Dataset Information

Respiratory motion artefacts in Gd-EOB-DTPA (Primovist/Eovist) and Gd-DOTA (Dotarem)-enhanced dynamic phase liver MRI after intensified and standard pre-scan patient preparation: A bi-institutional analysis.

ABSTRACT: OBJECTIVE:The objective of this study is to evaluate if intensified pre-scan patient preparation (IPPP) that comprises custom-made educational material on dynamic phase imaging and supervised pre-imaging breath-hold training in addition to standard informative conversation with verbal explanation of breath-hold commands (standard pre-scan patient preparation-SPPP) might reduce the incidence of gadoxetate disodium (Gd-EOB-DTPA)-related transient severe respiratory motion (TSM) and severity of respiratory motion (RM) during dynamic phase liver MRI. MATERIAL AND METHODS:In this bi-institutional study 100 and 110 patients who received Gd-EOB-DTPA for dynamic phase liver MRI were allocated to either IPPP or SPPP at site A and B. The control group comprised 202 patients who received gadoterate meglumine (Gd-DOTA) of which each 101 patients were allocated to IPPP or SPPP at site B. RM artefacts were scored retrospectively in dynamic phase images (1: none- 5: extensive) by five and two blinded readers at site A and B, respectively, and in the hepatobiliary phase of the Gd-EOB-DTPA-enhanced scans by two blinded readers at either site. RESULTS:The incidence of TSM was 15% at site A and 22.7% at site B (p = 0.157). IPPP did not reduce the incidence of TSM in comparison to SPPP: 16.7% vs. 21.6% (p = 0.366). This finding was consistent at site A: 12% vs. 18% (p = 0.401) and site B: 20.6% vs. 25% (p = 0.590). The TSM incidence in patients with IPPP and SPPP did not differ significantly between both sites (p = 0.227; p = 0.390). IPPP did not significantly mitigate RM in comparison to SPPP in any of the Gd-EOB-DTPA-enhanced dynamic phases and the hepatobiliary phase in patients without TSM (all p?0.072). In the Gd-DOTA control group on the other hand, IPPP significantly mitigated RM in all dynamic phases in comparison to SPPP (all p?0.031). CONCLUSIONS:We conclude that Gd-EOB-DTPA-related TSM cannot be mitigated by education and training and that Gd-EOB-DTPA-related breath-hold difficulty does not only affect the subgroup of patients with TSM or exclusively the arterial phase as previously proposed.

SUBMITTER: Wybranski C

PROVIDER: S-EPMC7083299 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Respiratory motion artefacts in Gd-EOB-DTPA (Primovist/Eovist) and Gd-DOTA (Dotarem)-enhanced dynamic phase liver MRI after intensified and standard pre-scan patient preparation: A bi-institutional analysis.

Wybranski Christian C Siedek Florian F Damm Robert R Gazis Angelos A Wenzel Ortrud O Haneder Stefan S Persigehl Thorsten T Steinhauser Susanne S Pech Maciej M Fischbach Frank F Fischbach Katharina K

PloS one 20200320 3

<h4>Objective</h4>The objective of this study is to evaluate if intensified pre-scan patient preparation (IPPP) that comprises custom-made educational material on dynamic phase imaging and supervised pre-imaging breath-hold training in addition to standard informative conversation with verbal explanation of breath-hold commands (standard pre-scan patient preparation-SPPP) might reduce the incidence of gadoxetate disodium (Gd-EOB-DTPA)-related transient severe respiratory motion (TSM) and severit ...[more]

PMID: 32196535

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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

Respiratory motion artefacts in Gd-EOB-DTPA (Primovist/Eovist) and Gd-DOTA (Dotarem)-enhanced dynamic phase liver MRI after intensified and standard pre-scan patient preparation: A bi-institutional analysis.

Publications

Respiratory motion artefacts in Gd-EOB-DTPA (Primovist/Eovist) and Gd-DOTA (Dotarem)-enhanced dynamic phase liver MRI after intensified and standard pre-scan patient preparation: A bi-institutional analysis.

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