Can IVIM help predict HCC recurrence after hepatectomy?

Zhang, Yao; Kuang, Sichi; Shan, Qungang; Rong, Dailin; Zhang, Zhongping; Yang, Hao; Wu, Jun; Chen, Jingbiao; He, Baorong; Deng, Ying; Roberts, Neil; Shen, Jun; Venkatesh, Sudhakar K.

doi:10.1007/s00330-019-06180-1

Cited by 31 publications

(30 citation statements)

References 52 publications

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“…These findings may be due to the pathophysiological phenomenon that increased cellular density and increased nuclear-to-cytoplasmic ratio would be more prominent than increased blood perfusion when HCC recurs. 21 In line with a previous study, ADC was not recognized as a significant predictor in either univariate or multivariate analysis. 35 In addition to the APT SI and D in tumor tissue, we found that the tumor size was also an independent predictor for tumor response, which is consistent with the results of previous studies.…”

Section: Discussionsupporting

confidence: 87%

“…35 In addition to the APT SI and D in tumor tissue, we found that the tumor size was also an independent predictor for tumor response, which is consistent with the results of previous studies. 36 Previous studies 7,21 on the prediction of early HCC recurrence focused on a single qMRI or conventional MRI techniques. For example, Zhang et al suggested that D, as measured by IVIM, may be a potential biomarker for response prediction prior to treatment 21 ; Chansik et al suggested that dynamic contrast-enhanced MRI can aid in determining the prognosis of HCC, providing a reference for clinical diagnosis and treatment.…”

Section: Discussionmentioning

confidence: 99%

“…36 Previous studies 7,21 on the prediction of early HCC recurrence focused on a single qMRI or conventional MRI techniques. For example, Zhang et al suggested that D, as measured by IVIM, may be a potential biomarker for response prediction prior to treatment 21 ; Chansik et al suggested that dynamic contrast-enhanced MRI can aid in determining the prognosis of HCC, providing a reference for clinical diagnosis and treatment. 7 Unlike these imaging features derived from contrast-enhanced MRI, quantitative IVIM and APT imaging techniques are not dependent on contrast agents and could be used in patients with hepatorenal insufficiency.…”

Section: Discussionmentioning

confidence: 99%

“…19,20 Moreover, a recent study has revealed that the IVIM-derived parameters may be a useful biomarker for preoperative prediction of recurrence after hepatectomy in patients with HCC. 21 Another preliminary study demonstrates that the APT imaging can act as a noninvasive biomarker for detecting early response to neoadjuvant chemotherapy in breast cancer patients. 11 However, whether the combination of IVIM-and APT-derived parameters can provide a better predictive performance has not been fully understood.…”

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confidence: 99%

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Prediction Model for Intermediate‐Stage Hepatocellular Carcinoma Response to Transarterial Chemoembolization

Jia

Yan

et al. 2020

Magnetic Resonance Imaging

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Background: The outcome of intermediate-stage hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) is greatly heterogeneous. Current means for predicting HCC response to TACE are lacking. Purpose: To investigate whether the combination of parameters derived from amide proton transfer (APT) and intravoxel incoherent motion (IVIM) imaging, and morphological characteristics of tumor can establish a better prediction model than the univariant model for HCC response to TACE. Study Type: Prospective. Subjects: 56 patients with intermediate-stage HCC (50 males and six females). Field Strength/Sequences: 3.0T; T 2-weighted-fast spin echo, 3D liver acquisition with volume flex, single-shot fast spin echo-planar imaging (EPI), spin echo-EPI. Assessment: Pretreatment APT signal intensities (SIs), apparent diffusion coefficient (ADC), true molecular diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) for tumor, peritumoral, and normal tissues were measured. Follow-up MRI scanning was performed, and the patients were classified as responders or nonresponders based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria. Statistical Tests: The imaging parameters were compared among the three tissues and between the two groups using analysis of variance (ANOVA) or two-sample t-test. The prediction model's variables were derived from univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC) curve analysis was used to explore the predictive performance. Results: Based on the logistic regression analysis results, we established a prediction model that integrated the APT SI and D values in the tumor tissue and the tumor size. ROC analyses revealed that the model was better able to predict tumor response to TACE (area under the ROC curve = 0.851) than the individual parameters on their own. Data Conclusion: A prediction model incorporating pretreatment APT SI, D in the tumor tissue and tumor size may be useful for predicting the response of intermediate-stage HCC to TACE. Level of Evidence: 1 Technical Efficacy: Stage 1

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Prediction Model for Intermediate‐Stage Hepatocellular Carcinoma Response to Transarterial Chemoembolization

Jia

Yan

et al. 2020

Magnetic Resonance Imaging

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“…Zhang et al[ 76 ] conducted IVIM-DWI on 157 patients with HBV-related HCC who underwent surgical resection. The results showed 47 cases of tumor recurrence, and a D value < 0.985 × 10 -3 mm 2 /s was found to be a risk factor for recurrence.…”

Section: Evaluation Of the Hcc Treatment Responsementioning

confidence: 99%

Progress of intravoxel incoherent motion diffusion-weighted imaging in liver diseases

Tao¹,

Zhou²,

Wang³

et al. 2020

WJCC

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Traditional magnetic resonance (MR) diffusion-weighted imaging (DWI) uses a single exponential model to obtain the apparent diffusion coefficient to quantitatively reflect the diffusion motion of water molecules in living tissues, but it is affected by blood perfusion. Intravoxel incoherent motion (IVIM)-DWI utilizes a double-exponential model to obtain information on pure water molecule diffusion and microcirculatory perfusion-related diffusion, which compensates for the insufficiency of traditional DWI. In recent years, research on the application of IVIM-DWI in the diagnosis and treatment of hepatic diseases has gradually increased and has achieved considerable progress. This study mainly reviews the basic principles of IVIM-DWI and related research progress in the diagnosis and treatment of hepatic diseases.

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A supervised deep neural network approach with standardized targets for enhanced accuracy of IVIM parameter estimation from multi‐SNR images

et al. 2022

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Extraction of intravoxel incoherent motion (IVIM) parameters from noisy diffusionweighted (DW) images using a biexponential fitting model is computationally challenging, and the reliability of the estimated perfusion-related quantities represents a limitation of this technique. Artificial intelligence can overcome the current limitations and be a suitable solution to advance use of this technique in both preclinical and clinical settings. The purpose of this work was to develop a deep neural network (DNN) approach, trained on numerical simulated phantoms with different signal to noise ratios (SNRs), to improve IVIM parameter estimation. The proposed approach is based on a supervised fully connected DNN having 3 hidden layers, 18 inputs and 3 targets with standardized values. 14 Â 10 3 simulated DW images, based on a Shepp-Logan phantom, were randomly generated with varying SNRs (ranging from 10 to 100). 7 Â 10 3 images (1000 for each SNR) were used for training. Performance accuracy was assessed in simulated images and the proposed approach was compared with the state-of-the-art Bayesian approach and other DNN algorithms. The DNN approach was also evaluated in vivo on a high-field MRI preclinical scanner. Our DNN approach showed an overall improvement in accuracy when compared with the Bayesian approach and other DNN methods in most of the simulated conditions. The in vivo results demonstrated the feasibility of the proposed approach in real settings and generated quantitative results comparable to those obtained using the Bayesian and unsupervised approaches, especially for D and f, and with lower variability in homogeneous regions. The DNN architecture proposed in this work outlines two innovative features as compared with other studies: (1) the use of standardized targets to improve the estimation of parameters, and (2) the implementation of a single DNN to enhance the IVIM fitting at different SNRs, providing a valuable alternative tool to compute IVIM parameters in conditions of high background noise.

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Can IVIM help predict HCC recurrence after hepatectomy?

Cited by 31 publications

References 52 publications

Prediction Model for Intermediate‐Stage Hepatocellular Carcinoma Response to Transarterial Chemoembolization

Prediction Model for Intermediate‐Stage Hepatocellular Carcinoma Response to Transarterial Chemoembolization

Progress of intravoxel incoherent motion diffusion-weighted imaging in liver diseases

A supervised deep neural network approach with standardized targets for enhanced accuracy of IVIM parameter estimation from multi‐SNR images

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