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In a multicenter, controlled trial, patients undergoing transplantation of a liver from a donor after circulatory death were randomly assigned to receive the liver after hypothermic oxygenated machine perfusion or conventional static cold storage. Hypothermic perfusion led to a lower risk of post-transplantation nonanastomotic biliary strictures.

Objectives Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation of the diagnostic decisions made by deep learning. Methods The liver fibrosis staging network (LFS network) was developed at contrast-enhanced CT images in the portal venous phase in 252 patients with histologically proven liver fibrosis stage. To give a visual explanation of the diagnostic decisions made by the LFS network, Gradient-weighted Class Activation Mapping (Grad-cam) was used to produce location maps indicating where the LFS network focuses on when predicting liver fibrosis stage. Results The LFS network had areas under the receiver operating characteristic curve of 0.92, 0.89, and 0.88 for staging significant fibrosis (F2–F4), advanced fibrosis (F3–F4), and cirrhosis (F4), respectively, on the test set. The location maps indicated that the LFS network had more focus on the liver surface in patients without liver fibrosis (F0), while it focused more on the parenchyma of the liver and spleen in case of cirrhosis (F4). Conclusions Deep learning methods are able to exploit CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage. Therefore, we suggest using the entire upper abdomen on CT images when developing deep learning–based liver fibrosis staging algorithms. Key Points • Deep learning algorithms can stage liver fibrosis using contrast-enhanced CT images, but the algorithm is still used as a black box and lacks transparency . • Location maps produced by Gradient-weighted Class Activation Mapping can indicate the focus of the liver fibrosis staging network . • Deep learning methods use CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage .

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.

BackgroundPost-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 < 120 Gy or ≥ 120 Gy group.ResultsThirty patients received 36 SIRT treatments, totalling 43 lesions. Median tumour-absorbed dose was significantly different between the ≥ 120 Gy (n = 28, 207 Gy, IQR 154–311 Gy) and < 120 Gy group (n = 15, 62 Gy, IQR 49–97 Gy, p <0 .01). Disease control (DC) was found more frequently in the ≥ 120 Gy group (79%) compared to < 120 Gy (53%). Mean tumour-absorbed dose optimal cut-off predicting DC was 131 Gy. Tumour control probability was 54% (95% CI 52–54%) for a mean tumour-absorbed dose of 120 Gy and 90% (95% CI 87–92%) for 284 Gy. Only D30 was significantly different between DC and progressive disease (p = 0.04). For the ≥ 120 Gy group, median OS and PFS were longer (median OS 33 months, [range 8–33 months] and median PFS 23 months [range 4–33 months]) than the < 120 Gy group (median OS 17 months, [range 5–33 months] and median PFS 13 months [range 1–33 months]) (p < 0.01 and p = 0.03, respectively).ConclusionsHigher 3D voxel-based tumour-absorbed dose in patients with HCC is associated with four-month DC and longer OS and PFS. DVHs in [90Y] SIRT could play a role in evaluative dosimetry.

BackgroundWe aimed to determine the anorectal physiological factors associated with rectocele formation.MethodsFemale patients (N = 32) with severe constipation, fecal incontinence, or suspicion of rectocele, who had undergone magnetic resonance defecography and anorectal function tests between 2015 and 2021, were retrospectively included for analysis. The anorectal function tests were used to measure pressure in the anorectum during defecation. Rectocele characteristics and pelvic floor anatomy were determined with magnetic resonance defecography. Constipation severity was determined with the Agachan score. Information regarding constipation-related symptoms was collected.ResultsMean rectocele size during defecation was 2.14 ± 0.88 cm. During defecation, the mean anal sphincter pressure just before defecation was 123.70 ± 67.37 mm Hg and was associated with rectocele size (P = 0.041). The Agachan constipation score was moderately correlated with anal sphincter pressure just before defecation (r = 0.465, P = 0.022), but not with rectocele size (r = 0.276, P = 0.191). During defecation, increased anal sphincter pressure just before defecation correlated moderately and positively with straining maneuvers (r = 0.539, P = 0.007) and defecation blockage (r = 0.532, P = 0.007). Rectocele size correlated moderately and positively with the distance between the pubococcygeal line and perineum (r = 0.446, P = 0.011).ConclusionIncreased anal sphincter pressure just before defecation is correlated with the rectocele size. Based on these results, it seems important to first treat the increased anal canal pressure before considering surgical rectocele repair to enhance patient outcomes.

ObjectivesDetecting ablation site recurrence (ASR) after thermal ablation remains a challenge for radiologists due to the similarity between tumor recurrence and post-ablative changes. Radiomic analysis and machine learning methods may show additional value in addressing this challenge. The present study primarily sought to determine the efficacy of radiomic analysis in detecting ASR on follow-up computed tomography (CT) scans. The second aim was to develop a visualization tool capable of emphasizing regions of ASR between follow-up scans in individual patients.Materials and methodsLasso regression and Extreme Gradient Boosting (XGBoost) classifiers were employed for modeling radiomic features extracted from regions of interest delineated by two radiologists. A leave-one-out test (LOOT) was utilized for performance evaluation. A visualization method, creating difference heatmaps (diff-maps) between two follow-up scans, was developed to emphasize regions of growth and thereby highlighting potential ASR.ResultsA total of 55 patients, including 20 with and 35 without ASR, were included in the radiomic analysis. The best performing model was achieved by Lasso regression tested with the LOOT approach, reaching an area under the curve (AUC) of 0.97 and an accuracy of 92.73%. The XGBoost classifier demonstrated better performance when trained with all extracted radiomic features than without feature selection, achieving an AUC of 0.93 and an accuracy of 89.09%. The diff-maps correctly highlighted post-ablative liver tumor recurrence in all patients.ConclusionsMachine learning-based radiomic analysis and growth visualization proved effective in detecting ablation site recurrence on follow-up CT scans.

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.

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. Compartmentalization within living cells is vital to orchestrate intracellular processes, but effective compartmentalization and organization within synthetic cells remains a key challenge. Here, the authors report a lab-on-a-chip system to reversibly trigger the formation of peptide-based coacervates as membraneless organelles via pH/temperature/osmolyte variations within cell-mimicking confinements.

BackgroundEarly 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.ResultsNineteen 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.ConclusionsOur study favours a dual-tracer approach in HCC staging in high-risk patients or when conventional imaging is non-conclusive.