Prospective characterization of incident hepatic steatosis in pediatric and adolescent patients after total pancreatectomy with islet autotransplantation

BackgroundHepatic steatosis has been described as a common finding in adults following total pancreatectomy with islet autotransplantation (TPIAT) but it is unknown if this occurs in children and adolescents.ObjectivesTo define the frequency of post-TPIAT hepatic steatosis in a sample of children and adolescents and to identify clinical predictors of incident steatosis post-TPIAT.MethodsIn this prospective study, consecutive participants at least 1-month post-TPIAT underwent a liver MRI with proton density fat fraction (PDFF) and blood draw at our pediatric academic medical center between April 2021 and January 2022. Comparison clinical pre-TPIAT liver MRI or ultrasound and insulin use and graft function data were extracted from the medical record. T-tests were used for the comparison of means across continuous variables between participants with and without post-TPIAT steatosis.ResultsA total of 20 participants (mean: 13 ± 4 years; 12 female) were evaluated. Mean liver PDFF at research MRI was 7.4 ± 6.2% (range: 2–25%). Seven participants (35%) had categorical hepatic steatosis (PDFF>5%) post-TPIAT, five of whom had pre-TPIAT steatosis, reflecting a 13% (2/15; 95% CI: 2–40%) incidence of post-TPIAT steatosis. Participant characteristics were not significantly different between subgroups with and without post-TPIAT steatosis. Mean PDFF at research MRI was not different between graft function subgroups (7.5% optimal/good vs. 7.3% marginal/failure; p = .96).ConclusionOur study shows a moderate prevalence but low incidence of hepatic steatosis in a small sample of children and adolescents post-TPIAT. This study raises questions about a causal relationship between TPIAT and hepatic steatosis.     

A Pilot First-in-Human Study of Embrace,a Polyethylene Glycol-Based Liquid Embolic Agent,in the Embolization of Malignant and Benign Hypervascular Tumors

PurposeTo investigate the safety and efficacy of an aqueous polyethylene glycol-based liquid embolic agent, Embrace Hydrogel Embolic System (HES), in the treatment of benign and malignant hypervascular tumors.Materials and MethodsA prospective, single-arm, multicenter study included 8 patients, 5 males and 3 females, with a median age of 58.5 years (30–85 years), who underwent embolization in 8 tumors between October 2019 and May 2020. Technical success was defined as successful delivery of HES to the index vessel, with disappearance of >90% of the targeted vascular enhancement or, for portal vein embolization, occlusion of the portal branches to the liver segments for future resection. The volume of HES administered, ease of use (5 point Likert scale), administration time, and adverse events (AEs) were recorded. Evaluation was performed at 7, 30, and 90 days via clinical assessment and blood testing, and follow-up imaging was performed at 30 days.ResultsEight patients were enrolled, and 10 embolizations were performed in 8 lesions. Tumors included hepatocellular carcinoma (n = 4), renal angiomyolipoma (n = 3), and intrahepatic cholangiocarcinoma (n = 1). Technical success was 100%, and the average ease of use was 3.3 ± 1.0 SD. The HES delivery time was 1–28 minutes (median, 16.5 minutes), and the HES volume injected was 0.4–4.0 mL (median, 1.3 mL). All patients reached 30-day follow-up with imaging, and 6 patients reached 90-day follow-up. There were 3 serious AEs in 2 patients that were unrelated to the embolic agent.ConclusionHES resulted in a 100% embolization technical success rate. The product ease of use was acceptable, and no target vessel recanalization was noted on follow-up imaging at 30 days.     

A new analytical cut-off point for determining 18 years of age using MRI on medial clavicular epiphysis

Evaluation of the ossification of the medial clavicular epiphysis plays a key role in forensic age estimation. The purpose of the present study was to assess a new numerical cut-off at the age of 18 years, taking into consideration Magnetic Resonance (MR) images of the medial clavicular epiphysis. We analyzed 163 MR scans of Italian subjects aged between 14 and 25 years. Using the data obtained we calculated two ratios: REM-1 (ratio between the length of the whole epiphysis and the length of the metaphysis) and REM-2 (ratio between the length of epiphyseal-metaphyseal fusion and the length of the metaphysis). In 68 out of 163 cases it was not possible to measure REM-2. The reproducibility was demonstrated using the Intraclass Correlation Coefficient (ICC) (Cronbach’s alpha > 0.80). REM-1 and REM-2 were compared in each category of age (adult and minor) by the Wilcoxon signed-rank test. The cut-off points for measurements of REM-1 and REM-2 were determined by logistic regression. For REM-1, the cut-off scores were 0.83 for all individuals (accuracy = 94.77%) and males (accuracy = 96.05%), and 0.86 for females (accuracy = 92.30%). For REM-2, the cut-off values were 0.40 for all individuals and males (accuracy = 100.00%), and 0.41 for females (accuracy = 100.00%). Finally, receiver operating characteristic (ROC) curves for age classification based on REM-1 and REM-2 were constructed, showing that REM-2 had the highest discriminative power. Thus, a new cut-off model for predicting the age of majority has been introduced, conducting a quantitative analysis thanks to the use of a high-resolution imaging tool.     

MR-safety in clinical practice at 7T: Evaluation of a multistep screening process in 1819 subjects

IntroductionMR facilities must implement and maintain adequate screening and safety procedures to ensure safety during MR examinations. The aim of this study was to evaluate a multi-step MR safety screening process used at a 7T facility regarding incidence of different types of safety risks detected during the safety procedure.MethodsSubjects scheduled for an MR examination and having entered the 7T facility during 2016–2019 underwent a pre-defined multi-step MR safety screening process. Screening documentation of 1819 included subjects was reviewed, and risks identified during the different screening steps were compiled. These data were also related to documented decisions made by a 7T MR safety committee and reported MR safety incidents.ResultsPassive or active implants (n = 315) were identified in a screening form and/or an additional documented interview in 305 subjects. Additional information not previously self-reported by the subject, regarding implants necessitating safety decisions performed by the staff was revealed in the documented interview in 102 subjects (106 items). In total, the 7T MR safety committee documented a decision in 36 (2%) of the included subjects. All of these subjects were finally cleared for scanning.ConclusionA multi-step screening process allows a thorough MR screening of subjects, avoiding safety incidents. Different steps in the process allow awareness to rise and items to be detected that were missed in earlier steps.Implications for practiceSafety questions posed at a single timepoint during an MR screening process might not reveal all safety risks. Repetition and rephrasing of screening questions leads to increased detection of safety risks. This could be effectively mitigated by a multi-step screening process. A multi-disciplinary safety committee is efficient at short notice responding to unexpected safety issues.     

Comparing Radiomics features of tumour and healthy liver tissue in a limited CT dataset: A machine learning study

IntroductionLiver cancer lesions on Computed Tomography (CT) withholds a great amount of data, which is not visible to the radiologists and radiographer. Radiomics features can be extracted from the lesions and used to train Machine Learning (ML) algorithms to predict between tumour and liver tissue. The purpose of this study was to investigate and classify Radiomics features extracted from liver tumours and normal liver tissue in a limited CT dataset.MethodsThe Liver Tumour Segmentation Benchmark (LiTS) dataset consisting of 131 CT scans of the liver with segmentations of tumour tissue and healthy liver was used to extract Radiomic features. Extracted Radiomic features included size, shape, and location extracted with morphological and statistical techniques according to the International Symposium on Biomedical Imaging manual. Relevant features was selected with chi2 correlation and principal component analysis (PCA) with tumour and healthy liver tissue as outcome according to a consensus between three experienced radiologists. Logistic regression, random forest and support vector machine was used to train and validate the dataset with a 10-fold cross-validation method and the Grid Search as hyper-parameter tuning. Performance was evaluated with sensitivity, specificity and accuracy.ResultsThe performance of the ML algorithms achieved sensitivities, specificities and accuracy ranging from 96.30% (95% CI: 81.03%–99.91%) to 100.00% (95% CI: 86.77%–100.00%), 91.30% (95% CI: 71.96%–98.93%) to 100.00% (95% CI: 83.89%–100.00%)and 94.00% (95% CI: 83.45%–98.75%) to 100.00% (95% CI: 92.45%–100.00%), respectively.ConclusionML algorithms classifies Radiomics features extracted from healthy liver and tumour tissue with perfect accuracy. The Radiomics signature allows for a prognostic biomarker for hepatic tumour screening on liver CT.Implications for practiceDifferentiation between tumour and liver tissue with Radiomics ML algorithms have the potential to increase the diagnostic accuracy, assist in the decision-making of supplementary multiphasic enhanced medical imaging, as well as for developing novel prognostic biomarkers for liver cancer patients.