Early detection of pancreatic cancer

Inter-reader agreement of pancreatic adenocarcinoma resectability assessment with photon counting versus energy integrating detector CT

PURPOSE

To compare the inter-reader agreement of pancreatic adenocarcinoma resectability assessment at pancreatic protocol photon-counting CT (PCCT) with conventional energy-integrating detector CT (EID-CT).

METHODS

A retrospective single institution database search identified all contrast-enhanced pancreatic mass protocol abdominal CT performed at an outpatient facility with both a PCCT and EID-CT from 4/11/2022 to 10/30/2022. Patients without pancreatic adenocarcinoma were excluded. Four fellowship-trained abdominal radiologists, blinded to CT type, independently assessed vascular tumor involvement (uninvolved, abuts ≤ 180°, encases > 180°; celiac, superior mesenteric artery (SMA), common hepatic artery (CHA), superior mesenteric vein (SMV), main portal vein), the presence/absence of metastases, overall tumor resectability (resectable, borderline resectable, locally advanced, metastatic), and diagnostic confidence. Fleiss's kappa was used to calculate inter-reader agreement. CTDIvol was recorded. Radiation dose metrics were compared with a two-sample t-test. A p < .05 indicated statistical significance.

RESULTS

145 patients (71 men, mean[SD] age: 66[9] years) were included. There was substantial inter-reader agreement, for celiac artery, SMA, and SMV involvement at PCCT (kappa = 0.61-0.69) versus moderate agreement at EID-CT (kappa = 0.56-0.59). CHA had substantial inter-reader agreement at both PCCT (kappa = 0.67) and EIDCT (kappa = 0.70). For metastasis identification, radiologists had substantial inter-reader agreement at PCCT (kappa = 0.78) versus moderate agreement at EID-CT (kappa = 0.56). CTDIvol for PCCT and EID-CT were 16.9[7.4]mGy and 29.8[26.6]mGy, respectively (p < .001).

CONCLUSION

There was substantial inter-reader agreement for involvement of 4/5 major peripancreatic vessels (celiac artery, SMA, CHA, and SMV) at PCCT compared with 2/5 for EID-CT. PCCT also afforded substantial inter-reader agreement for metastasis detection versus moderate agreement at EID-CT with statistically significant radiation dose reduction.

Targeting Pancreatic Cancer with Novel Nicolaioidesin C Derivatives: Molecular Mechanism, In Vitro, and In Vivo Evaluations

Pancreatic cancer, one of the deadliest cancers with the lowest 5-year survival rate, often develops resistance to gemcitabine-based chemotherapies. The hypovascular nature of pancreatic tumors forces cancer cells to adapt to nutrient-depleted tumor microenvironments. Conventional anticancer agents targeting rapidly dividing cancer cells under nutrient-rich conditions are largely ineffective against adapted pancreatic cancer cells. Thus, targeting cancer cells under nutrient starvation, termed the "antiausterity strategy", may be effective for pancreatic cancer. This study examined nicolaioidesin C (Nic-C) derivatives as antiausterity agents. Among the 32 derivatives, Nic-15 (4n) exhibited superior cytotoxicity against MIA PaCa-2 and PANC-1 pancreatic cancer cells, inhibited MIA PaCa-2 cell migration and colony formation, and modulated the PI3K/Akt/mTOR pathway, while reducing the ER stress markers induced by gemcitabine. Nic-15 was found to inhibit tumor growth and enhance the efficacy of gemcitabine in an in vivo xenograft model. Nic-15 in combination with gemcitabine may be an effective strategy for the treatment of pancreatic cancer.

Economic evaluations of pancreatic cancer screening: a systematic review protocol

INTRODUCTION

The early detection of pancreatic cancer is an important step in reducing mortality by offering patients curative treatment. Screening strategies in risk populations and by means of different detection methods have been economically evaluated. However, a synthesis of screening studies to inform resource allocation towards early detection within the disease area has not been done. Therefore, studies evaluating the cost-effectiveness and costs of screening for pancreatic cancer should be systematically reviewed.

METHODS AND ANALYSIS

A systematic review of economic evaluations reporting the cost-effectiveness or costs of pancreatic cancer screening will be conducted. The electronic databases Medline, Web of Science and EconLit will be searched without geographical or time restrictions. Two independent reviewers will select eligible studies based on predefined criteria. The study quality will be assessed using the Consolidated Health Economic Evaluation Reporting Standards statement and the Bias in Economic Evaluation checklist. One reviewer will extract relevant data and a second reviewer will cross-check compliance with the extraction sheet. Key items will include characteristics of screened individuals, the screening strategies used, and costs, health effects and cost-effectiveness as study outputs. Differences of opinion between the reviewers will be solved by consulting a third reviewer.

ETHICS AND DISSEMINATION

Ethics approval is not required for this study since no original data will be collected. The results will be disseminated through presentations at conferences and publication in a peer-reviewed journal. The results of the systematic review will inform future economic evaluations of pancreatic screening, which provide guidance for decision-making in healthcare resource prioritisation.

PROSPERO REGISTRATION NUMBER

CRD42023475348.

Five-year recurrence/survival after pancreatoduodenectomy for pancreatic adenocarcinoma: does pre-existing diabetes matter? Results from the Recurrence After Whipple's (RAW) study

BACKGROUND

Diabetes mellitus (DM) has a complex relationship with pancreatic cancer. This study examines the impact of preoperative DM, both recent-onset and pre-existing, on long-term outcomes following pancreatoduodenectomy (PD) for pancreatic ductal adenocarcinoma (PDAC).

METHODS

Data were extracted from the Recurrence After Whipple's (RAW) study, a multi-centre cohort of PD for pancreatic head malignancy (2012-2015). Recurrence and five-year survival rates of patients with DM were compared to those without, and subgroup analysis performed to compare patients with recent-onset DM (less than one year) to patients with established DM.

RESULTS

Out of 758 patients included, 187 (24.7%) had DM, of whom, 47 of the 187 (25.1%) had recent-onset DM. There was no difference in the rate of postoperative pancreatic fistula (DM: 5.9% vs no DM 9.8%; p = 0.11), five-year survival (DM: 24.1% vs no DM: 22.9%; p = 0.77) or five-year recurrence (DM: 71.7% vs no DM: 67.4%; p = 0.32). There was also no difference between patients with recent-onset DM and patients with established DM in postoperative outcomes, recurrence, or survival.

CONCLUSION

We found no difference in five-year recurrence and survival between diabetic patients and those without diabetes. Patients with pre-existing DM should be evaluated for PD on a comparable basis to non-diabetic patients.

Differential miRNA and Protein Expression Reveals miR-1285, Its Targets TGM2 and CDH-1, as Well as CD166 and S100A13 as Potential New Biomarkers in Patients with Diabetes Mellitus and Pancreatic Adenocarcinoma

Early detection of PDAC remains challenging due to the lack of early symptoms and the absence of reliable biomarkers. The aim of the present project was to identify miRNA and proteomics signatures discriminating PDAC patients with DM from nondiabetic PDAC patients. Proteomics analysis and miRNA array were used for protein and miRNA screening. We used Western blotting and Real-Time Quantitative Reverse Transcription polymerase chain reaction (qRT-PCR) for protein and miRNA validation. Comparisons between experimental groups with normal distributions were performed using one-way ANOVA followed by Tukey's post hoc test, and pairwise tests were performed using t-tests. p ≤ 0.05 was considered statistically significant. Protein clusters of differentiation 166 (CD166), glycoprotein CD63 (CD63), S100 calcium-binding protein A13 (S100A13), and tumor necrosis factor-β (TNF-β) were detected in the proteomics screening. The miRNA assay revealed a differential miRNA 1285 regulation. Previously described target proteins of miR-1285 cadherin-1 (CDH-1), cellular Jun (c-Jun), p53, mothers against decapentaplegic homolog 4 (Smad4), human transglutaminase 2 (TGM2) and yes-associated protein (YAP), were validated via Western blotting. miR-1285-3p was successfully validated as differentially regulated in PDAC + DM via qRT-PCR. Overall, our data suggest miRNA1285-3p, TGM2, CDH-1, CD166, and S100A13 as potential meaningful biomarker candidates to characterize patients with PDAC + DM. Data are available via ProteomeXchange with the identifier PXD053169.

Artificial Intelligence in Pancreatic Image Analysis: A Review

Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing challenges due to ambiguous symptoms, high misdiagnosis rates, and significant financial costs. Artificial intelligence (AI) offers a promising solution by relieving medical personnel's workload, improving clinical decision-making, and reducing patient costs. This study focuses on AI applications such as segmentation, classification, object detection, and prognosis prediction across five types of medical imaging: CT, MRI, EUS, PET, and pathological images, as well as integrating these imaging modalities to boost diagnostic accuracy and treatment efficiency. In addition, this study discusses current hot topics and future directions aimed at overcoming the challenges in AI-enabled automated pancreatic cancer diagnosis algorithms.

Automatically Detecting Pancreatic Cysts in Autosomal Dominant Polycystic Kidney Disease on MRI Using Deep Learning

BACKGROUND

Pancreatic cysts in autosomal dominant polycystic kidney disease (ADPKD) correlate with PKD2 mutations, which have a different phenotype than PKD1 mutations. However, pancreatic cysts are commonly overlooked by radiologists. Here, we automate the detection of pancreatic cysts on abdominal MRI in ADPKD.

METHODS

Eight nnU-Net-based segmentation models with 2D or 3D configuration and various loss functions were trained on positive-only or positive-and-negative datasets, comprising axial and coronal T2-weighted MR images from 254 scans on 146 ADPKD patients with pancreatic cysts labeled independently by two radiologists. Model performance was evaluated on test subjects unseen in training, comprising 40 internal, 40 external, and 23 test-retest reproducibility ADPKD patients.

RESULTS

Two radiologists agreed on 52% of cysts labeled on training data, and 33%/25% on internal/external test datasets. The 2D model with a loss of combined dice similarity coefficient and cross-entropy trained with the dataset with both positive and negative cases produced an optimal dice score of 0.7 ± 0.5/0.8 ± 0.4 at the voxel level on internal/external validation and was thus used as the best-performing model. In the test-retest, the optimal model showed superior reproducibility (83% agreement between scan A and B) in segmenting pancreatic cysts compared to six expert observers (77% agreement). In the internal/external validation, the optimal model showed high specificity of 94%/100% but limited sensitivity of 20%/24%.

CONCLUSIONS

Labeling pancreatic cysts on T2 images of the abdomen in patients with ADPKD is challenging, deep learning can help the automated detection of pancreatic cysts, and further image quality improvement is warranted.

Down-Regulation of AKT Proteins Slows the Growth of Mutant-KRAS Pancreatic Tumors

Serine/threonine kinase AKT isoforms play a well-established role in cell metabolism and growth. Most pancreatic adenocarcinomas (PDACs) harbor activation mutations of KRAS, which activates the PI3K/AKT signaling pathway. However, AKT inhibitors are not effective in the treatment of pancreatic cancer. To better understand the role of AKT signaling in mutant-KRAS pancreatic tumors, this study utilized proteolysis-targeting chimeras (PROTACs) and CRISPR-Cas9-genome editing to investigate AKT proteins. The PROTAC down-regulation of AKT proteins markedly slowed the growth of three pancreatic tumor cell lines harboring mutant KRAS. In contrast, the inhibition of AKT kinase activity alone had very little effect on the growth of these cell lines. The concurrent genetic deletion of all AKT isoforms (AKT1, AKT2, and AKT3) in the KPC (KrasG12D; Trp53R172H; Pdx1-Cre) pancreatic cancer cell line also dramatically slowed its growth in vitro and when orthotopically implanted in syngeneic mice. Surprisingly, insulin-like growth factor-1 (IGF-1), but not epidermal growth factor (EGF), restored KPC cell growth in serum-deprived conditions, and the IGF-1 growth stimulation effect was AKT-dependent. The RNA-seq analysis of AKT1/2/3-deficient KPC cells suggested that reduced cholesterol synthesis may be responsible for the decreased response to IGF-1 stimulation. These results indicate that the presence of all three AKT isoforms supports pancreatic tumor cell growth, and the pharmacological degradation of AKT proteins may be more effective than AKT catalytic inhibitors for treating pancreatic cancer.

Early detection of pancreatic cancer by liquid biopsy "PANLIPSY": a french nation-wide study project

BACKGROUND

Pancreatic cancer, predominantly characterized by ductal adenocarcinoma (PDAC) accounts for 90% of cases and is the fourth leading cause of cancer-related deaths globally. Its incidence is notably increasing. This poor prognosis is primarily due to late-stage diagnosis (approximately 70% to 80% of patients are diagnosed at an advanced stage), aggressive tumor biology, and low sensitivity to chemotherapy. Consequently, it is crucial to identify and develop a simple, feasible and reproducible blood-based signature (i.e., combination of biomarkers) for early detection of PDAC.

METHODS

The PANLIPSY study is a multi-center, non-interventional prospective clinical trial designed to achieve early detection of PDAC with high specificity and sensitivity, using a combinatorial approach in blood samples. These samples are collected from patients with resectable, borderline or locally advanced, and metastatic stage PDAC within the framework of the French Biological and Clinical Database for PDAC cohort (BACAP 2). All partners of the BACAP consortium are eligible to participate. The study will include 215 PDAC patients, plus 25 patients with benign pancreatic conditions from the PAncreatic Disease Cohort of TOuLouse (PACTOL) cohort, and 115 healthy controls, totaling 355 individuals. Circulating biomarkers will be collected in a total volume of 50 mL of blood, divided into one CellSave tube (10 mL), two CELL-FREE DNA BCT® preservative tubes (18 mL), and five EDTA tubes (22 mL in total). Samples preparation will adhere to the guidelines of the European Liquid Biopsy Society (ELBS). A unique feature of the study is the AI-based comparison of these complementary liquid biopsy biomarkers. Main end-points: i) to define a liquid biopsy signature that includes the most relevant circulating biomarkers, ii) to validate the multi-marker panel in an independent cohort of healthy controls and patients, with resectable PDAC, and iii) to establish a unique liquid biopsy biobank for PDAC study.

DISCUSSION

The PANLIPSY study is a unique prospective non-interventional clinical trial that brings together liquid biopsy experts. The aim is to develop a biological signature for the early detection of PDAC based on AI-assisted detection of circulating biomarkers in blood samples (CTCs, ctDNA, EVs, circulating immune system, circulating cell-free nucleosomes, proteins, and microbiota).

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT06128343 / NCT05824403. Registration dates: June 8,2023 and April 21, 2023.

Passion fruit (Passiflora edulis Sims) by-products as a source of bioactive compounds for non-communicable disease prevention: extraction methods and mechanisms of action: a systematic review

Introduction

The review titled Passion fruit by-products as a source of bioactive compounds for non-communicable disease prevention: extraction methods and mechanisms provide valuable insights into the health benefits and industrial applications of passion fruit waste. Passion fruits are a tropical and subtropical vine species, which produces edible fruits. Many food product types can be made from passion fruits. However, during passion fruit processing, large amounts of waste are released in to the environment. This review focuses on extraction methods of bioactive compounds from passion fruit by-products such as leaves, peels, seeds, and bagasse.

Methods

This comprehensive review focuses on the bioactive compounds present in passion fruit by-products, emphasis on their mechanisms of action on non-communicable diseases. It also provides a detailed analysis of the extraction methods used to obtain these bioactive compounds, their potential industrial applications, and the factors that affect extraction efficiency.

Results

This review encourages further research and innovation in utilization of passion fruit waste as a source of bioactive compounds for non- communicable disease prevention and their mechanisms of action. This can advance the circular economy. It also highlights the importance of sustainable and green extraction methods, which have gained attention due to environmental concerns.

Discussion

Unlike previous reviews, this comprehensive article explores the potential health benefits of multiple passion fruit waste products. It also examines the possible applications of these extracts for industrial goods such as food additives, colorants, nutraceuticals, natural antioxidants, and antimicrobial agents. Overall, it contributes new information emphasizing the potential of passion fruit by-products as a source of bioactive, and the findings have implications for the scientific community and industry, promoting a deeper understanding of the health benefits and sustainable practices associated with passion fruit waste utilization.

Identification of pancreatic cancer risk factors from clinical notes using natural language processing

OBJECTIVES

Screening for pancreatic ductal adenocarcinoma (PDAC) is considered in high-risk individuals (HRIs) with established PDAC risk factors, such as family history and germline mutations in PDAC susceptibility genes. Accurate assessment of risk factor status is provider knowledge-dependent and requires extensive manual chart review by experts. Natural Language Processing (NLP) has shown promise in automated data extraction from the electronic health record (EHR). We aimed to use NLP for automated extraction of PDAC risk factors from unstructured clinical notes in the EHR.

METHODS

We first developed rule-based NLP algorithms to extract PDAC risk factors at the document-level, using an annotated corpus of 2091 clinical notes. Next, we further improved the NLP algorithms using a cohort of 1138 patients through patient-level training, validation, and testing, with comparison against a pre-specified reference standard. To minimize false-negative results we prioritized algorithm recall.

RESULTS

In the test set (n = 807), the NLP algorithms achieved a recall of 0.933, precision of 0.790, and F1-score of 0.856 for family history of PDAC. For germline genetic mutations, the algorithm had a high recall of 0.851, while precision and F1-score were lower at 0.350 and 0.496 respectively. Most false positives for germline mutations resulted from erroneous recognition of tissue mutations.

CONCLUSIONS

Rule-based NLP algorithms applied to unstructured clinical notes are highly sensitive for automated identification of PDAC risk factors. Further validation in a large primary-care patient population is warranted to assess real-world utility in identifying HRIs for pancreatic cancer screening.

Early detection of pancreatic cancer in the era of precision medicine

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related mortality and it is often diagnosed at advanced stages due to non-specific clinical presentation. Disease detection at localized disease stage followed by surgical resection remains the only potentially curative treatment. In this era of precision medicine, a multifaceted approach to early detection of PDAC includes targeted screening in high-risk populations, serum biomarkers and "liquid biopsies", and artificial intelligence augmented tumor detection from radiologic examinations. In this review, we will review these emerging techniques in the early detection of PDAC.

Early diagnosis of pancreatic cancer: Shedding light on an unresolved challenge

Diagnosing early-stage pancreatic cancer (PC) remains a clinical challenge. Hence, studying novel imaging aspects that could enhance the diagnostic accuracy of malignant pancreatic precursor lesions is imperative. This article aims to underscore the promising role of emerging imaging aspects that may facilitate the earlier diagnosis of PC, thereby improving its management and prognosis.

Down-regulation of AKT proteins slows the growth of mutant-KRAS pancreatic tumors

Serine/threonine kinase AKT isoforms play a well-established role in cell metabolism and growth. Most pancreatic adenocarcinoma (PDAC) harbors activation mutations of KRAS, which activates the PI3K/AKT signaling pathway. However, AKT inhibitors are not effective in the treatment of pancreatic cancer. To better understand the role of AKT signaling in mutant-KRAS pancreatic tumors, this study utilizes proteolysis-targeting chimeras (PROTACs) and CRISPR-Cas9-genome editing to investigate AKT proteins. PROTAC down-regulation of AKT proteins markedly slowed the growth of three pancreatic tumor cell lines harboring mutant KRAS. In contrast, inhibition of AKT kinase activity alone had very little effect on the growth of these cell lines. Concurrent genetic deletion of all AKT isoforms (AKT1, AKT2, and AKT3) in the KPC (KrasG12D; Trp53R172H; Pdx1-Cre) pancreatic cancer cell line also dramatically slowed its growth in vitro and when orthotopically implanted in syngeneic mice. Surprisingly, insulin-like growth factor-1 (IGF-1), but not epidermal growth factor (EGF), restored KPC cell growth in serum-deprived conditions and the IGF-1 growth stimulation effect was AKT dependent. RNA-seq analysis of AKT1/2/3-deficient KPC cells suggested that reduced cholesterol synthesis may be responsible for the decreased response to IGF-1 stimulation. These results indicate that the presence of all three AKT isoforms supports pancreatic tumor cell growth and pharmacological degradation of AKT proteins may be more effective than AKT catalytic inhibitors for treating pancreatic cancer.

A causality-inspired generalized model for automated pancreatic cancer diagnosis

Pancreatic cancer (PC) is a severely malignant cancer variant with high mortality. Since PC has no obvious symptoms, most PC patients are belatedly diagnosed at advanced disease stages. Recently, artificial intelligence (AI) approaches have demonstrated promising prospects for early diagnosis of pancreatic cancer. However, certain non-causal factors (such as intensity and texture appearance variations, also called confounders) tend to induce spurious correlation with PC diagnosis. This undermines the generalization performance and the clinical applicability of the AI-based PC diagnosis approaches. Therefore, we propose a causal intervention based automated method for pancreatic cancer diagnosis with contrast-enhanced computerized tomography (CT) images, where a confounding effects reduction scheme is developed for alleviating spurious correlations to achieve unbiased learning, thereby improving the generalization performance. Specifically, a continuous image generation strategy was developed to simulate wide variations of intensity differences caused by imaging heterogeneities, where Monte Carlo sampling is added to further enhance the continuity of simulated images. Then, to enhance the pancreatic texture variability, a texture diversification method was introduced in conjunction with gradient-based data augmentation. Finally, a causal intervention strategy was proposed to alleviate the adverse confounding effects by decoupling the causal and non-causal factors and combining them randomly. Extensive experiments showed remarkable diagnosis performance on a cross-validation dataset. Also, promising generalization performance with an average accuracy of 0.87 was attained on three independent test sets of a total of 782 subjects. Therefore, the proposed method shows high clinical feasibility and applicability for pancreatic cancer diagnosis.

Differences in Plasma Fatty Acid Composition Related to Chronic Pancreatitis: A Pilot Study

OBJECTIVES

Chronic pancreatitis (CP) is an inflammatory disease affecting the absorption of fat-soluble nutrients. Signaling in pancreatic cells that lead to inflammation may be influenced by fatty acids (FAs) through diet and de novo lipogenesis. Here, we investigated the relationship between plasma FA composition in CP with heterogeneity of etiology and complications of CP.

MATERIALS AND METHODS

Blood and clinical parameters were collected from subjects with CP (n = 47) and controls (n = 22). Plasma was analyzed for FA composition using gas chromatography and compared between controls and CP and within CP.

RESULTS

Palmitic acid increased, and linoleic acid decreased in CP compared with controls. Correlations between age or body mass index and FAs are altered in CP compared with controls. Diabetes, pancreatic calcifications, and substance usage, but not exocrine pancreatic dysfunction, were associated with differences in oleic acid and linoleic acid relative abundance in CP. De novo lipogenesis index was increased in the plasma of subjects with CP compared with controls and in calcific CP compared with noncalcific CP.

CONCLUSIONS

Fatty acids that are markers of de novo lipogenesis and linoleic acid are dysregulated in CP depending on the etiology or complication. These results enhance our understanding of CP and highlight potential pathways targeting FAs for treating CP.

Early Diagnosis and Prognosis Prediction of Pancreatic Cancer Using Engineered Hybrid Core-Shells in Laser Desorption/Ionization Mass Spectrometry

Effective detection of bio-molecules relies on the precise design and preparation of materials, particularly in laser desorption/ionization mass spectrometry (LDI-MS). Despite significant advancements in substrate materials, the performance of single-structured substrates remains suboptimal for LDI-MS analysis of complex systems. Herein, designer Au@SiO2@ZrO2 core-shell substrates are developed for LDI-MS-based early diagnosis and prognosis of pancreatic cancer (PC). Through controlling Au core size and ZrO2 shell crystallization, signal amplification of metabolites up to 3 orders is not only achieved, but also the synergistic mechanism of the LDI process is revealed. The optimized Au@SiO2@ZrO2 enables a direct record of serum metabolic fingerprints (SMFs) by LDI-MS. Subsequently, SMFs are employed to distinguish early PC (stage I/II) from controls, with an accuracy of 92%. Moreover, a prognostic prediction scoring system is established with enhanced efficacy in predicting PC survival compared to CA19-9 (p < 0.05). This work contributes to material-based cancer diagnosis and prognosis.

Efficacy and safety of first-line chemotherapies for patients with advanced pancreatic ductal adenocarcinoma: A systematic review and network meta-analysis

Background

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease, often diagnosed at an advanced stage. Systemic chemotherapy is the primary treatment, but direct comparisons of different regimens are limited. This study conducted a systematic review and network meta-analysis (NMA) to compare the efficacy and safety of various chemotherapy regimens, with the unique advantage of only including Phase III randomized controlled trials (RCTs).

Methods

NMA was conducted regarding the searched phase III RCTs by comparing overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and adverse events (AEs) of different chemotherapy protocols.

Results

The analysis included 24 studies with 11470 patients across 25 treatment modalities. Among the chemotherapy regimens evaluated, FOLFIRINOX (fluorouracil, leucovorin, irinotecan, and oxaliplatin) demonstrated the highest OS and PFS, with a risk ratio (logHR) of 4.5 (95 % confidence interval 4.32-4.68) compared to gemcitabine monotherapy. The PEFG regimen (cisplatin, epirubicin, 5-fluorouracil, and gemcitabine) exhibited the highest ORR, with an odds ratio (OR) of 6.67 (2.08-20) compared to gemcitabine monotherapy. Notably, gemcitabine plus sorafenib was associated with the lowest hematological toxicity, with an odds ratio (OR) of 0.1 (0.02-0.48).

Conclusion

Combination therapies may offer greater benefits but also cause more toxic effects. However, combinations with targeted agents seem to have fewer adverse reactions.

Impact of PET/CT Imaging with FDG in Locally Advanced Cervical Carcinoma-A Literature Review

Positron emission tomography (PET) and computed tomography (CT) have evolved as a pivotal diagnostic modality in the field of oncology. With its increasing application in staging and ready availability, it becomes imperative for committed radiation oncologists to possess a complete analysis and understanding of integration of molecular imaging, which can be helpful for radiation planning, while also acknowledging its possible limitations and challenges. A significant obstacle lies in the synthesis and design of tumor-specific bmolecules for diagnosing and treating cancer. The utilization of radiation in medical biochemistry and biotechnology, encompassing diagnosis, therapy, and control of biological systems, is encapsulated under the umbrella term "nuclear medicine". Notably, the application of various radioisotopes in pharmaceutics has garnered significant attention, particularly in the realm of delivery systems for drugs, DNA, and imaging agents. The present article provides a comprehensive review of use of novel techniques PET and CT with major positron-emitting radiopharmaceuticals currently in progress or utilized in clinical practice with their integration into imaging and radiation therapy.

Comprehensive Genomic Studies on the Cell Blocks of Pancreatic Cancer

Pancreatic cancer is one of the deadliest malignancies, characterized by late-stage diagnosis and limited treatment options. Comprehensive genomic profiling plays an important role in understanding the molecular mechanisms underlying the disease and identifying potential therapeutic targets. Cell blocks (CBs), derived from EUS-FNA, have become valuable resources for diagnosis and genomic analysis. We examine the molecular profile of pancreatic ductal adenocarcinoma (PDAC) using specimens obtained from CB EUS-FNA, across a large gene panel, within the framework of next-generation sequencing (NGS). Our findings revealed that over half (55%) of PDAC CB cases provided adequate nucleic acid for next-generation sequencing, with tumor cell percentages averaging above 30%. Despite challenges such as low DNA quantification and degraded DNA, sequencing reads showed satisfactory quality control statistics, demonstrating the detection of genomic alterations. Most cases (84.6%) harbored at least one gene variant, including clinically significant gene mutation variants such as KRAS, TP53, and CDKN2A. Even at minimal concentrations, as long as the extracted DNA is of high quality, performing comprehensive molecular profiling on PDAC samples from cell blocks has remained feasible. This strategy has yielded valuable information about the diagnosis, genetic landscape, and potential therapeutic targets, aligning closely with a precision cytopathology approach.

Comprehensive analysis and prognostic assessment of senescence-associated genes in bladder cancer

BACKGROUND

The prevalence and mortality of bladder cancer (BLCA) present a significant medical challenge. While the function of senescence-related genes in tumor development is recognized, their prognostic significance in BLCA has not been thoroughly explored.

METHODS

BLCA transcriptome datasets were sourced from the TCGA and GEO repositories. Gene groupings were determined through differential gene expression and non-negative matrix factorization (NMF) methodologies. Key senescence-linked genes were isolated using singular and multivariate Cox regression analyses, combined with lasso regression. Validation was undertaken with GEO database information. Predictive models, or nomograms, were developed by merging risk metrics with clinical records, and their efficacy was gauged using ROC curve methodologies. The immune response's dependency on the risk metric was assessed through the immune phenomenon score (IPS). Additionally, we estimated IC50 metrics for potential chemotherapeutic agents.

RESULTS

Reviewing 406 neoplastic and 19 standard tissue specimens from the TCGA repository facilitated the bifurcation of subjects into two unique clusters (C1 and C2) according to senescence-related gene expression. After a stringent statistical evaluation, a set of ten pivotal genes was discerned and applied for risk stratification. Validity tests for the devised nomograms in forecasting 1, 3, and 5-year survival probabilities for BLCA patients were executed via ROC and calibration plots. IC50 estimations highlighted a heightened responsiveness in the low-risk category to agents like cisplatin, cyclopamine, and sorafenib.

CONCLUSIONS

In summation, our research emphasizes the prospective utility of risk assessments rooted in senescence-related gene signatures for enhancing BLCA clinical oversight.

Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.

MicroRNA-20a in extracellular vesicles derived from duodenal fluid is a possible biomarker for pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate owing to its late diagnosis and aggression. In addition, there are relatively few minimally invasive screening methods for the early detection of PDAC, making the identification of biomarkers for this disease a critical priority. Recent studies have reported that microRNAs in extracellular vesicles (EV-miRs) from bodily fluids can be useful for the diagnosis of PDACs. Given this, we designed this study to evaluate the utility of cancer EVs extracted from duodenal fluid (DF) and their resident EV-miRs as potential biomarkers for the detection of PDAC.

Methods

EV-miRs were evaluated and identified in the supernatants of various pancreatic cancer cell lines (Panc-1, SUIT2, and MIAPaca2), human pancreatic duct epithelial cells, and the DF from patients with PDAC and healthy controls. EVs were extracted using ultracentrifugation and the relative expression of EV-miR-20a was quantified.

Results

We collected a total of 34 DF samples (27 PDAC patients and seven controls) for evaluation and our data suggest that the relative expression levels of EV-miR-20a were significantly higher in patients with PDAC than in controls (p = 0.0025). In addition, EV-miR-20a expression could discriminate PDAC from control patients regardless of the location of the tumor with an area under the curve values of 0.88 and 0.88, respectively.

Conclusions

We confirmed the presence of EVs in the DF and suggest that the expression of EV-miR-20a in these samples may act as a potential diagnostic biomarker for PDAC.

Investigating the early diagnosis of pancreatic cancer by surveillance of patients with "K-sign" of the pancreas by computed tomography: A prospective study

BACKGROUND

We previously conducted a retrospective study investigating pancreatic morphological abnormalities that lead to early diagnosis of pancreatic cancer (PC) using computed tomography (CT). We reviewed 41 of 308 PC patients between 2011 and 2017 who had previously undergone CT to look for morphological changes leading to cancer development. In 24 patients (58.5%), a K-shaped constriction of the pancreas ("K-sign") was observed before the appearance of cancer. This study aimed to investigate whether an early PC diagnosis is possible by prospective CT follow-up of patients with the K-sign.

METHODS

We investigated PC development through prospective surveillance of patients exhibiting K-signs identified on CT.

RESULTS

Of approximately 87 000 CT scans performed between April 2019 and August 2022, the K-sign was observed in 54 patients. A total of 30 patients provided informed consent and were subsequently monitored using CT. Five patients (16.7%) were diagnosed with PC and underwent surgery after 3-24 months follow-up. Pathologically, four of five patients (80%) were diagnosed with early-stage pancreatic cancer (stage 0-IA). All patients exhibited defects in acinar structure, fibrous tissue, fat replacement, and inflammatory cells, suggesting their potential involvement in PC development.

CONCLUSION

The detection and surveillance of the K-sign may be helpful for early PC diagnosis.

Advancements in Pancreatic Cancer Detection: Integrating Biomarkers, Imaging Technologies, and Machine Learning for Early Diagnosis

Artificial intelligence (AI) has come to play a pivotal role in revolutionizing medical practices, particularly in the field of pancreatic cancer detection and management. As a leading cause of cancer-related deaths, pancreatic cancer warrants innovative approaches due to its typically advanced stage at diagnosis and dismal survival rates. Present detection methods, constrained by limitations in accuracy and efficiency, underscore the necessity for novel solutions. AI-driven methodologies present promising avenues for enhancing early detection and prognosis forecasting. Through the analysis of imaging data, biomarker profiles, and clinical information, AI algorithms excel in discerning subtle abnormalities indicative of pancreatic cancer with remarkable precision. Moreover, machine learning (ML) algorithms facilitate the amalgamation of diverse data sources to optimize patient care. However, despite its huge potential, the implementation of AI in pancreatic cancer detection faces various challenges. Issues such as the scarcity of comprehensive datasets, biases in algorithm development, and concerns regarding data privacy and security necessitate thorough scrutiny. While AI offers immense promise in transforming pancreatic cancer detection and management, ongoing research and collaborative efforts are indispensable in overcoming technical hurdles and ethical dilemmas. This review delves into the evolution of AI, its application in pancreatic cancer detection, and the challenges and ethical considerations inherent in its integration.

[Early detection of sporadic pancreatic cancer]

The incidence of pancreatic cancer is rising. At present, pancreatic cancer is the third most common cancer-causing death in Germany, but it is expected to become the second in 2030 and finally the leading cause of cancer death in 2050. Pancreatic ductal adenocarcinoma (PC) is generally diagnosed at advanced stages, and 5-year-survival has remained poor. Early detection of sporadic PC at stage IA, however, can yield a 5-year-survival rate of about 80%. Early detection initiatives aim at identifying persons at high risk. People with new-onset diabetes at age 50 or older have attracted much interest. Novel strategies regarding how to detect sporadic PC at an early stage are being discussed.

Prognostic Value of Circulating Cell-Free DNA Concentration and Neutrophil-to-Lymphocyte Ratio in Patients with Pancreatic Ductal Adenocarcinoma: A Prospective Cohort Study

Circulating cell-free DNA (ccfDNA) quantity correlates with the clinical characteristics and prognosis of various cancer types. We investigated whether ccfDNA levels and the neutrophil-to-lymphocyte ratio (NLR) have prognostic value in patients with pancreatic ductal adenocarcinoma (PDAC). Peripheral blood was collected from 82 patients with PDAC prior to any diagnostic procedure or the administration of chemotherapy. Plasma DNA was isolated, and ccfDNA concentration and NLR were determined. We found that ccfDNA levels were correlated with age and tumor burden. Moreover, higher values of NLR (≥3.31) were linked with worse overall survival (OS) (4 vs. 10 months; log rank p = 0.011), and an elevated ccfDNA concentration (≥25.79 ng/mL) was strongly associated with shorter OS (4 vs. 8 months; log rank p = 0.009). According to the results of the multivariable Cox regression analysis, the baseline concentration of ccfDNA was an independent prognostic factor for OS (HR 0.45, 95% CI 0.21-0.97, p = 0.041). Furthermore, the combination of ccfDNA levels with NLR greatly enhanced the prognostic accuracy of PDAC patients. Our study demonstrates that ccfDNA concentration and NLR are independent predictors of survival in PDAC. Subsequent studies should validate this combination as a prognostic indicator in PDAC patients and assess its utility for guiding therapeutic decisions.

Correlation between main pancreatic duct diameter measurements: Special pancreatic ultrasonography versus magnetic resonance cholangiopancreatography

Main pancreatic duct (MPD) dilatation is reported to be a risk factor for pancreatic cancer (PC). Although magnetic resonance cholangiopancreatography (MRCP) and ultrasonographic modalities are valuable for monitoring the pancreas, there is limited information on the efficacy of different imaging modalities in measuring MPD diameter. To improve pancreatic imaging, we developed a specialized ultrasound approach focusing on the pancreas (special pancreatic US). We aimed to examine the correlation between MPD diameter measurements using special pancreatic US versus MRCP. We retrospectively reviewed the clinical data of patients with MPD dilation (≥2.5 mm) via special pancreatic US used for screening at our institution between January 2020 and October 2022 and included patients who underwent magnetic resonance imaging 2 months before and after pancreatic US. The MPD diameter on MRCP was measured at the pancreatic locus, where the maximum MPD diameter was obtained on special pancreatic US. This study included 96 patients, with a median interval of 8.5 days between the date of special pancreatic US and the date of undergoing MRCP. MPD dilatation and/or pancreatic cysts were diagnosed in 86 patients, PC in 5 patients, and other diseases in 5 patients. The median MPD diameter, measured using special pancreatic US, was 3.4 mm (interquartile range: 2.9-4.9 mm), whereas it was 3.5 mm using MRCP (interquartile range: 2.8-4.5 mm). There were strong positive correlations between MPD diameter measured on special pancreatic US and that measured on MRCP (R = 0.925, P < .001). This study revealed strong positive correlations between the MPD diameter measurements using special pancreatic US and MRCP. MPD diameter measurements from each imaging method can be helpful during follow-up in individuals at a high risk of PC.

Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images

Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentation of the lesion, and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show a lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.

Endoscopic Ultrasound-Guided Fine-Needle Biopsy Versus Aspiration for Tissue Sampling Adequacy for Molecular Testing in Pancreatic Ductal Adenocarcinoma

BACKGROUND AND AIMS

There is limited literature on sample adequacy for molecular testing in pancreatic ductal adenocarcinoma obtained via endoscopic ultrasound (EUS) fine-needle aspiration (FNA) versus EUS fine-needle biopsy (FNB). We aimed to compare these two modalities regarding sample adequacy for molecular and genomic sequencing.

METHODS

We reviewed all patients with pancreatic ductal adenocarcinoma who underwent EUS at Saint Luke's Hospital from 2018 to 2021. The patients were categorized based on the method of EUS tissue acquisition, specifically FNA or FNB. A comprehensive evaluation was conducted for all cases by cytotechnologists.

RESULTS

Out of 132 patients who underwent EUS-guided biopsies, 76 opted for FNA, 48 opted for FNB, and 8 opted for a combination of both. The average number of passes required for FNB and FNA was 2.58 ± 1.06 and 2.49 ± 1.07, respectively (p = 0.704), indicating no significant difference. Interestingly, 71.4% (35) of FNB-obtained samples were deemed adequate for molecular testing, surpassing the 32.1% (26) adequacy observed with FNA (p < 0.001). Additionally, 46.4% (26) of FNB-obtained samples were considered adequate for genomic testing, a notable improvement over the 23.8% (20) adequacy observed with FNA (p = 0.005).

CONCLUSION

Although the number of passes required for cytologic diagnosis did not differ significantly between EUS-FNB and EUS-FNA, the former demonstrated superiority in obtaining samples adequate for molecular testing. Tumor surface area and cellularity were crucial parameters in determining sample adequacy for molecular testing, irrespective of the chosen tissue acquisition modality.

Clinical response of pancreatic cancer bearing a germline BRCA2 p.I3169M fs*48 variant for platinum-based drug and PARP inhibitor

Pancreatic cancer is a malignancy with a high mortality rate, accounting for 37 000 people annually in Japan. It is rarely diagnosed in a resectable state, and effective medicines for its advanced stage are scarce. Some pancreatic cancer is hereditary, and ~10% have germline mutations of Breast cancer 1/2 (BRCA1/2). BRCA1/2 are key molecules involved in homologous recombination to repair DNA double-strand break. Platinum-based drugs and poly Adenosine diphosphate ribose (ADP) ribose polymerase inhibitors that induce synthetic lethality would be theoretically effective in patients with loss-of-function mutations in BRCA1/2. Strictly speaking, some discrepancy between the pathogenicity of BRCA1/2 and their drug sensitivity might be expected. Hence, we report that platinum-based anticancer agents and poly ADP ribose polymerase inhibitors were effective against pancreatic cancer bearing BRCA2 p.I3169M fs*48.

The Effect of 4-(Dimethylamino)phenyl-5-oxopyrrolidines on Breast and Pancreatic Cancer Cell Colony Formation, Migration, and Growth of Tumor Spheroids

A series of hydrazones, azoles, and azines bearing a 4-dimethylaminophenyl-5-oxopyrrolidine scaffold was synthesized. Their cytotoxic effect against human pancreatic carcinoma Panc-1 and triple-negative breast cancer MDA-MB-231 cell lines was established by MTT assay. Pyrrolidinone derivatives 3c and 3d, with incorporated 5-chloro and 5-methylbenzimidazole fragments; hydrazone 5k bearing a 5-nitrothien-2-yl substitution; and hydrazone 5l with a naphth-1-yl fragment in the structure significantly decreased the viability of both cancer cell lines. Compounds 3c and 5k showed the highest selectivity, especially against the MDA-MB-231 cancer cell line. The EC50 values of the most active compound 5k against the MDA-MB231 cell line was 7.3 ± 0.4 μM, which were slightly higher against the Panc-1 cell line (10.2 ± 2.6 μM). Four selected pyrrolidone derivatives showed relatively high activity in a clonogenic assay. Compound 5k was the most active in both cell cultures, and it completely disturbed MDA-MB-231 cell colony growth at 1 and 2 μM and showed a strong effect on Panc-1 cell colony formation, especially at 2 μM. The compounds did not show an inhibitory effect on cell line migration by the 'wound-healing' assay. Compound 3d most efficiently inhibited the growth of Panc-1 spheroids and reduced cell viability in MDA-MB-231 spheroids. Considering these different activities in biological assays, the selected pyrrolidinone derivatives could be further tested to better understand the structure-activity relationship and their mechanism of action.

Modular Self-Assembling Dendrimer Nanosystems for Magnetic Resonance and Multimodality Imaging of Tumors

Bioimaging is a powerful tool for diagnosing tumors but remains limited in terms of sensitivity and specificity. Nanotechnology-based imaging probes able to accommodate abundant imaging units with different imaging modalities are particularly promising for overcoming these limitations. In addition, the nanosized imaging agents can specifically increase the contrast of tumors by exploiting the enhanced permeability and retention effect. A proof-of-concept study is performed on pancreatic cancer to demonstrate the use of modular amphiphilic dendrimer-based nanoprobes for magnetic resonance (MR) imaging (MRI) or MR/near-infrared fluorescence (NIRF) multimodality imaging. Specifically, the self-assembly of an amphiphilic dendrimer bearing multiple Gd3+ units at its terminals, generates a nanomicellar agent exhibiting favorable relaxivity for MRI with a good safety profile. MRI reveals an up to two-fold higher contrast enhancement in tumors than in normal muscle. Encapsulating the NIRF dye within the core of the nanoprobe yields an MR/NIRF bimodal imaging agent for tumor detection that is efficient both for MRI, at Gd3+ concentrations 1/10 the standard clinical dose, and for NIRF imaging, allowing over two-fold stronger fluorescence intensities. These self-assembling dendrimer nanosystems thus constitute effective probes for MRI and MR/NIRF multimodality imaging, offering a promising nanotechnology platform for elaborating multimodality imaging probes in biomedical applications.

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.

Optimal conspicuity of pancreatic ductal adenocarcinoma in virtual monochromatic imaging reconstructions on a photon-counting detector CT: comparison to conventional MDCT

PURPOSE

To analyze the conspicuity of pancreatic ductal adenocarcinoma (PDAC) in virtual monoenergetic images (VMI) on a novel photon-counting detector CT (PCD-CT) in comparison to energy-integrating CT (EID-CT).

METHODS

Inclusion criteria comprised initial diagnosis of PDAC (reference standard: histopathological analysis) and standardized contrast-enhanced CT imaging either on an EID-CT or a PCD-CT. Patients were excluded due to different histopathological diagnosis or missing tumor delineation on CT. On the PCD-CT, 40-190 keV VMI reconstructions were generated. Image noise, tumor-to-pancreas ratio (TPR) and contrast-to-noise ratio (CNR) were analyzed by ROI-based measurements in arterial and portal venous contrast phase. Two board-certified radiologist evaluated image quality and tumor delineation at both, EID-CT and PCD-CT (40 and 70 keV).

RESULTS

Thirty-eight patients (mean age 70.4 years ± 10.3 [range 45-91], 27 males; PCD-CT: n=19, EID-CT: n=19) were retrospectively included. On the PCD-CT, tumor conspicuity (reflected by low TPR and high CNR) was significantly improved at low-energy VMI series (≤ 70 keV compared to > 70 keV), both in arterial and in portal venous contrast phase (P < 0.001), reaching the maximum at 40 keV. Comparison between PCD-CT and EID-CT showed significantly higher CNR on the PCD-CT in portal venous contrast phase at < 70 keV (P < 0.016). On the PCD-CT, tumor conspicuity was improved in portal venous contrast phase compared to arterial contrast phase especially at the lower end of the VMI spectrum (≤ 70 keV). Qualitative analysis revealed that tumor delineation is improved in 40 keV reconstructions compared to 70 keV reconstructions on a PCD-CT.

CONCLUSION

PCD-CT VMI reconstructions (≤ 70 keV) showed significantly improved conspicuity of PDAC in quantitative and qualitative analysis in both, arterial and portal venous contrast phase, compared to EID-CT, which may be important for early detection of tumor tissue in clinical routine. Tumor delineation was superior in portal venous contrast phase compared to arterial contrast phase.

Artificial intelligence system for the detection of Barrett's esophagus

BACKGROUND

Barrett's esophagus (BE), which has increased in prevalence worldwide, is a precursor for esophageal adenocarcinoma. Although there is a gap in the detection rates between endoscopic BE and histological BE in current research, we trained our artificial intelligence (AI) system with images of endoscopic BE and tested the system with images of histological BE.

AIM

To assess whether an AI system can aid in the detection of BE in our setting.

METHODS

Endoscopic narrow-band imaging (NBI) was collected from Chung Shan Medical University Hospital and Changhua Christian Hospital, resulting in 724 cases, with 86 patients having pathological results. Three senior endoscopists, who were instructing physicians of the Digestive Endoscopy Society of Taiwan, independently annotated the images in the development set to determine whether each image was classified as an endoscopic BE. The test set consisted of 160 endoscopic images of 86 cases with histological results.

RESULTS

Six pre-trained models were compared, and EfficientNetV2B2 (accuracy [ACC]: 0.8) was selected as the backbone architecture for further evaluation due to better ACC results. In the final test, the AI system correctly identified 66 of 70 cases of BE and 85 of 90 cases without BE, resulting in an ACC of 94.37%.

CONCLUSION

Our AI system, which was trained by NBI of endoscopic BE, can adequately predict endoscopic images of histological BE. The ACC, sensitivity, and specificity are 94.37%, 94.29%, and 94.44%, respectively.

Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis

Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.

Utilising Pancreatic Exocrine Insufficiency in the Detection of Resectable Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is usually diagnosed late, leading to a high mortality rate. Early detection facilitates better treatment options. The aim of this UK-based case-control study was to determine whether two validated tests for pancreatic exocrine insufficiency (PEI), namely, the 13C-mixed triglyceride breath test (13C-MTGBT) and a faecal elastase (FE-1) test, can discriminate between patients with resectable PDAC versus healthy volunteers (HVs) along with a comparison group with chronic pancreatitis (CP). Discrimination between disease states and HVs was tested with receiver operator characteristic (ROC) curves. In total, 59 participants (23 PDAC (16 men), 24 HVs (13 men) and 12 CP (10 men)) were recruited, with a similar age in each population, and a combined median (IQR) age of 66 (57-71). The areas under the ROC curve for discriminating between PDAC and HVs were 0.83 (95% CI: 0.70-0.96) for the 13C-MTGBT, and 0.85 (95% CI: 0.75-0.95) for the FE-1 test. These were similar to CP vs. HV. In conclusion, PEI occurs in resectable PDAC to a similar extent as in CP; further large-scale, prospective studies using these tests in the primary care setting on high-risk groups are warranted.

MicroRNA 211-5p inhibits cancer cell proliferation and migration in pancreatic cancer by targeting BMP2

MicroRNAs (miRNAs) are essential to the tumour growth and metastasis of several cancers. However, the implied functions of miR-211-5p in pancreatic cancer (PC) remains poorly known. In the present study, we discovered that miR-211-5p was a significantly downregulated miRNA in PC tissues compared to adjacent non-tumour tissues. Moreover, we revealed that miR-211-5p overexpression suppressed the proliferation and metastasis of PC cells. Mechanistically, miR-211-5p directly bond to 3'UTR of bone morphogenetic protein-2 (BMP2) and negatively regulated its expression. Rescue experiments showed that the biological function of miR-211-5p was reversed by BMP-2 overexpression in PC cells. Clinical data indicated that BMP2 expression was negatively correlated with miR-211-5p levels in PC patients. Our study provided evidence that miR-211-5p served as a significant suppressor in PC, provided potential targets for prognosis and treatment of patients with PC.

Circulating autoantibodies to alpha-enolase (ENO1) and far upstream element-binding protein 1 (FUBP1) are negative prognostic factors for pancreatic cancer patient survival

Pancreatic ductal adenocarcinoma (PDA) has a dismal prognosis due to a lack of early diagnostic markers and effective therapy. In PDA patients, the glycolytic enzyme and plasminogen receptor alpha-enolase (ENO1) and the transcription factor far upstream element-binding protein 1 (FUBP1) are upregulated and elicit the production of autoantibodies (aAb) that discriminate healthy subjects from PDA patients, with the latter mostly directed to post-translational phosphorylated isoforms. Here, the correlation of prognosis with circulating ENO1 and FUBP1aAb, and their protein tissue expression was analyzed in PDA patients. Circulating ENO1 and FUBP1 aAb was analyzed in two cohorts of PDA patients by ELISA (n = 470), while tissues expression was observed by immunohistochemistry (n = 45). Overall survival (OS) was estimated using the Kaplan-Meier method, while the Cox model was used to estimate the hazard ratios (HR) adjusted for the main prognostic factors. Logistic models were applied to assess associations between death and its risk indicators. All statistical analyses were performed with Stata version 15. Unlike ENO1 aAb, there was a significant correlation between FUBP1 aAb and FUBP1 expression in tumors (p = 0.0268). In addition, we found that high ENO1 (p = 0.016) and intermediate FUBP1 aAb levels (p = 0.013) were unfavorable prognostic factors. Notably, it was found that high anti-FUBP1 aAb level is a good prognostic marker for tail-body PDA (p = 0.016). Our results suggest that different levels of circulating aAb to ENO1 and FUBP1 predict a poor outcome in PDA patients and can be used to improve therapeutic strategies.

Large-scale pancreatic cancer detection via non-contrast CT and deep learning

Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986-0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening.

Quantifying the potential benefits of early detection for pancreatic cancer through a counterfactual simulation modeling analysis

The benefits of cancer early detection depend on various factors, including cancer type, screening method performance, stage at diagnosis, and subsequent treatment. Although numerous studies have evaluated the effectiveness of screening interventions for identifying cancer at earlier stages, there is no quantitative analysis that studies the optimal early detection time interval that results in the greatest mortality benefit; such data could serve as a target and benchmark for cancer early detection strategies. In this study, we focus on pancreatic ductal adenocarcinoma (PDAC), a cancer known for its lack of early symptoms. Consequently, it is most often detected at late stages when the 5-year survival rate is only 3%. We developed a PDAC population model that simulates an individual patient's age and stage at diagnosis, while replicating overall US cancer incidence and mortality rates. The model includes "cancer sojourn time," serving as a proxy for the speed of cancer progression, with shorter times indicating rapid progression and longer times indicating slower progression. In our PDAC model, our hypothesis was that earlier cancer detection, potentially through a hypothetical screening intervention in the counterfactual analysis, would yield reduced mortality as compared to a no-screening group. We found that the benefits of early detection, such as increased life-years gained, are greater when the sojourn time is shorter, reaching their maximum when identification is made 4-6 years prior to clinical diagnosis (e.g., when a symptomatic diagnosis is made). However, when early detection occurs even earlier, for example 6-10 years prior to clinical diagnosis, the benefits significantly diminish for shorter sojourn time cancers, and level off for longer sojourn time cancers. Our study clarifies the potential benefits of PDAC early detection that explicitly incorporates individual patient heterogeneity in cancer progression and identifies quantitative benchmarks for future interventions.

Nuclear overexpression of DNA damage-inducible transcript 4 (DDIT4) is associated with aggressive tumor behavior in patients with pancreatic tumors

DNA damage-inducible transcript 4 (DDIT4) is induced in various cellular stress conditions. Several studies showed that the dysregulation of DDIT4 is involved in different malignancies with paradoxical expressions and roles. Therefore, this study investigated the clinical significance, prognostic, and diagnostic value of DDIT4 in different types of pancreatic tumors (PT). The expression of DDIT4 and long non-coding RNA (TPTEP1) in mRNA level was examined in 27 fresh PT samples using Real-time quantitative PCR (RT-qPCR). Moreover, 200 formalin-fixed paraffin-embedded PT tissues, as well as 27 adjacent normal tissues, were collected to evaluate the clinical significance, prognostic, and diagnosis value of DDIT4 expression by immunohistochemistry (IHC) on tissue microarrays (TMA) slides. The results of RT-qPCR showed that the expression of DDIT4 in tumor samples was higher than in normal samples which was associated with high tumor grade (P = 0.015) and lymphovascular invasion (P = 0.048). Similar to this, IHC findings for nucleus, cytoplasm, and membrane localization showed higher expression of DDIT4 protein in PT samples rather than in nearby normal tissues. A statistically significant association was detected between a high level of nuclear expression of DDIT4 protein, and lymphovascular invasion (P = 0.025), as well as advanced TNM stage (P = 0.034) pancreatic ductal adenocarcinoma (PDAC) and in pancreatic neuroendocrine tumor (PNET), respectively. In contrast, a low level of membranous expression of DDIT4 protein showed a significant association with advanced histological grade (P = 0.011), margin involvement (P = 0.007), perineural invasion (P = 0.023), as well as lymphovascular invasion (P = 0.005) in PDAC. No significant association was found between survival outcomes and expression of DDIT4 in both types. It was found that DDIT4 has rational accuracy and high sensitivity as a diagnostic marker. Our results revealed a paradoxical role of DDIT4 expression protein based on the site of nuclear and membranous expression. The findings of this research indicated that there is a correlation between elevated nuclear expression of DDIT4 and the advancement and progression of disease in patients with PT. Conversely, high membranous expression of DDIT4 was associated with less aggressive tumor behavior in patients with PDAC. However, further studies into the prognostic value and biological function of DDIT4 are needed in future studies.

Therapeutic Strategies for Pancreatic-Cancer-Related Type 2 Diabetes Centered around Natural Products

Pancreatic ductal adenocarcinoma (PDAC), a highly malignant neoplasm, is classified as one of the most severe and devastating types of cancer. PDAC is a notable malignancy that exhibits a discouraging prognosis and a rising occurrence. The interplay between diabetes and pancreatic cancer exhibits a reciprocal causation. The identified metabolic disorder has been observed to possess noteworthy consequences on health outcomes, resulting in elevated rates of morbidity. The principal mechanisms involve the suppression of the immune system, the activation of pancreatic stellate cells (PSCs), and the onset of systemic metabolic disease caused by dysfunction of the islets. From this point forward, it is important to recognize that pancreatic-cancer-related diabetes (PCRD) has the ability to increase the likelihood of developing pancreatic cancer. This highlights the complex relationship that exists between these two physiological states. Therefore, we investigated into the complex domain of PSCs, elucidating their intricate signaling pathways and the profound influence of chemokines on their behavior and final outcome. In order to surmount the obstacle of drug resistance and eliminate PDAC, researchers have undertaken extensive efforts to explore and cultivate novel natural compounds of the next generation. Additional investigation is necessary in order to comprehensively comprehend the effect of PCRD-mediated apoptosis on the progression and onset of PDAC through the utilization of natural compounds. This study aims to examine the potential anticancer properties of natural compounds in individuals with diabetes who are undergoing chemotherapy, targeted therapy, or immunotherapy. It is anticipated that these compounds will exhibit increased potency and possess enhanced pharmacological benefits. According to our research findings, it is indicated that naturally derived chemical compounds hold potential in the development of PDAC therapies that are both safe and efficacious.

Blood-based Migration Signature Biomarker Panel Discriminates Early Stage New Onset Diabetes related Pancreatic Ductal Adenocarcinoma from Type 2 Diabetes

BACKGROUND AND AIMS

While type 2 diabetes is a well-known risk factor for pancreatic ductal adenocarcinoma (PDAC), PDAC-induced new-onset diabetes (PDAC-NOD) is a manifestation of underlying PDAC. In this study, we sought to identify potential blood-based biomarkers for distinguishing PDAC-NOD from type 2 diabetes (T2DM) without PDAC.

MATERIALS AND METHODS

By ELISA analysis, a migration signature biomarker panel comprising tissue factor pathway inhibitor (TFPI), tenascin C (TNC-FNIII-C) and CA 19-9 was analyzed in plasma samples from 50 PDAC-NOD and 50 T2DM controls.

RESULTS

Both TFPI (area under the curve (AUC) 0.71) and TNC-FNIII-C (AUC 0.69) outperformed CA 19-9 (AUC 0.60) in distinguishing all stages of PDAC-NOD from T2DM controls. The combined panel showed an AUC of 0.82 (95% CI = 0.73-0.90) (p = 0.002). In the PDAC-NOD early stage II samples, the three biomarkers had an AUC of 0.84 (95% CI = 0.73-0.93) vs CA 19-9, AUC = 0.60, (95% CI = 0.45-0.73), which also improved significance (p = 0.0123).

CONCLUSION

The migration signature panel adds significantly to CA 19-9 to discriminate PDAC-NOD from T2DM controls and warrants further validation for high-risk group stratification.

[Type 3 c diabetes: Approach from the first level doctor]

DM3c is diabetes (DM) of the exocrine pancreas that must be suspected whenever there is a history of chronic pancreatitis (CP), acute pancreatitis (AP) or recurrence (80% of cases) or new-onset DM in individuals from over 50 years of age without any other justification (negative autoimmunity tests, Glutamic Acid Decarboxylase antibodies). It is an entity misdiagnosed as type 2 diabetes (DM2) (90%) and therefore, if it is not suspected, it can go unnoticed. For its diagnosis, abdominal ultrasound, determination of the CA 19.9 tumor antigen (carbohydrate antigen 19-9), nuclear magnetic resonance (NMR) or computerized axial tomography (CT) are useful. The treatment is the same as DM2, although certain specifications depend on the type of drugs and with the particularity that in dealing with «fragile diabetes» greater caution must be taken with hypoglycemia (monitoring). Likewise, as it is a disease of the exocrine pancreas, it will have to be specifically treated to avoid metabolic, malabsorptive and/or nutritional alterations.

A narrative review of imaging for pancreas adenocarcinoma: staging, surgical considerations, and surveillance

Background and Objective

Pancreas adenocarcinoma is a disease with dire prognosis. Imaging is pivotal to the diagnosis, staging, reassessment, surgical planning, and surveillance of pancreas cancer. The purpose of this paper is to provide the reader an overview of current imaging practices for pancreas adenocarcinoma.

Methods

A literature search of original papers and reviews through 2022 was performed using the PubMed database. The most current American College of Radiology Appropriateness Criteria and National Comprehensive Cancer Network guidelines on pancreas cancer imaging were also included.

Key Content and Findings

Multidisciplinary team care at a high-volume institution is instrumental to optimal patient management and outcomes. It is therefore important for all team members to be aware of imaging modality options, strengths, and challenges. Additionally, a high-level understanding of imaging findings is useful clinically. This manuscript provides a current overview of imaging modalities used in the identification and assessment of pancreas adenocarcinoma, including ultrasound, computed tomography, magnetic resonance imaging, and positron emission tomography. Imaging findings, including the expected and unexpected, are reviewed to give the novice imager a better understanding.

Conclusions

This review provides a current overview of imaging for pancreas adenocarcinoma, including strengths and weakness of various imaging modalities; therefore, providing the reader with a robust resource when considering imaging in the management of this disease.

Systemic treatment for advanced pancreatic cancer

Pancreatic cancer is a deadly disease with an extremely poor 5-year survival rate due to treatment resistance and late-stage detection. Despite numerous years of research and pharmaceutical development, these figures have not changed. Treatment options for advanced pancreatic cancer are still limited. This illness is typically detected at a late stage, making curative surgical resection impossible. Chemotherapy is the most commonly utilized technique for treating advanced pancreatic cancer but has poor efficacy. Targeted therapy and immunotherapy have made significant progress in many other cancer types and have been proven to have extremely promising possibilities; these therapies also hold promise for pancreatic cancer. There is an urgent need for research into targeted treatment, immunotherapy, and cancer vaccines. In this review, we emphasize the foundational findings that have fueled the therapeutic strategy for advanced pancreatic cancer. We also address current advancements in targeted therapy, immunotherapy, and cancer vaccines, all of which continue to improve the clinical outcome of advanced pancreatic cancer. We believe that clinical translation of these novel treatments will improve the low survival rate of this deadly disease.