Dataset Information

Evaluation of Focal Liver Reaction after Proton Beam Therapy for Hepatocellular Carcinoma Examined Using Gd-EOB-DTPA Enhanced Hepatic Magnetic Resonance Imaging.

ABSTRACT:

Background

Proton beam therapy (PBT) achieves good local control for hepatocellular carcinoma (HCC), and toxicity tends to be lower than for photon radiotherapy. Focal liver parenchymal damage in radiotherapy is described as the focal liver reaction (FLR); the threshold doses (TDs) for FLR in the background liver have been analyzed in stereotactic ablative body radiotherapy and brachytherapy. To develop a safer approach for PBT, both TD and liver volume changes are considered clinically important in predicting the extent of damage before treatment, and subsequently in reducing background liver damage. We investigated appearance time, TDs and volume changes regarding FLR after PBT for HCC.

Material and methods

Patients who were treated using PBT and were followed up using gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (Gd-EOB-DTPA MRI) after PBT were enrolled. Sixty-eight lesions in 58 patients were eligible for analysis. MRI was acquired at the end of treatment, and at 1, 2, 3 and 6 months after PBT. We defined the FLR as a clearly depicted hypointense area on the hepatobiliary phase of Gd-EOB-DTPA MRI, and we monitored TDs and volume changes in the FLR area and the residual liver outside of the FLR area.

Results

FLR was depicted in all lesions at 3 months after PBT. In FLR expressed as the 2-Gy equivalent dose (?/? = 3 Gy), TDs did not differ significantly (27.0±6.4 CGE [10 fractions [Fr] vs. 30.5±7.3 CGE [20 Fr]). There were also no correlations between the TDs and clinical factors, and no significant differences between Child-Pugh A and B scores. The volume of the FLR area decreased and the residual liver volume increased, particularly during the initial 3 months.

Conclusion

This study established the FLR dose for liver with HCC, which might be useful in the prediction of remnant liver volume for PBT.

SUBMITTER: Takamatsu S

PROVIDER: S-EPMC5132228 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Evaluation of Focal Liver Reaction after Proton Beam Therapy for Hepatocellular Carcinoma Examined Using Gd-EOB-DTPA Enhanced Hepatic Magnetic Resonance Imaging.

Takamatsu Shigeyuki S Yamamoto Kazutaka K Maeda Yoshikazu Y Kawamura Mariko M Shibata Satoshi S Sato Yoshitaka Y Terashima Kazuki K Shimizu Yasuhiro Y Tameshige Yuji Y Sasaki Makoto M Asahi Satoko S Kondou Tamaki T Kobayashi Satoshi S Matsui Osamu O Gabata Toshifumi T

PloS one 20161201 12

<h4>Background</h4>Proton beam therapy (PBT) achieves good local control for hepatocellular carcinoma (HCC), and toxicity tends to be lower than for photon radiotherapy. Focal liver parenchymal damage in radiotherapy is described as the focal liver reaction (FLR); the threshold doses (TDs) for FLR in the background liver have been analyzed in stereotactic ablative body radiotherapy and brachytherapy. To develop a safer approach for PBT, both TD and liver volume changes are considered clinically ...[more]

PMID: 27907063

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Project description:BackgroundInserting diffusion weighted imaging (DWI) into the time interval between post contrast and hepatobiliary phase (HBP) is time saving and health economic friendly. However, whether DWI would be affected before and after Gd-EOB-DTPA is still unknown. This study aims to validate whether the DWI at both low and high b-values is affected before and after Gd-EOB-DTPA enhancement.MethodsFrom July 2019 to November 2019, seventy-three patients who satisfied the inclusion criteria were enrolled. Those patients were scanned with multiple b-value (b-value of 0, 50, 800, 1,000, and 1,200 s/mm2) DWI using a 3.0 T magnetic resonance (MR) scanner before and after the injection of Gd-EOB-DTPA. The final imaging diagnosis of the malignant liver lesions were made by histopathological analysis. The lesion-liver contrast intensity ratio (CIR) and the apparent diffusion coefficients (ADCs) of hepatic parenchyma and lesions at each b-value was evaluated. The Student's t-test or Mann-Whitney U test was used to compare the CIR and ADC between the MR images before and after contrast agent injection. In addition, the Student's t-test or Mann-Whitney U test was used to compare the ADC values between benign and malignant lesions. Receiver operating characteristics (ROC) curves were used to assess the area under the curve (AUC) of the ADC values in differentiating between benign and malignant lesions.ResultsFor the CIRs comparison, the CIRs showed no statistical significance before and after Gd-EOB-DTPA on b =0 (1.34±1.15 vs. 1.45±1.48, P=0.664), b=50 (1.23±1.13 vs. 1.35±1.34, P=0.982), b=800 (1.19±0.87 vs. 1.19±0.94, P=0.946), b=1,000 (1.21±0.90 vs. 1.32±1.05, P=0.294) and b=1,200 (1.25±1.03 vs. 1.45±1.48, P=0.165) s/mm2. For the ADC value comparison, the ADC also showed no statistical significance before and after Gd-EOB-DTPA on b=50 (4.04±2.82 vs. 3.91±3.00, P=0.151), b=800 (1.68±0.71 vs.1.67±0.76, P=0.163), b=1,000 (1.53±0.69 vs.1.50±0.70, P=0.078) and b=1,200 (1.48±0.66 vs. 1.48±0.70, P=0.294) s/mm2.ConclusionsDWI scanned between the interval of dynamic enhanced imaging and HBP imaging can save overall scanning time without influencing the CIRs, ADCs, and diagnostic capabilities of hepatic lesions at both low and high b-values.

Project description:BackgroundNuclear protein Ki-67 indicates the status of cell proliferation and has been regarded as an attractive biomarker for the prognosis of HCC. The aim of this study is to investigate which radiomics model derived from different sequences and phases of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI was superior to predict Ki-67 expression in hepatocellular carcinoma (HCC), then further to validate the optimal model for preoperative prediction of Ki-67 expression in HCC.MethodsThis retrospective study included 151 (training cohort: n = 103; validation cohort: n = 48) pathologically confirmed HCC patients. Radiomics features were extracted from the artery phase (AP), portal venous phase (PVP), hepatobiliary phase (HBP), and T2-weighted (T2W) images. A logistic regression with the least absolute shrinkage and selection operator (LASSO) regularization was used to select features to build a radiomics score (Rad-score). A final combined model including the optimal Rad-score and clinical risk factors was established. Receiver operating characteristic (ROC) curve analysis, Delong test and calibration curve were used to assess the predictive performance of the combined model. Decision cure analysis (DCA) was used to evaluate the clinical utility.ResultsThe AP radiomics model with higher decision curve indicating added more net benefit, gave a better predictive performance than the HBP and T2W radiomic models. The combined model (AUC = 0.922 vs. 0.863) including AP Rad-score and serum AFP levels improved the predictive performance more than the AP radiomics model (AUC = 0.873 vs. 0.813) in the training and validation cohort. Calibration curve of the combined model showed a good agreement between the predicted and the actual probability. DCA of the validation cohort revealed that at a range threshold probability of 30-60%, the combined model added more net benefit compared with the AP radiomics model.ConclusionsA combined model including AP Rad-score and serum AFP levels based on enhanced MRI can preoperatively predict Ki-67 expression in HCC.

Project description:ObjectivesThe accurate prediction of post-hepatectomy early recurrence in patients with hepatocellular carcinoma (HCC) is crucial for decision-making regarding postoperative adjuvant treatment and monitoring. We aimed to explore the feasibility of deep learning (DL) features derived from gadoxetate disodium (Gd-EOB-DTPA) MRI, qualitative features, and clinical variables for predicting early recurrence.MethodsIn this bicentric study, 285 patients with HCC who underwent Gd-EOB-DTPA MRI before resection were divided into training (n = 195) and validation (n = 90) sets. DL features were extracted from contrast-enhanced MRI images using VGGNet-19. Three feature selection methods and five classification methods were combined for DL signature construction. Subsequently, an mp-MR DL signature fused with multiphase DL signatures of contrast-enhanced images was constructed. Univariate and multivariate logistic regression analyses were used to identify early recurrence risk factors including mp-MR DL signature, microvascular invasion (MVI), and tumor number. A DL nomogram was built by incorporating deep features and significant clinical variables to achieve early recurrence prediction.ResultsMVI (p = 0.039), tumor number (p = 0.001), and mp-MR DL signature (p < 0.001) were independent risk factors for early recurrence. The DL nomogram outperformed the clinical nomogram in the training set (AUC: 0.949 vs. 0.751; p < 0.001) and validation set (AUC: 0.909 vs. 0.715; p = 0.002). Excellent DL nomogram calibration was achieved in both training and validation sets. Decision curve analysis confirmed the clinical usefulness of DL nomogram.ConclusionThe proposed DL nomogram was superior to the clinical nomogram in predicting early recurrence for HCC patients after hepatectomy.Key points• Deep learning signature based on Gd-EOB-DTPA MRI was the predominant independent predictor of early recurrence for hepatocellular carcinoma (HCC) after hepatectomy. • Deep learning nomogram based on clinical factors and Gd-EOB-DTPA MRI features is promising for predicting early recurrence of HCC. • Deep learning nomogram outperformed the conventional clinical nomogram in predicting early recurrence.

Dataset Information

Evaluation of Focal Liver Reaction after Proton Beam Therapy for Hepatocellular Carcinoma Examined Using Gd-EOB-DTPA Enhanced Hepatic Magnetic Resonance Imaging.

Background

Material and methods

Results

Conclusion

Publications

Evaluation of Focal Liver Reaction after Proton Beam Therapy for Hepatocellular Carcinoma Examined Using Gd-EOB-DTPA Enhanced Hepatic Magnetic Resonance Imaging.

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