Active learning using adaptable task-based prioritisation.

Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert annotation, for label-efficient model training. We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning, for multi-class segmentation tasks. The controller is optimised by rewarding positive task-specific performance gain, within a Markov decision process (MDP) environment that also optimises the task predictor. In this work, the task predictor is a segmentation network. A meta-reinforcement learning algorithm is proposed with multiple MDPs, such that the pre-trained controller can be adapted to a new MDP that contains data from different institutes and/or requires segmentation of different organs or structures within the abdomen. We present experimental results using multiple CT datasets from more than one thousand patients, with segmentation tasks of nine different abdominal organs, to demonstrate the efficacy of the learnt prioritisation controller function and its cross-institute and cross-organ adaptability. We show that the proposed adaptable prioritisation metric yields converging segmentation accuracy for a new kidney segmentation task, unseen in training, using between approximately 40% to 60% of labels otherwise required with other heuristic or random prioritisation metrics. For clinical datasets of limited size, the proposed adaptable prioritisation offers a performance improvement of 22.6% and 10.2% in Dice score, for tasks of kidney and liver vessel segmentation, respectively, compared to random prioritisation and alternative active sampling strategies.

A bile-based microRNA signature for differentiating malignant from benign pancreaticobiliary disease.

Differentiating between pancreatic ductal adenocarcinoma (PDAC) and cholangiocarcinoma (CCA) is crucial for the appropriate course of treatment, especially with advancements in the role of neoadjuvant chemotherapies for PDAC, compared to CCA. Furthermore, benign pancreaticobiliary diseases can mimic malignant disease, and indeterminate lesions may require repeated investigations to achieve a diagnosis. As bile flows in close proximity to these lesions, we aimed to establish a bile-based microRNA (miRNA) signature to discriminate between malignant and benign pancreaticobiliary diseases. We performed miRNA discovery by global profiling of 800 miRNAs using the NanoString nCounter platform in prospectively collected bile samples from malignant (n = 43) and benign (n = 14) pancreaticobiliary disease. Differentially expressed miRNAs were validated by RT-qPCR and further assessed in an independent validation cohort of bile from malignant (n = 37) and benign (n = 38) pancreaticobiliary disease. MiR-148a-3p was identified as a discriminatory marker that effectively distinguished malignant from benign pancreaticobiliary disease in the discovery cohort (AUC = 0.797 [95% CI 0.68-0.92]), the validation cohort (AUC = 0.772 [95% CI 0.66-0.88]), and in the combined cohorts (AUC = 0.752 [95% CI 0.67-0.84]). We also established a two-miRNA signature (miR-125b-5p and miR-194-5p) that distinguished PDAC from CCA (validation: AUC = 0.815 [95% CI 0.67-0.96]; and combined cohorts: AUC = 0.814 [95% CI 0.70-0.93]). Our research stands as the largest, multicentric, global profiling study of miRNAs in the bile from patients with pancreaticobiliary disease. We demonstrated their potential as clinically useful diagnostic tools for the detection and differentiation of malignant pancreaticobiliary disease. These bile miRNA biomarkers could be developed to complement current approaches for diagnosing pancreaticobiliary cancers.

British Society of Gastroenterology guidelines for the diagnosis and management of cholangiocarcinoma.

These guidelines for the diagnosis and management of cholangiocarcinoma (CCA) were commissioned by the British Society of Gastroenterology liver section. The guideline writing committee included a multidisciplinary team of experts from various specialties involved in the management of CCA, as well as patient/public representatives from AMMF (the Cholangiocarcinoma Charity) and PSC Support. Quality of evidence is presented using the Appraisal of Guidelines for Research and Evaluation (AGREE II) format. The recommendations arising are to be used as guidance rather than as a strict protocol-based reference, as the management of patients with CCA is often complex and always requires individual patient-centred considerations.

Performance and Safety of EUS Ablation Techniques for Pancreatic Cystic Lesions: A Systematic Review and Meta-Analysis.

BACKGROUND: Pancreatic cystic lesions (PCL) represent an increasingly diagnosed condition with significant burden to patients' lives and medical resources. Endoscopic ultrasound (EUS) ablation techniques have been utilized to treat focal pancreatic lesions. This systematic review with meta-analysis aims to assess the efficacy of EUS ablation on PCL in terms of complete or partial response and safety. METHODS: A systematic search in Medline, Cochrane and Scopus databases was performed in April 2023 for studies assessing the performance of the various EUS ablation techniques. The primary outcome was complete cyst resolution, defined as cyst disappearance in follow-up imaging. Secondary outcomes included partial resolution (reduction in PCL size), and adverse events rate. A subgroup analysis was planned to evaluate the impact of the available ablation techniques (ethanol, ethanol/paclitaxel, radiofrequency ablation (RFA), and lauromacrogol) on the results. Meta-analyses using a random effects model were conducted and the results were reported as percentages with 95% confidence intervals (95%CI). RESULTS: Fifteen studies (840 patients) were eligible for analysis. Complete cyst resolution after EUS ablation was achieved in 44% of cases (95%CI: 31-57; 352/767; I2 = 93.7%), and the respective partial response rate was 30% (95%CI: 20-39; 206/767; I2 = 86.1%). Adverse events were recorded in 14% (95%CI: 8-20; 164/840; I2 = 87.2%) of cases, rated as mild in 10% (95%CI: 5-15; 128/840; I2 = 86.7%), and severe in 4% (95%CI: 3-5; 36/840; I2 = 0%). The subgroup analysis for the primary outcome revealed rates of 70% (95%CI: 64-76; I2 = 42.3%) for ethanol/paclitaxel, 44% (95%CI: 33-54; I2= 0%) for lauromacrogol, 32% (95%CI: 27-36; I2 = 88.4%) for ethanol, and 13% (95%CI: 4-22; I2 = 95.8%) for RFA. Considering adverse events, the ethanol-based subgroup rated the highest percentage (16%; 95%CI: 13-20; I2 = 91.0%). CONCLUSION: EUS ablation of pancreatic cysts provides acceptable rates of complete resolution and a low incidence of severe adverse events, with chemoablative agents yielding higher performance rates.

Serum biomarker-based early detection of pancreatic ductal adenocarcinomas with ensemble learning.

BACKGROUND: Earlier detection of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcomes, as it is mostly detected at advanced stages which are associated with poor survival. Developing non-invasive blood tests for early detection would be an important breakthrough. METHODS: The primary objective of the work presented here is to use a dataset that is prospectively collected, to quantify a set of cancer-associated proteins and construct multi-marker models with the capacity to predict PDAC years before diagnosis. The data used is part of a nested case-control study within the UK Collaborative Trial of Ovarian Cancer Screening and is comprised of 218 samples, collected from a total of 143 post-menopausal women who were diagnosed with pancreatic cancer within 70 months after sample collection, and 249 matched non-cancer controls. We develop a stacked ensemble modelling technique to achieve robustness in predictions and, therefore, improve performance in newly collected datasets. RESULTS: Here we show that with ensemble learning we can predict PDAC status with an AUC of 0.91 (95% CI 0.75-1.0), sensitivity of 92% (95% CI 0.54-1.0) at 90% specificity, up to 1 year prior to diagnosis, and at an AUC of 0.85 (95% CI 0.74-0.93) up to 2 years prior to diagnosis (sensitivity of 61%, 95% CI 0.17-0.83, at 90% specificity). CONCLUSIONS: The ensemble modelling strategy explored here outperforms considerably biomarker combinations cited in the literature. Further developments in the selection of classifiers balancing performance and heterogeneity should further enhance the predictive capacity of the method.

Pancreatic cancers are most frequently detected at an advanced stage. This limits treatment options and contributes to the dismal survival rates currently recorded. The development of new tests that could improve detection of early-stage disease is fundamental to improve outcomes. Here, we use advanced data analysis techniques to devise an early detection test for pancreatic cancer. We use data on markers in the blood from people enrolled on a screening trial. Our test correctly identifies as positive for pancreatic cancer 91% of the time up to 1 year prior to diagnosis, and 78% of the time up to 2 years prior to diagnosis. These results surpass previously reported tests and should encourage further evaluation of the test in different populations, to see whether it should be adopted in the clinic.

Endoscopic ultrasound guided gastrojejunostomy in the treatment of gastric outlet obstruction: multi-centre experience from the United Kingdom.

BACKGROUND: Endoscopic ultrasound guided gastrojejunostomy (EUS-GJ) with lumen apposing metal stents has recently emerged as a viable option, as an alternative to surgical gastrojejunostomy and endoscopic enteral stenting, for managing gastric outlet obstruction (GOO). We aim to perform a retrospective analysis of the efficacy, safety and outcomes of EUS-GJ performed at three tertiary institutions in the United Kingdom. METHODS: Consecutive patients who underwent EUS-GJ between August 2018 and March 2021 were identified from a prospectively maintained database. Data were obtained from interrogation of electronic health records. RESULTS: Twenty five patients (15 males) with a median age of 63 years old (range 29-80) were included for analysis. 88% (22/25) of patients had GOO due to underlying malignant disease. All patients were deemed surgically inoperable or at high surgical risk. Both technical and clinical success were achieved in 92% (23/25) of patients. There was an improvement in the mean Gastric Outlet Obstruction Scoring System scores following a technically successful EUS-GJ (2.52 vs 0.68, p < 0.01). Adverse events occurred in 2/25 patients (8%), both due to stent maldeployment necessitating endoscopic closure of the gastric defect with clips. Long-term follow-up data were available for 21 of 23 patients and the re-intervention rate was 4.8% (1/21) over a median follow-up period of 162 (range 5-474) days. CONCLUSION: EUS-GJ in carefully selected patients is an effective and safe procedure when performed by experienced endoscopists.

Tissue-Specific Human Extracellular Matrix Scaffolds Promote Pancreatic Tumour Progression and Chemotherapy Resistance.

Over 80% of patients with pancreatic ductal adenocarcinoma (PDAC) are diagnosed at a late stage and are locally advanced or with concurrent metastases. The aggressive phenotype and relative chemo- and radiotherapeutic resistance of PDAC is thought to be mediated largely by its prominent stroma, which is supported by an extracellular matrix (ECM). Therefore, we investigated the impact of tissue-matched human ECM in driving PDAC and the role of the ECM in promoting chemotherapy resistance. Decellularized human pancreata and livers were recellularized with PANC-1 and MIA PaCa-2 (PDAC cell lines), as well as PK-1 cells (liver-derived metastatic PDAC cell line). PANC-1 cells migrated into the pancreatic scaffolds, MIA PaCa-2 cells were able to migrate into both scaffolds, whereas PK-1 cells were able to migrate into the liver scaffolds only. These differences were supported by significant deregulations in gene and protein expression between the pancreas scaffolds, liver scaffolds, and 2D culture. Moreover, these cell lines were significantly more resistant to gemcitabine and doxorubicin chemotherapy treatments in the 3D models compared to 2D cultures, even after confirmed uptake by confocal microscopy. These results suggest that tissue-specific ECM provides the preserved native cues for primary and metastatic PDAC cells necessary for a more reliable in vitro cell culture.

Accuracy and agreement of a large panel of endosonographers for endomicroscopy-guided virtual biopsy of pancreatic cystic lesions.

BACKGROUND: Although emerging data evidences that EUS-guided needle-based confocal laser endomicroscopy (nCLE) accurately diagnoses pancreatic cystic lesions (PCLs), there are a lack of interobserver agreement (IOA) studies utilizing reference histopathological diagnosis and for specific PCL subtypes. Hence, we sought to assess the IOA, intra-observer reliability (IOR), and diagnostic performance of EUS-nCLE using a large cohort of patients with histopathological diagnosis amongst a broad panel of international observers. METHODS: EUS-nCLE videos (n = 76) of subjects with PCLs [intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm (MCN), serous cystadenoma (SCA), pseudocyst, and cystic-neuroendocrine tumors/solid pseudopapillary neoplasm (cystic-NET/SPN)], simulating clinical prevalence rates were obtained from 3 prospective studies. An international panel of 13 endosonographers with nCLE experience, blinded to all PCL data, evaluated the video library twice with a two-week washout for PCL differentiation (mucinous vs. non-mucinous) and subtype diagnosis. RESULTS: The IOA (κ = 0.82, 95% CI 0.77-0.87) and IOR (κ = 0.82, 95% CI 0.78-0.85) were "almost perfect" to differentiate mucinous vs. non-mucinous PCLs. For PCL subtype, IOA was highest for SCA (almost perfect; κ = 0.85), followed by IPMN (substantial, κ = 0.72), and cystic-NET/SPN (substantial, κ = 0.73). The IOA was moderate for MCN (κ = 0.47), and pseudocyst (κ = 0.57). Compared to histopathology, observers differentiated mucinous vs. non-mucinous PCLs with high accuracy (94.8%, 95% CI 93.3-96.1). For detecting specific PCLs subtypes, EUS-nCLE was highly accurate in diagnosing non-mucinous cysts (SCA: 98%; cystic-NET/SPN: 96%; pseudocyst: 96%) and slightly less accurate for mucinous lesions (IPMN: 86%; MCN: 84%). CONCLUSION: Diagnosis of PCLs by EUS-nCLE guided virtual biopsy is very accurate and reliable for the most prevalent pancreatic cysts in clinical practice.

Clinical relevance of biomarkers in cholangiocarcinoma: critical revision and future directions.

Cholangiocarcinoma (CCA) is a malignant tumour arising from the biliary system. In Europe, this tumour frequently presents as a sporadic cancer in patients without defined risk factors and is usually diagnosed at advanced stages with a consequent poor prognosis. Therefore, the identification of biomarkers represents an utmost need for patients with CCA. Numerous studies proposed a wide spectrum of biomarkers at tissue and molecular levels. With the present paper, a multidisciplinary group of experts within the European Network for the Study of Cholangiocarcinoma discusses the clinical role of tissue biomarkers and provides a selection based on their current relevance and potential applications in the framework of CCA. Recent advances are proposed by dividing biomarkers based on their potential role in diagnosis, prognosis and therapy response. Limitations of current biomarkers are also identified, together with specific promising areas (ie, artificial intelligence, patient-derived organoids, targeted therapy) where research should be focused to develop future biomarkers.

Deep hashing for global registration of untracked 2D laparoscopic ultrasound to CT.

PURPOSE: The registration of Laparoscopic Ultrasound (LUS) to CT can enhance the safety of laparoscopic liver surgery by providing the surgeon with awareness on the relative positioning between critical vessels and a tumour. In an effort to provide a translatable solution for this poorly constrained problem, Content-based Image Retrieval (CBIR) based on vessel information has been suggested as a method for obtaining a global coarse registration without using tracking information. However, the performance of these frameworks is limited by the use of non-generalisable handcrafted vessel features. METHODS: We propose the use of a Deep Hashing (DH) network to directly convert vessel images from both LUS and CT into fixed size hash codes. During training, these codes are learnt from a patient-specific CT scan by supplying the network with triplets of vessel images which include both a registered and a mis-registered pair. Once hash codes have been learnt, they can be used to perform registration with CBIR methods. RESULTS: We test a CBIR pipeline on 11 sequences of untracked LUS distributed across 5 clinical cases. Compared to a handcrafted feature approach, our model improves the registration success rate significantly from 48% to 61%, considering a 20 mm error as the threshold for a successful coarse registration. CONCLUSIONS: We present the first DH framework for interventional multi-modal registration tasks. The presented approach is easily generalisable to other registration problems, does not require annotated data for training, and may promote the translation of these techniques.

Endoscopic biliary therapy in the era of bariatric surgery.

There is an increasing demand and availability of bariatric surgery, with a range of procedures performed, some leading to altered upper gastrointestinal anatomy. The patient population undergoing bariatric surgery is also at increased risk of gallstones and biliary stone disease. Endoscopy (ie, endoscopic retrograde cholangiopancreatography) is the cornerstone of management of biliary stone disease, but may be challenging after bariatric surgery. In this review the endoscopic, surgery assisted, or percutaneous options that may be considered are discussed, based on the details of surgical anatomy and available expertise.

Blood levels of adiponectin and IL-1Ra distinguish type 3c from type 2 diabetes: Implications for earlier pancreatic cancer detection in new-onset diabetes.

BACKGROUND: Screening for pancreatic ductal adenocarcinoma (PDAC) in populations at high risk is recommended. Individuals with new-onset type 2 diabetes mellitus (NOD) are the largest high-risk group for PDAC. To facilitate screening, we sought biomarkers capable of stratifying NOD subjects into those with type 2 diabetes mellitus (T2DM) and those with the less prevalent PDAC-related diabetes (PDAC-DM), a form of type 3c DM commonly misdiagnosed as T2DM. METHODS: Using mass spectrometry- and immunoassay-based methodologies in a multi-stage analysis of independent sample sets (n=443 samples), blood levels of 264 proteins were considered using Ingenuity Pathway Analysis, literature review and targeted training and validation. FINDINGS: Of 30 candidate biomarkers evaluated in up to four independent patient sets, 12 showed statistically significant differences in levels between PDAC-DM and T2DM. The combination of adiponectin and interleukin-1 receptor antagonist (IL-1Ra) showed strong diagnostic potential, (AUC of 0.91; 95% CI: 0.84-0.99) for the distinction of T3cDM from T2DM. INTERPRETATION: Adiponectin and IL-1Ra warrant further consideration for use in screening for PDAC in individuals newly-diagnosed with T2DM. FUNDING: North West Cancer Research, UK, Cancer Research UK, Pancreatic Cancer Action, UK.

Voice-Assisted Image Labeling for Endoscopic Ultrasound Classification Using Neural Networks.

Ultrasound imaging is a commonly used technology for visualising patient anatomy in real-time during diagnostic and therapeutic procedures. High operator dependency and low reproducibility make ultrasound imaging and interpretation challenging with a steep learning curve. Automatic image classification using deep learning has the potential to overcome some of these challenges by supporting ultrasound training in novices, as well as aiding ultrasound image interpretation in patient with complex pathology for more experienced practitioners. However, the use of deep learning methods requires a large amount of data in order to provide accurate results. Labelling large ultrasound datasets is a challenging task because labels are retrospectively assigned to 2D images without the 3D spatial context available in vivo or that would be inferred while visually tracking structures between frames during the procedure. In this work, we propose a multi-modal convolutional neural network (CNN) architecture that labels endoscopic ultrasound (EUS) images from raw verbal comments provided by a clinician during the procedure. We use a CNN composed of two branches, one for voice data and another for image data, which are joined to predict image labels from the spoken names of anatomical landmarks. The network was trained using recorded verbal comments from expert operators. Our results show a prediction accuracy of 76% at image level on a dataset with 5 different labels. We conclude that the addition of spoken commentaries can increase the performance of ultrasound image classification, and eliminate the burden of manually labelling large EUS datasets necessary for deep learning applications.

Smart Nanoparticles as Advanced Anti-Akt Kinase Delivery Systems for Pancreatic Cancer Therapy.

Pancreatic cancer is one of the deadliest cancers partly due to late diagnosis, poor drug delivery to the target site, and acquired resistance to therapy. Therefore, more effective therapies are urgently needed to improve the outcome of patients. In this work, we have tested self-assembling genetically engineered polymeric nanoparticles formed by elastin-like recombinamers (ELRs), carrying a small peptide inhibitor of the protein kinase Akt, in both PANC-1 and patient-derived pancreatic cancer cells (PDX models). Nanoparticle cell uptake was measured by flow cytometry, and subcellular localization was determined by confocal microscopy, which showed a lysosomal localization of these nanoparticles. Furthermore, metabolic activity and cell viability were significantly reduced after incubation with nanoparticles carrying the Akt inhibitor in a time- and dose-dependent fashion. Self-assembling 73 ± 3.2 nm size nanoparticles inhibited phosphorylation and consequent activation of Akt protein, blocked the NF-κB signaling pathway, and triggered caspase 3-mediated apoptosis. Furthermore, in vivo assays showed that ELR-based nanoparticles were suitable devices for drug delivery purposes with long circulating time and minimum toxicity. Hence, the use of these smart nanoparticles could lead to the development of more effective treatment options for pancreatic cancer based on the inhibition of Akt.

Biliary Strictures and Cholangiocarcinoma - Untangling a Diagnostic Conundrum.

Cholangiocarcinoma is an uncommon and highly aggressive biliary tract malignancy with few manifestations until late disease stages. Diagnosis is currently achieved through a combination of clinical, biochemical, radiological and histological techniques. A number of reported cancer biomarkers have the potential to be incorporated into diagnostic pathways, but all lack sufficient sensitivity and specificity limiting their possible use in screening and early diagnosis. The limitations of standard serum markers such as CA19-9, CA125 and CEA have driven researchers to identify multiple novel biomarkers, yet their clinical translation has been slow with a general requirement for further validation in larger patient cohorts. We review recent advances in the diagnostic pathway for suspected CCA as well as emerging diagnostic biomarkers for early detection, with a particular focus on non-invasive approaches.

Identifying symptoms associated with diagnosis of pancreatic exocrine and neuroendocrine neoplasms: a nested case-control study of the UK primary care population.

BACKGROUND: Pancreatic cancer has the worst survival rate among all cancers. Almost 70% of patients in the UK were diagnosed at Stage IV. AIM: This study aimed to investigate the symptoms associated with the diagnoses of pancreatic ductal adenocarcinoma (PDAC) and pancreatic neuroendocrine neoplasms (PNEN), and comparatively characterise the symptomatology between the two tumour types to inform earlier diagnosis. DESIGN AND SETTING: A nested case-control study in primary care was conducted using data from the QResearch® database. Patients aged ≥25 years and diagnosed with PDAC or PNEN during 2000 to 2019 were included as cases. Up to 10 controls from the same general practice were matched with each case by age, sex, and calendar year using incidence density sampling. METHOD: Conditional logistic regression was used to investigate the association between the 42 shortlisted symptoms and the diagnoses of PDAC and (or) PNEN in different timeframes relative to the index date, adjusting for patients' sociodemographic characteristics, lifestyle, and relevant comorbidities. RESULTS: A total of 23 640 patients were identified as diagnosed with PDAC and 596 with PNEN. Of the symptoms identified, 23 were significantly associated with PDAC, and nine symptoms with PNEN. The two alarm symptoms for both tumours were jaundice and gastrointestinal bleeding. The two newly identified symptoms for PDAC were thirst and dark urine. The risk of unintentional weight loss may be longer than 2 years before the diagnosis of PNEN. CONCLUSION: PDAC and PNEN have overlapping symptom profiles. The QCancer® (pancreas) risk prediction model could be updated by including the newly identified symptoms and comorbidities, which could help GPs identify high-risk patients for timely investigation in primary care.

Can we screen for pancreatic cancer? Identifying a sub-population of patients at high risk of subsequent diagnosis using machine learning techniques applied to primary care data.

BACKGROUND: Pancreatic cancer (PC) represents a substantial public health burden. Pancreatic cancer patients have very low survival due to the difficulty of identifying cancers early when the tumour is localised to the site of origin and treatable. Recent progress has been made in identifying biomarkers for PC in the blood and urine, but these cannot be used for population-based screening as this would be prohibitively expensive and potentially harmful. METHODS: We conducted a case-control study using prospectively-collected electronic health records from primary care individually-linked to cancer registrations. Our cases were comprised of 1,139 patients, aged 15-99 years, diagnosed with pancreatic cancer between January 1, 2005 and June 30, 2009. Each case was age-, sex- and diagnosis time-matched to four non-pancreatic (cancer patient) controls. Disease and prescription codes for the 24 months prior to diagnosis were used to identify 57 individual symptoms. Using a machine learning approach, we trained a logistic regression model on 75% of the data to predict patients who later developed PC and tested the model's performance on the remaining 25%. RESULTS: We were able to identify 41.3% of patients < = 60 years at 'high risk' of developing pancreatic cancer up to 20 months prior to diagnosis with 72.5% sensitivity, 59% specificity and, 66% AUC. 43.2% of patients >60 years were similarly identified at 17 months, with 65% sensitivity, 57% specificity and, 61% AUC. We estimate that combining our algorithm with currently available biomarker tests could result in 30 older and 400 younger patients per cancer being identified as 'potential patients', and the earlier diagnosis of around 60% of tumours. CONCLUSION: After further work this approach could be applied in the primary care setting and has the potential to be used alongside a non-invasive biomarker test to increase earlier diagnosis. This would result in a greater number of patients surviving this devastating disease.

Zero-Shot Super-Resolution With a Physically-Motivated Downsampling Kernel for Endomicroscopy.

Super-resolution (SR) methods have seen significant advances thanks to the development of convolutional neural networks (CNNs). CNNs have been successfully employed to improve the quality of endomicroscopy imaging. Yet, the inherent limitation of research on SR in endomicroscopy remains the lack of ground truth high-resolution (HR) images, commonly used for both supervised training and reference-based image quality assessment (IQA). Therefore, alternative methods, such as unsupervised SR are being explored. To address the need for non-reference image quality improvement, we designed a novel zero-shot super-resolution (ZSSR) approach that relies only on the endomicroscopy data to be processed in a self-supervised manner without the need for ground-truth HR images. We tailored the proposed pipeline to the idiosyncrasies of endomicroscopy by introducing both: a physically-motivated Voronoi downscaling kernel accounting for the endomicroscope's irregular fibre-based sampling pattern, and realistic noise patterns. We also took advantage of video sequences to exploit a sequence of images for self-supervised zero-shot image quality improvement. We run ablation studies to assess our contribution in regards to the downscaling kernel and noise simulation. We validate our methodology on both synthetic and original data. Synthetic experiments were assessed with reference-based IQA, while our results for original images were evaluated in a user study conducted with both expert and non-expert observers. The results demonstrated superior performance in image quality of ZSSR reconstructions in comparison to the baseline method. The ZSSR is also competitive when compared to supervised single-image SR, especially being the preferred reconstruction technique by experts.

EUS-guided verteporfin photodynamic therapy for pancreatic cancer.

BACKGROUND AND AIMS: Locally advanced pancreatic cancer (LAPC) often causes obstruction. Verteporfin photodynamic therapy (PDT) can feasibly "debulk" the tumor more safely than noncurative surgery and has multiple advantages over older PDT agents. We aimed to assess the feasibility of EUS-guided verteporfin PDT in ablating nonresectable LAPC. METHODS: Adults with LAPC with adequate biliary drainage were prospectively enrolled. Exclusion criteria were significant metastatic disease burden, disease involving >50% duodenal or major artery circumference, and recent treatment with curative intent. CT was obtained between days -28 to 0. On day 0, verteporfin .4 mg/kg was infused 60 to 90 minutes before EUS, during which a diffuser was positioned in the tumor and delivered light at 50 J/cm for 333 seconds. CT was obtained on day 2, with adverse event monitoring occurring on days 1, 2, and 14. The primary outcome was presence of necrosis. RESULTS: Of 8 patients (62.5% men, mean age 65 ± 7.9 years) included in the study, 5 were staged at T3, 2 at T2, and 1 at T1. Most (n = 4) had primary lesions in the pancreatic head. Mean pretrial tumor diameter was 33.3 ± 13.4 mm. On day 2 CT, 5 lesions demonstrated a zone of necrosis measuring a mean diameter of 15.7 ± 5.5 mm; 3 cases did not develop necrosis. No adverse events were noted during the procedure or postprocedure observation period (days 1-3), and no changes in patient-reported outcomes were noted. CONCLUSIONS: In this pilot study, EUS-guided verteporfin PDT is feasible and shows promise as a minimally invasive ablative therapy for LAPC in select patients. Tumor necrosis is visible within 48 hours after treatment. Patient enrollment and data collection are ongoing. (Clinical trial registration number: NCT03033225.).

Utility of palliative EUS-guided biliary drainage using lumen-apposing metal stents: a prospective multicenter feasibility study (with video).

BACKGROUND AND AIMS: Biliary drainage with ERCP is successful in only 80% to 90% of cases of extrahepatic cholangiocarcinoma and pancreatic cancer. We present the results of a multicenter prospective study assessing the safety, feasibility, and quality of life of patients after EUS-guided biliary drainage (EUS-BD) with lumen-apposing metal stents after failed ERCP. METHODS: All consecutive adults with a dilated common bile duct (CBD) ≥14 mm secondary to inoperable malignant distal CBD stricture and failed ERCP biliary drainage were screened and recruited from 3 tertiary UK centers. Technical success of EUS-BD using lumen-apposing metal stents was the primary endpoint. Improvement in serum bilirubin level, 30-day mortality, procedure-related adverse events, and quality of life were secondary endpoints. Improvement in quality of life was measured using a validated questionnaire (EORTC QLQ-BIL21). RESULTS: Twenty patients were included in the analysis. EUS-BD was technically successful in all patients and the clinical success rate was 95% (19 of 20) at day 7 (>50% reduction in bilirubin level) and 92.3% (12 of 13) at day 30 (bilirubin <50 µmol/L). There were significant improvements in overall quality of life score (49 vs 42, P = .03) at day 30. All-cause 30-day mortality was 20% and the moderate adverse event rate was 10% (1 cholangitis and 1 stent migration). CONCLUSION: EUS-BD has acceptable technical success and safety as a second-line palliative treatment for inoperable malignant distal CBD strictures. Randomized controlled studies comparing EUS-BD with percutaneous transhepatic biliary drainage are needed to determine their effectiveness in clinical practice. (ISCRTN registration number: ISRCTN13196704.).