
Keyphrases
- diffusion weighted imaging
- contrast enhanced
- diffusion weighted
- magnetic resonance imaging
- end stage renal disease
- chronic kidney disease
- ejection fraction
- newly diagnosed
- prognostic factors
- optical coherence tomography
- computed tomography
- magnetic resonance
- peritoneal dialysis
- radiofrequency ablation
- randomized controlled trial
- clinical trial
- patient reported outcomes
- gene expression
- convolutional neural network