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Abstract Compartmentalization is a vital aspect of living cells to orchestrate intracellular processes. In a similar vein, constructing dynamic and responsive sub-compartments is key to synthetic cell engineering. In recent years, liquid-liquid phase separation via coacervation has offered an innovative avenue for creating membraneless organelles (MOs) within artificial cells. Here, we present a lab-on-a-chip system to reversibly trigger peptide-based coacervates within cell-mimicking confinements. We use double emulsion droplets (DEs) as our synthetic cell containers while pH-responsive elastin-like polypeptides (ELPs) act as the coacervate system. We first present a high-throughput microfluidic DE production enabling efficient encapsulation of the ELPs. The DEs are then harvested to perform multiple MO formation-dissolution cycles using pH as well as temperature variation. For controlled long-term visualization and modulation of the external environment, we developed an integrated microfluidic device for trapping and environmental stimulation of DEs, with negligible mechanical force, and demonstrated a proof-of-principle osmolyte-based triggering to induce multiple MO formation-dissolution cycles. In conclusion, our work showcases the use of DEs and ELPs in designing membraneless reversible compartmentalization within synthetic cells via physicochemical triggers. Additionally, presented on-chip platform can be applied over a wide range of phase separation and vesicle systems for applications in synthetic cells and beyond.

Abstract Background Early detection of recurrent or progressive HCC remains the strongest prognostic factor for survival. Dual tracer PET/CT imaging with [11C]CH and [18F]FDG can further increase detection rates as both tracers entail different metabolic pathways involved in HCC development. We investigated dual-tracer PET/CT in clinical decision making in patients suspected of recurrent or progressive HCC. All HCC patients who underwent both [11C]CH and [18F]FDG PET/CT in our institute from February 2018 to December 2021 were included. Both tracer PET/CT were within 4 weeks of each other with at least 6-month follow-up. Patients underwent dual tracer PET/CT because of unexplained and suspicious CT/MRI or sudden rise of serum tumour markers. A detected lesion was considered critical when the finding had prognostic consequences leading to treatment changes. Results Nineteen patients who underwent [11C]CH and [18F]FDG PET/CT were included of which all but six patients were previously treated for HCC. Dual-tracer critical finding detection rate was 95%, with [18F]FDG 68%, and [11C]CH 84%. Intrahepatic HCC recurrence finding rate was 65% for both tracers. [18F]FDG found more ablation site recurrences (4/5) compared to [11C]CH (2/5). Only [11C]CH found two needle tract metastases. Both tracers found 75% of the positive lymph nodes. Two new primary tumours were found, one by [18F]FDG and both by [11C]CH. Conclusions Our study favours a dual-tracer approach in HCC staging in high-risk patients or when conventional imaging is non-conclusive.

Hepatocellular adenomas (HCAs) are benign liver tumors associated with bleeding or malignant transformation. Data on the indication for surgery are scarce. We analyzed indications and outcome of patients operated for HCAs 50 mm (52%), suspicion of (pre)malignancy (28%), and (previous) bleeding (5.1%). No difference was observed in HCA‐subtype distribution between small and large tumors. Ninety‐six (43%) patients had a postoperative change in diagnosis. Independent risk factors for change in diagnosis were tumor size

Abstract Background Post-therapy [90Y] PET/CT-based dosimetry is currently recommended to validate treatment planning as [99mTc] MAA SPECT/CT is often a poor predictor of subsequent actual [90Y] absorbed dose. Treatment planning software became available allowing 3D voxel dosimetry offering tumour-absorbed dose distributions and dose-volume histograms (DVH). We aim to assess dose–response effects in post-therapy [90Y] PET/CT dosimetry in SIRT-treated HCC patients for predicting overall and progression-free survival (OS and PFS) and four-month follow-up tumour response (mRECIST). Tumour-absorbed dose and mean percentage of the tumour volume (V) receiving ≥ 100, 150, 200, or 250 Gy and mean minimum absorbed dose (D) delivered to 30%, 50%, 70%, and 90% of tumour volume were calculated from DVH’s. Depending on the mean tumour -absorbed dose, treated lesions were assigned to a

Purpose Doppler ultrasound (DUS) is widely used to detect vascular complications after pediatric liver transplantation (LT). This study aimed to assess the moment of first detection of vascular complications with DUS, and to determine the positive predictive value (PPV) of DUS.

Background: The similarity of gallbladder cancer and benign gallbladder lesions brings challenges to diagnosing gallbladder cancer (GBC). This study investigated whether a convolutional neural network (CNN) could adequately differentiate GBC from benign gallbladder diseases, and whether information from adjacent liver parenchyma could improve its performance. Methods: Consecutive patients referred to our hospital with suspicious gallbladder lesions with histopathological diagnosis confirmation and available contrast-enhanced portal venous phase CT scans were retrospectively selected. A CT-based CNN was trained once on gallbladder only and once on gallbladder including a 2 cm adjacent liver parenchyma. The best-performing classifier was combined with the diagnostic results based on radiological visual analysis. Results: A total of 127 patients were included in the study: 83 patients with benign gallbladder lesions and 44 with gallbladder cancer. The CNN trained on the gallbladder including adjacent liver parenchyma achieved the best performance with an AUC of 0.81 (95% CI 0.71–0.92), being >10% better than the CNN trained on only the gallbladder (p = 0.09). Combining the CNN with radiological visual interpretation did not improve the differentiation between GBC and benign gallbladder diseases. Conclusions: The CT-based CNN shows promising ability to differentiate gallbladder cancer from benign gallbladder lesions. In addition, the liver parenchyma adjacent to the gallbladder seems to provide additional information, thereby improving the CNN’s performance for gallbladder lesion characterization. However, these findings should be confirmed in larger multicenter studies.

Hyperammonemia can lead to encephalopathy and may be accompanied by a diagnostic dilemma. Imaging as well as biochemical analyses are the cornerstone for identifying possible underlying causes such as severe liver disease or urea cycle defect. We report a case of a patient that presented with neurological deficits based on hyperammonemia in the presence of a large pancreatic neuroendocrine tumor (PNET) and portosystemic shunts in the liver. Prior cases are rather scarce, and the exact mechanism is not fully understood. The case illustrates the added value of a multimodality imaging approach in patients presenting with hyperammonemia-induced encephalopathy.

A patient with cystic fibrosis (CF) with pancreatic insufficiency presented with jaundice due to an ampullary tumour. CF is known for a higher incidence of gastrointestinal malignancies. The patient suffered from pancreatic insufficiency. At computed tomography (CT), pancreatic lipomatosis with absence of the pancreatic duct was seen. This is uncommon, also in patients with CF. During surgery, a total pancreatectomy was performed, because there was no possibility to construct a duct to mucosa anastomosis due to the absence of the pancreatic duct and more importantly the pancreas was already afunctional. The presence of lipomatosis increases the risk of leakage at the pancreaticojejunal anastomosis. Therefore, it is important to take this phenomenon, in this case already visible on the preoperative CT scan, into account during the workup for surgery.

Hepatocellular adenomas (HCA) are rare benign tumors of the liver, occurring predominantly in females using oral contraceptives. Our case describes a 66-year-old woman presenting with a palpable mass in her upper abdomen. Contrast-enhanced computed tomography and magnetic resonance imaging showed a large exophytic mass protruding from the caudal border of liver segments IV and V, without visible metastases. Laparoscopic resection of the tumor and gallbladder was performed. Histopathological examination showed a hepatocellular carcinoma with areas of HNF1a-HCA (H-HCA). This case shows that malignant transformation is possible in H-HCA. We present our preoperative decision-making process, as well as the role of imaging techniques in this rare case.

BackgroundThree-dimensional (3D) volumetric ablation margin assessment after thermal ablation of liver tumors using software has been described, but its predictive value on treatment efficacy when accounting for other factors known to correlate ablation site recurrence (ASR) remains unknown.PurposeTo investigate 3D quantitative ablation margins (3D-QAMs) as an algorithm to predict ASR within 1 year after stereotactic microwave ablation (SMWA) for colorectal liver metastases (CRLM).Materials and MethodsSixty-five tumors in 47 patients from a prospective multicenter study of patients undergoing SMWA for CRLM were included in this retrospective 3D-QAM analysis. Using a previously developed algorithm, 3D-QAM defined as the distribution of tumor to ablation surface distances was assessed in co-registered pre- and post-ablation CT scans. The discriminatory power and optimal cutoff values for 3D-QAM were assessed using receiver operating characteristic (ROC) curves. Multivariable logistic regression analysis using generalized estimating equations was applied to investigate the impact of various 3D-QAM outputs on 1-year ASR while accounting for other known influencing factors.ResultsTen of the 65 (15.4%) tumors included for 3D-QAM analysis developed ASR. ROC analyses identified i) 3D-QAM 23% of the tumor surface, ii) 3D-QAM 45%, and iii) the minimal ablation margin (MAM) as the 3D-QAM outputs with optimal discriminatory qualities. The multivariable regression model without 3D-QAM yielded tumor diameter and KRAS mutation as 1-year ASR predictors. When adding 3D-QAM, this factor became the main predictor of 1-year ASR [odds ratio (OR) 21.67 (CI 2.48, 165.21) if defined as >23%

Purpose: (i) to compare two 2.45 GHz MWA devices with respect to AZV in relation to the applied energy after MWA in patients with hepatocellular carcinoma (HCC) or colorectal liver metastasis (CRLM) and (ii) to identify potential confounders for this relationship. Methods: In total, 102 tumours, 65 CRLM and 37 HCC were included in this retrospective analysis. Tumours were treated with Emprint (n = 71) or Neuwave (n = 31) MWA devices. Ablation treatment setting were recorded and applied energy was calculated. AZV and tumour volumes were segmented on the contrast-enhanced CT scans obtained 1 week after treatment. The AZV to applied energy R(AZV:E) ratios were calculated for each tumour treatment and compared between both MWA devices and tumour types. Results: R(AZV:E)EMPRINT was 0.41 and R(AZV:E)NEUWAVE was 0.81, p < 0.001. Moderate correlation between AZV and applied energy was found for Emprint (r = 0.57, R2 = 0.32, p < 0.001) and strong correlation was found for Neuwave (r = 0.78, R2 = 0.61, p < 0.001). R(AZV:E)CRLM was 0.45 and R(AZV:E)HCC was 0.52, p = 0.270. Conclusion: This study confirms the unpredictability of AZVs based on the applied output energy for HCC and CRLM. No significant differences in R(AZV:E) were observed between CRLM and HCC. Significantly lower R(AZV:E) was found for Emprint devices compared to Neuwave; however, reflected energy due to cable and antenna design remains unclear and might contribute to these differences.

Background: Approximately 26% of esophageal cancer (EC) patients do not respond to neoadjuvant chemoradiotherapy (nCRT), emphasizing the need for pre-treatment selection. The aim of this study was to predict non-response using a radiomic model on baseline 18F-FDG PET. Methods: Retrospectively, 143 18F-FDG PET radiomic features were extracted from 199 EC patients (T1N1-3M0/T2–4aN0-3M0) treated between 2009 and 2019. Non-response (n = 57; 29%) was defined as Mandard Tumor Regression Grade 4–5 (n = 44; 22%) or interval progression (n = 13; 7%). Randomly, 139 patients (70%) were allocated to explore all combinations of 24 feature selection strategies and 6 classification methods towards the cross-validated average precision (AP). The predictive value of the best-performing model, i.e AP and area under the ROC curve analysis (AUC), was evaluated on an independent test subset of 60 patients (30%). Results: The best performing model had an AP (mean ± SD) of 0.47 ± 0.06 on the training subset, achieved by a support vector machine classifier trained on five principal components of relevant clinical and radiomic features. The model was externally validated with an AP of 0.66 and an AUC of 0.67. Conclusion: In the present study, the best-performing model on pre-treatment 18F-FDG PET radiomics and clinical features had a small clinical benefit to identify non-responders to nCRT in EC.

Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven liver fibrosis stages between 2006 and 2018. The radiomics analyses for liver fibrosis staging were done by hepatic and hepatic–splenic features, respectively. The most predictive radiomic features were automatically selected by machine learning models. Results: When using splenic–hepatic features in the CT-based radiomics analysis, the average accuracy rates for significant fibrosis, advanced fibrosis, and cirrhosis were 88%, 82%, and 86%, and area under the receiver operating characteristic curves (AUCs) were 0.92, 0.81, and 0.85. The AUC of hepatic–splenic-based radiomics analysis with the ensemble classifier was 7% larger than that of hepatic-based analysis (p < 0.05). The most important features selected by machine learning models included both hepatic and splenic features, and they were consistent with the location maps indicating the focus of deep learning when predicting liver fibrosis stage. Conclusions: Adding CT-based splenic radiomic features to hepatic radiomic features increases radiomics analysis performance for liver fibrosis staging. The most important features of the radiomics analysis were consistent with the information exploited by deep learning.

Background: Positron emission tomography (PET) with 16α-[18F]-fluoro-17β-estradiol ([18F]-FES) can visualize estrogen receptor (ER) expression, but it is challenging to determine the ER status of liver metastases, due to high physiological [18F]-FES uptake. We evaluated whether [18F]-FES-PET can be used to determine the ER status of liver metastases, using corresponding liver biopsies as the gold standard. Methods: Patients with metastatic breast cancer (n = 23) were included if they had undergone a [18F]-FES-PET, liver metastasis biopsy, CT-scan, and [18F]-FDG-PET. [18F]-FES-PET scans were assessed by visual and quantitative analysis, tracer uptake was correlated with ER expression measured by immunohistochemical staining and the effects of region-of-interest size and background correction were determined. Results: Visual analysis allowed ER assessment of liver metastases with 100% specificity and 18% sensitivity. Quantitative analysis improved the sensitivity. Reduction of the region-of-interest size did not further improve the results, but background correction improved ER assessment, resulting in 83% specificity and 77% sensitivity. Using separate thresholds for ER+ and ER− metastases, positive and negative predictive values of 100% and 75%, respectively, could be obtained, although 30% of metastases remained inconclusive. Conclusion: In the majority of liver metastases, ER status can be determined with [18F]-FES-PET if background correction and separate thresholds are applied.

Portal biliopathy refers to biliary tree abnormalities in patients with peribiliary collateral vessels and non-neoplastic extrahepatic portal vein occlusion. These biliary abnormalities are caused by vascular compression and ischemic damage of the biliary tree, which can result in bile duct compression, stenosis, fibrotic strictures, bile duct dilation, and thickening of the bile duct wall. Portal biliopathy is difficult to distinguish from cholangiocarcinoma, IgG4-related disease, and sclerosing cholangitis. Although most patients are asymptomatic, portal biliopathy can lead to serious complications, such as recurrent cholangitis. This case illustrates the importance of including portal biliopathy in the differential diagnosis at an early stage, especially in patients with portal hypertension. With early recognition, the need for additional invasive diagnostic procedures such as biopsies is minimized. Pathogenesis, clinical presentation, diagnostics, and treatment options of portal biliopathy are described in the article.

Summary: Background: Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field. Methods: In this machine learning modelling study, we used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. Another retrospective dataset of 493 patients with these tumors enrolled under clinical protocols at National Institutes of Health (00-CH-0093) and the Netherlands (PRESCRIPT trial) was used to train and validate machine learning models according to selections of additional features. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. For comparison, 12 specialists provided predictions of metastatic disease using data from the training and external validation datasets. Findings: Prospective predictions indicated that plasma methoxytyramine could identify metastatic disease at sensitivities of 52% and specificities of 85%. The best performing machine learning model was based on an ensemble tree classifier algorithm that used nine features: plasma methoxytyramine, metanephrine, normetanephrine, age, sex, previous history of pheochromocytoma or paraganglioma, location and size of primary tumours, and presence of multifocal disease. This model had an area under the receiver operating characteristic curve of 0·942 (95% CI 0·894–0·969) that was larger (p

Introduction Benign liver tumours and cysts (BLTCs) comprise a heterogeneous group of cystic and solid lesions, including hepatic haemangioma, focal nodular hyperplasia and hepatocellular adenoma. Some BLTCs, for example, (large) hepatocellular adenoma, are at risk of complications. Incidence of malignant degeneration or haemorrhage is low in most other BLTCs. Nevertheless, the diagnosis BLTC may carry a substantial burden and patients may be symptomatic, necessitating treatment. The indications for interventions remain matter of debate. The primary study aim is to investigate patient-reported outcomes (PROs) of patients with BLTCs, with special regards to the influence of invasive treatment as compared with the natural course of the disease.Methods and analysis A nationwide observational cohort study of patients with BLTC will be performed between October 2021 and October 2026, the minimal follow-up will be 2 years. During surveillance, a questionnaire regarding symptoms and their impact will be sent to participants on a biannual basis and more often in case of invasive intervention. The questionnaire was previously developed based on PROs considered relevant to patients with BLTCs and their caregivers. Most questionnaires will be administered by computerised adaptive testing through the Patient-Reported Outcomes Measurement Information System. Data, such as treatment outcomes, will be extracted from electronic patient files. Multivariable analysis will be performed to identify patient and tumour characteristics associated with significant improvement in PROs or a complicated postoperative course.Ethics and dissemination The study was assessed by the Medical Ethics Committee of the University Medical Center Groningen and the Amsterdam UMC. Local consultants will provide information and informed consent will be asked of all patients. Results will be published in a peer-reviewed journal.Study registration NL8231—10 December 2019; Netherlands Trial Register.

Objective Benign liver tumours (BLT) are increasingly diagnosed as incidentalomas. Clinical implications and management vary across and within the different types of BLT. High-quality clinical practice guidelines are needed, because of the many nuances in tumour types, diagnostic modalities, and conservative and invasive management strategies. Yet, available observational evidence is subject to interpretation which may lead to practice variation. Therefore, we aimed to systematically search for available clinical practice guidelines on BLT, to critically appraise them, and to compare management recommendations.Design A scoping review was performed within MEDLINE, EMBASE, and Web of Science. All BLT guidelines published in peer-reviewed, and English language journals were eligible for inclusion. Clinical practice guidelines on BLT were analysed, compared, and critically appraised using the Appraisal of Guidelines, Research and Evaluation (AGREE II) checklist regarding hepatic haemangioma, focal nodular hyperplasia (FNH), and hepatocellular adenoma (HCA). Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations (PRISMA) for scoping reviews were adhered to.Results The literature search yielded unique 367 papers, 348 were excluded after screening of title/abstract, and 16 after full-text screening. Three guidelines were included: the American College of Gastroenterology (ACG; 2014), Brazilian Society of Hepatology (SBH; 2015), and European Association for the Study of the Liver (EASL; 2016). There was no uniformity in the assessment methods for grading and gravity of recommendations between guidelines. Among observed differences were: (1) indications for biopsy in all three tumours; (2) advices on contraceptive pills and follow-up in FNH and HCA; (3) use of an individualised approach to HCA; (4) absence of recommendations for treatment of HCA in men; and (5) approaches to HCA subtype identification on magnetic resonance imaging.Conclusion Recognising differences in recommendations can assist in harmonisation of practice standards and identify unmet needs in research. This may ultimately contribute to improved global patient care.