Solid and cystic intrapancreatic accessory spleen: report of 10 cases in a single institution

BACKGROUND

Precise diagnosis of intrapancreatic accessory spleen (IPAS) remains challenging due to its rarity and diverse presentations. Despite comprehensive examinations, including radiography and other diagnostic methods, the potential for malignancy cannot be excluded, often leading to unnecessary pancreatic surgeries. We review our institutional experience to provide insights for accurately distinguishing IPAS.

METHODS

We retrospectively reviewed 10 patients who underwent distal pancreatectomy for the lesion in the pancreas tail which was determined to be IPAS on final pathology at our institution between January 2020 and April 2024. The presenting symptoms, medical history, preoperative imaging, operative therapy, final pathology and postoperative course were evaluated.

RESULTS

Patient ages ranged from 30 to 72 (median 55.5), including six women and four men. Most patients were asymptomatic. One patient had the medical history of splenectomy. Lesions ranged from 1.4 to 7.3 cm (mean 2.9 cm). All lesions were located in the pancreatic tail. On radiologic evaluation, these lesions had both solid and cystic presentations. The most common operative approach was laparoscopic distal pancreatectomy and splenectomy. Four patients were diagnosed with epidermoid cysts arising in intrapancreatic accessory spleen (ECIPAS) on final pathologic evaluation.

CONCLUSIONS

IPAS are predominantly benign lesions which have solid and cystic presentations commonly mistaken for pancreatic neoplasms. Combining CT, MRI, EUS-FNA and nuclear medicine may enhance IPAS detection, though no definitive diagnostic method exists. Increased awareness of IPAS in the differential diagnosis of pancreatic tail tumors, coupled with advancements in imaging techniques could improve diagnostic accuracy and exclude malignancy, preventing unnecessary surgeries.

Uncovering the therapeutic potentials of marine-derived natural compounds with small amounts for neurological disorders

The discovery of novel drugs from the ocean is a relatively recent development. However, for most studies of marine drugs, the priority is the discovery of compounds with new structures. Normally, only small amounts (< 1 mg) of new compounds could be extracted from marine microbes, and it is difficult to evaluate the therapeutic potentials of these newly-identified marine-derived natural compounds by traditional cell- or animal-based phenotypic screenings. Genes play a crucial role in determining the phenotype of diseases and the action of drugs. By comparing genomic expression associated with disease conditions and compound treatments, it is possible to predict the potential of a certain compound to counteract a specific type of disease. In this study, marine-derived natural compounds-induced genomic changes in cells were collected either from public databases or by using RNA-seq analysis. The therapeutic potentials of representative marine-derived natural compounds, namely phycocyanobilin, cycloheximide and NBU-1, a newly-identified natural compound extracted from a marine sponge-associated Streptomyces, on bipolar disorder (BD), Parkinson's disease (PD) and Alzheimer's disease (AD), were predicted by gene set enrichment analysis (GSEA), respectively. The anti-neurological disorder activity of these marine-derived natural compounds were further validated in methamphetamine-induced rats mimicking manic phase of BD, 6-OHDA-treated PC12 cells mimicking PD neurotoxicity and β-amyloid oligomer-incubated SH-SY5Y cells mimicking AD neuronal loss. Our study provides not only new insights for pharmacological applications of the marine-derived natural compounds here studied, but also a method for predicting and evaluating therapeutic potentials of newly-identified marine-derived natural compounds with small quantities.

Advances in nano drug delivery systems for enhanced efficacy of emodin in cancer therapy

Cancer remains one of the leading causes of death worldwide, highlighting the urgent need for novel antitumor drugs. Natural products have long been a crucial source of anticancer agents. Among these, emodin (EMO), a multifunctional anthraquinone compound, exhibits significant anticancer effects but is hindered in clinical applications by challenges such as low solubility, rapid metabolism, poor bioavailability, and off-target toxicity. Nano drug delivery systems offer effective strategies to overcome these limitations by enhancing the solubility, stability, bioavailability, and targeting ability of EMO. While substantial progress has been made in developing EMO-loaded nanoformulations, a comprehensive review on this topic is still lacking. This paper aims to fill this gap by providing an overview of recent advancements in nanocarriers for EMO delivery and their anticancer applications. These carriers include liposomes, nanoparticles, polymeric micelles, nanogels, and others, with nanoparticle-based formulations being the most extensively explored. Nanoformulations encapsulating EMO have demonstrated promising therapeutic results against various cancers, particularly breast cancer, followed by liver and lung cancers. We systematically summarize the preparation methods, materials, and physicochemical properties of EMO-loaded nanopreparations, underscoring key findings on how nanotechnology improves the anticancer efficacy of EMO. This review provides valuable insights for researchers engaged in developing nano delivery systems for anticancer drugs.

Molecular mechanism of tumor-infiltrating immune cells regulating endometrial carcinoma

Endometrial carcinoma (EC) is a prevalent gynecological cancer, and its interaction with the immune system is pivotal in cancer progression. This comprehensive review explores the molecular mechanisms involved in the regulation of EC by tumor-infiltrating immune cells. This review discusses the composition and functions of various immune cell types within the tumor microenvironment, including T cells, B cells, macrophages, and natural killer cells, and elucidates their specific roles in cancer control. It also delves into the immune evasion strategies employed by EC cells, with a specific focus on immune checkpoint pathways and their influence on tumor development. Signaling pathways, cytokines, and chemokines mediating immune responses within the tumor microenvironment are also detailed. Furthermore, clinical implications and therapeutic strategies, such as immunotherapies, are also reviewed, and relevant clinical trials are discussed. Additionally, this review discusses the existing gaps in our knowledge, suggests potential avenues for future research, and emphasizes the significance of understanding these mechanisms for enhanced EC treatment. This review provides an exhaustive overview of the current knowledge, supporting the ongoing quest for more effective therapeutic interventions on EC.

Erythrocyte membrane vesicles as drug delivery systems: A systematic review of preclinical studies on biodistribution and pharmacokinetics

This systematic review aims to summarize the development of erythrocyte membrane vesicles (EMVs) as drug delivery carriers, with a focus on elucidating their fate in terms of biodistribution and pharmacokinetics in preclinical studies. The PubMed database was systematically reviewed to search for original peer-reviewed published studies on the use of EMVs for drug delivery to summarize the preclinical findings, following the PRISMA guidelines. A total of 142 articles matched the selection criteria and were included in the review. For each study, the following parameters were extracted: type of active pharmaceutical ingredient (API) encapsulated into EMVs, EMVs-API formulation method and final particle size, EMVs surface modifications for active targeting, cell lines and animal models used in the study, crucial treatment data, biodistribution data and finally, where applicable, data about the EMVs circulation time and blood half-life. EMVs size did not vary significantly among the different formulation methods. A complete list of cell lines and animal models used is provided. Circulation times and data for blood half-life were grouped per animal type. For the most commonly used animal type, BALB/c mice, the average half-life of EMV-API was calculated to be 10.4 h, and in all cases, up to a 10-fold increase was observed compared with that of free API. Surface modifications did not drastically change the circulation time but did improve target tissue accumulation. The most critical weaknesses in the analysed studies were identified. Key points for future studies are provided to fill the current knowledge gaps and improve the quality of publications.

Radiomics for Differentiating Pancreatic Mucinous Cystic Neoplasm from Serous Cystic Neoplasm: Systematic Review and Meta-Analysis

BACKGROUND

As pancreatic cystic neoplasms (PCN) differ in current standard of care, and these treatments can affect quality of life to varying degrees, a definitive preoperative diagnosis must be reliable. Current diagnostic approaches, specifically traditional cross-sectional imaging techniques, face certain limitations. But radiomics has been shown to have high diagnostic accuracy across a range of diseases. Objective to conduct a comprehensive review of the literature on the use of radiomics to differentiate Mucinous Cystic Neoplasm (MCN) from Serous Cystic Neoplasm (SCN).

METHODS

This study was comprehensively searched in Pubmed, Scopus and Web of Science databases for meta-analysis of studies that used radiomics to distinguish MCN from SCN. Risk of bias was assessed using the diagnostic accuracy study quality assessment method and combined with sensitivity, specificity, diagnostic odds ratio, and summary receiver operating characteristic (SROC)curve analysis.

RESULTS

A total of 884 patients from 8 studies were included in this analysis, including 365 MCN and 519 SCN. The Meta-analysis found that radiomics identified MCN and SCN with high sensitivity and specificity, with combined sensitivity and specificity of 0.84(0.82-0.87) and 0.82(0.79-0.84). The positive likelihood ratio (PLR) and the negative likelihood ratio (NLR) are 5.61(3.72, 8.47) and 0.14(0.09-0.26). In addition, the area under the SROC curve (AUC) was drawn at 0.93. No significant risk of publication bias was detected through the funnel plot analysis. The performances of feature extraction from the volume of interest (VOI) or Using AI classifier in the radiomics models were superior to those of protocols employing region of interest (ROI) or absence of AI classifier.

CONCLUSION

This meta-analysis demonstrates that radiomics exhibits high sensitivity and specificity in distinguishing between MCN and SCN, and has the potential to become a reliable diagnostic tool for their identification.

Traditional Chinese medicine and natural products in management of hepatocellular carcinoma: Biological mechanisms and therapeutic potential

Hepatocellular carcinoma (HCC), originating from hepatocytes, is the most common type of primary liver cancer. HCC imposes a significant global health burden with high morbidity and mortality, making it a critical public concern. Surgical interventions, including hepatectomy and liver transplantation, are pivotal in achieving long-term survival for patients with HCC. Additionally, ablation therapy, endovascular interventional therapy, radiotherapy, and systemic anti-tumor therapies are commonly employed. However, these treatment modalities are often associated with considerable challenges, including high postoperative recurrence rates and adverse effects. Traditional Chinese medicine (TCM) and natural products have been utilized for centuries as a complementary approach in managing HCC and its complications, demonstrating favorable clinical outcomes. Various bioactive compounds derived from TCM and natural products have been identified and purified, and their mechanisms of action have been extensively investigated. This review aims to provide a comprehensive and up-to-date evaluation of the clinical efficacy of TCM, natural products and their active constituents in the treatment and management of HCC. Particular emphasis is placed on elucidating the potential molecular mechanisms and therapeutic targets of these agents, including their roles in inhibiting HCC cell proliferation, inducing apoptosis and pyroptosis, suppressing tumor invasion and metastasis, and restraining angiogenesis within HCC tissues.

Mechanistic insights and therapeutic strategies for targeting autophagy in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is characterised by early metastasis and resistance to anti-cancer therapy, leading to an overall poor prognosis. Macroautophagy (hereinafter referred to as autophagy) is a conserved cellular homeostasis mechanism that degrades various cargoes (e.g., proteins, organelles, and pathogens) mainly playing a role in promoting survival under environmental stress. Autophagy is an essential defense mechanism against PDAC initiation, acting on multiple levels to maintain cellular and tissue homeostasis. However, autophagy is also intimately involved in the molecular mechanisms driving PDAC progression, facilitating the adaptation of cancer cells to the tumor microenvironment's harsh conditions. In this review, we examine the complex role of autophagy in PDAC and assess the potential of modulating autophagy as a therapeutic strategy. By reviewing current research and clinical trials, we seek to elucidate how targeting autophagy can disrupt PDAC tumor survival mechanisms, enhance the efficacy of existing treatments, and ultimately improve patient outcomes.

Advancements in molecular imaging for the diagnosis and treatment of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant tumor characterized by poor overall patient survival and prognosis, largely due to challenges in early diagnosis, limited surgical options, and a high propensity for therapy resistance. The integration of various imaging modalities through molecular imaging techniques, particularly multimodal molecular imaging, offers the potential to provide more precise and comprehensive information about the lesion. With advances in nanomedicine, new imaging and drug delivery approaches that allow the development of multifunctional theranostic agents offer opportunities for improving pancreatic cancer treatment using precision oncology. Herein, we review the diagnostic and therapeutic applications of molecular imaging for PDAC and discuss the adoption of multimodal imaging approaches that combine the strengths of different imaging techniques to enhance diagnostic accuracy and therapeutic efficacy. We emphasize the significant role of nanomedicine technology in advancing multimodal molecular imaging and theranostics, and their potential impact on PDAC management. This comprehensive review aims to serve as a valuable reference for researchers and clinicians, offering insights into the current state of molecular imaging in PDAC and outlining future directions for improving early diagnosis, combination therapies, and prognostic evaluations.

Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology

BACKGROUND AND AIM

Colorectal cancer is among the most prevalent and deadliest cancers. Early prediction of metastasis in patients with colorectal cancer is crucial in preventing it from the advanced stages and enhancing the prognosis among these patients. So far, previous studies have been conducted to predict metastasis in colorectal cancer patients using clinical data. The current research attempts to leverage a combination of demographic, lifestyle, nutritional, and clinical factors, such as diagnostic and therapeutical factors, to construct an ML model with more predictive insights and generalizability than previous ones.

MATERIALS AND METHODS

In this retrospective study, we used 1156 CRC patients referred to the Masoud internal clinic in Tehran City from January 2017 to December 2023. The chosen machine learning algorithms, including LightGBM, XG-Boost, random forest, artificial neural network, support vector machine, decision tree, K-Nearest Neighbor and logistic regression, were utilized to establish prediction models for predicting metastasis among colorectal cancer patients. We also assessed features based on the best-performing model to improve clinical usability. To show the generalizability of the established prediction model for predicting CRC metastasis, we leveraged the data of 115 CRC patients from Imam Khomeini Hospital in Sari City. We assessed the predictive ability of LightGBM as the best-performing model based on external data.

RESULTS

The LightGBM model with a PPV of 97.32%, NPV of 84.67%, sensitivity of 83.14%, specificity of 93.14%, accuracy of 88.14%, F1-score of 87.51%, and an AU-ROC of 0.9 [Formula: see text]0.01 obtained satisfactory performance for prediction purposes on this topic. Factors including the history of IBD, family history of CRC, number of lymph nodes involved, fruit intake, and tumor size were considered as more strengthful predictors for metastasis in colorectal cancer and clinical usability. The external validation cohort showed a PPV of 0.8, NPV of 0.85, sensitivity of 0.78, specificity of 0.86, accuracy of 0.834, F1-score of 0.795, and AU-ROC of 0.77[Formula: see text]0.03, demonstrating satisfactory generalizability when leveraging external data from other clinical settings.

CONCLUSION

The current empirical results indicated that LighGBM has predictive competency that can be leveraged by physicians in clinical environments for early prediction of metastasis and enhanced prognosis in patients with colorectal cancer.

CLINICAL TRIAL NUMBER

Not applicable.

Impact of functionality and grading on survival in pancreatic neuroendocrine tumor patients receiving peptide receptor radionuclide therapy

Background

Peptide receptor radionuclide therapy (PRRT) is a well-established treatment option for neuroendocrine tumors (NET), yet randomized controlled trials have not provided data on its impact on overall survival. The real-world efficacy of PRRT and its association with tumor functionality and grading in pancreatic neuroendocrine tumors (PanNET) remains underexplored.

Methods

A retrospective analysis of 166 patients with histologically confirmed metastatic PanNET was performed. Subgroup analyses examined progression-free survival (PFS) and overall survival (OS) following PRRT cycles, stratified by tumor grading, tumor functionality and bone metastases.

Results

Of 166 patients, 100 (60.2%) received PRRT with a median of four cycles. In the PRRT cohort, 68% of patients had deceased. PFS after four and eight consecutive cycles was 20 and 18 months, respectively (p=0.4). OS for the entire cohort was 79 months, with patients receiving 4+ cycles of PRRT having an OS of 87 months and those receiving 5+ cycles achieving an OS of 100 months. Patients with grade 1 or 2 tumors had a significantly longer median OS of 97 months compared to 74.5 months for grade 3 tumors (p = 0.0055). There was no significant difference in OS between functioning and non-functioning tumors after PRRT. Patients with bone metastases who received PRRT had a significantly shorter OS than those without (74 vs. 89 months, p = 0.013). In 19% of patients who received PRRT, therapy was discontinued due to progressive disease, toxicity or death.

Conclusions

Patients receiving extended cycles of PRRT showed improved survival outcomes in metastatic PanNET, particularly in patients with lower tumor grades and without bone metastases. No survival difference was seen between functioning and non-functioning PanNET, while patients with grade 3 tumors and bone metastases had significantly shorter survival despite PRRT.

Advancing clinical biochemistry: addressing gaps and driving future innovations

Modern healthcare depends fundamentally on clinical biochemistry for disease diagnosis and therapeutic guidance. The discipline encounters operational constraints, including sampling inefficiencies, precision limitations, and expansion difficulties. Recent advancements in established technologies, such as mass spectrometry and the development of high-throughput screening and point-of-care technologies, are revolutionizing the industry. Modern biosensor technology and wearable monitors facilitate continuous health tracking, Artificial Intelligence (AI)/machine learning (ML) applications enhance analytical capabilities, generating predictive insights for individualized treatment protocols. However, concerns regarding algorithmic bias, data privacy, lack of transparency in decision-making ("black box" models), and over-reliance on automated systems pose significant challenges that must be addressed for responsible AI integration. However, significant limitations remain-substantial implementation expenses, system incompatibility issues, and information security vulnerabilities intersect with ethical considerations regarding algorithmic fairness and protected health information. Addressing these challenges demands coordinated efforts between clinicians, scientists, and technical specialists. This review discusses current challenges in clinical biochemistry, explicitly addressing the limitations of reference intervals and barriers to implementing innovative biomarkers in medical settings. The discussion evaluates how advanced technologies and multidisciplinary collaboration can overcome these constraints while identifying research priorities to enhance diagnostic precision and accessibility for better healthcare delivery.

Prediction of solid pseudopapillary tumor invasiveness of the pancreas based on multiphase contrast-enhanced CT radiomics nomogram

Objectives

To construct a multiphase contrast-enhanced CT-based radiomics nomogram that combines traditional CT features and radiomics signature for predicting the invasiveness of pancreatic solid pseudopapillary neoplasm (PSPN).

Methods

A total of 114 patients with surgical pathologic diagnoses of PSPN were retrospectively included and classified into training (n = 79) and validation sets (n = 35). Univariate and multivariate analyses were adopted for screening traditional CT features significantly associated with the invasiveness of PSPN as independent predictors, and a traditional CT model was established. Radiomics features were extracted from the contrast-enhanced CT images, and logistic regression analysis was employed to establish a machine learning model, including an unenhanced model (model U), an arterial phase model (model A), a venous phase model (model V), and a combined radiomics model (model U+A+V). A radiomics nomogram was subsequently constructed and visualized by combining traditional CT independent predictors and radiomics signature. Model performance was assessed through Delong's test and receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was applied to assess the model's clinical utility.

Results

Multivariate analysis suggested that solid tumors (OR = 6.565, 95% CI: 1.238-34.816, P = 0.027) and ill-defined tumor margins (OR = 2.442, 95% CI: 1.038-5.741, P = 0.041) were independent predictors of the invasiveness of PSPN. The areas under the curve (AUCs) of the traditional CT model in the training and validation sets were 0.653 and 0.797, respectively. Among the four radiomics models, the model U+A+V exhibited the best diagnostic performance, with AUCs of 0.857 and 0.839 in the training and validation sets, respectively. In addition, the AUCs of the nomogram in the training and validation sets were 0.87 and 0.867, respectively, which were better than those of the radiomics model and the traditional CT model. The DCA results indicated that with the threshold probability being within the relevant range, the radiomics nomogram offered an increased net benefit to clinical decision making.

Conclusion

Multiphase contrast-enhanced CT radiomics can noninvasively predict the invasiveness of PSPN. In addition, the radiomics nomogram combining radiomics signature and traditional CT signs can further improve classification ability.

Role of emodin to prevent gastrointestinal cancers: recent trends and future prospective

Gastrointestinal malignancies are responsible for approximately 35% of all cancer-related deaths, underscoring the critical need to explore pharmacologically active molecules for chemoprevention. Emodin (1,3,8-trihydroxy-6-methylanthraquinone), a natural compound derived from traditional Chinese and Japanese medicine, has recently garnered significant attention for its potential anticancer properties. Emodin exerts its chemoprotective effects through a combination of antioxidative, anti-inflammatory, and anti-proliferative mechanisms. Research indicates that emodin inhibits cancer metastasis, disrupts cell cycle progression, and impairs cancer cell survival. These effects are mediated through the activation of the p38 MAPK/JNK1/2 signaling pathway, the upregulation of pro-apoptotic factors such as Bax/Bcl-2 and caspases, and the enhancement of reactive oxygen species (ROS) levels (Supplementary Fig. 1). To optimize emodin's therapeutic potential, it is crucial to further investigate its underlying mechanisms of action and develop advanced nano-targeted delivery systems to enhance its bioavailability. This review highlights emodin's promise as a chemopreventive agent for gastrointestinal cancers and emphasizes its potential for development into a novel clinical formulation.

An integrative multi-omics analysis reveals a multi-analyte signature of pancreatic ductal adenocarcinoma in serum

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) remains a formidable health challenge due to its detection at a late stage and a lack of reliable biomarkers for early detection. Although levels of carbohydrate antigen 19-9 are often used in conjunction with imaging-based tests to aid in the diagnosis of PDAC, there is still a need for more sensitive and specific biomarkers for early detection of PDAC.

METHODS

We obtained serum samples from 88 subjects (patients with PDAC (n = 58) and controls (n = 30)). We carried out a multi-omics analysis to measure cytokines and related proteins using proximity extension technology and lipidomics and metabolomics using tandem mass spectrometry. Statistical analysis was carried out to find molecular alterations in patients with PDAC and a machine learning model was used to derive a molecular signature of PDAC.

RESULTS

We quantified 1,462 circulatory proteins along with 873 lipids and 1,001 metabolites. A total of 505 proteins, 186 metabolites and 33 lipids including bone marrow stromal antigen 2 (BST2), keratin 18 (KRT18), and cholesteryl ester(20:5) were found to be significantly altered in patients. We identified different levels of sphingosine, sphinganine, urobilinogen and lactose indicating that glycosphingolipid and galactose metabolisms were significantly altered in patients compared to controls. In addition, elevated levels of diacylglycerols and decreased cholesteryl esters were observed in patients. Using a machine learning model, we identified a signature of 38 biomarkers for PDAC, composed of 21 proteins, 4 lipids, and 13 metabolites.

CONCLUSIONS

Overall, this study identified several proteins, metabolites and lipids involved in various pathways including cholesterol and lipid metabolism to be changing in patients. In addition, we discovered a multi-analyte signature that could be further tested for detection of PDAC.

Predictive impact of T2-MRI radiomics model on initial diagnosis of bone metastasis in prostate cancer patients

OBJECTIVE

The purpose of this study was to examine the potential predictive impact of the T2-MRI radiomics model on the initial diagnosis of bone metastasis in patients with prostate cancer (PCa).

METHODS

We retrospectively analyzed a total of 141 patients with confirmed PCa from clinical pathology records. Among them, 52 cases had bone metastasis and 89 cases did not. By employing a computer, the patients were randomly assigned to either a training group or a test group. Using ITK-SNAP software, we manually outlined T2WI images for all patients and performed radiomic analysis using Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. In the training group, a single-variable t-test was conducted to identify features strongly associated with PCa bone metastasis. Statistical significance was defined as P < 0.05. After dimensionality reduction, the Lasso model was employed to select the best subset, and a random forest model was established. To evaluate the performance of the radiomics model in predicting PCa bone metastasis in the test group, receiver operating characteristic (ROC) curves and confusion matrices were utilized.

RESULTS

The selected imaging features exhibited a significant correlation with the differential diagnosis of prostate cancer presence or absence of metastasis. The radiomic model demonstrated high predictive efficiency for PCa bone metastasis, achieving accuracy rates of 0.81% and 0.85% in the training and test groups, respectively. The sensitivities were 92% and 93%, and the specificities were 85% and 81%. The area under the curve values were 0.88 and 0.80 for the training and test groups, respectively.

CONCLUSION

The MRI radiomics method based onT2WI images shows promise in accurately predicting PCa bone metastasis and can serve as a valuable tool for developing clinical treatment plans.

Metabolite Changes Associated with Resectable Pancreatic Ductal Adenocarcinoma

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC) is insidious, with only 15-20% of those diagnosed suitable for surgical resection as it is either too advanced and has invaded local structures or has already spread to distant sites. The associated tumor microenvironment provides a protective shield which limits the efficacy of chemotherapeutic agents, but also impairs the delivery of nutrients required for the PDAC cells. To compensate for this, metabolic adaptions occur to provide alternative sources of fuel. The aim of this study is to explore metabolomic differences between participants with resectable PDAC compared to healthy volunteers (HV). The objectives were to use nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to determine if resectable PDAC induces sufficient metabolic adaptations and variations which could be used to discriminate between the two groups.

METHODS

Plasma samples were collected from fasted individuals with resectable PDAC (n = 23, median age 68 [IQR 56-75], 69.6% male) and HV (n = 24, median age 63 [IQR 58-71], 54.2% male). Samples were analyzed using NMR and the Biocrates MxP Quant 500 kit at University Hospital Southampton.

RESULTS

NMR spectroscopy identified six independent metabolites that significantly discriminated between the PDAC and HV groups, including elevated plasma concentrations of 3-hydroxybutyrate and citrate, with decreased amounts of glutamine and histidine. MS analysis identified 84 metabolites with a significant difference between the PDAC and HV cohorts. The metabolites with a fold change (FC) > 1.5 in the PDAC population were conjugated bile acids (taurocholic acid, glycocholic acid, and glycochenodexoycholic acid).

DISCUSSION

In conclusion, using metabolomics, biochemical differences between resectable PDAC and HV were detected. These differences indicate metabolic plasticity and utilization of alternative fuel sources.

Synergic value of 3D CT-derived body composition and triglyceride glucose body mass for survival prognostic modeling in unresectable pancreatic cancer

Background

Personalized and accurate survival risk prognostication remains a significant challenge in advanced pancreatic ductal adenocarcinoma (PDAC), despite extensive research on prognostic and predictive markers. Patients with PDAC are prone to muscle loss, fat consumption, and malnutrition, which is associated with inferior outcomes. This study investigated the use of three-dimensional (3D) anthropometric parameters derived from computed tomography (CT) scans and triglyceride glucose-body mass index (TyG-BMI) in relation to overall survival (OS) outcomes in advanced PDAC patients. Additionally, a predictive model for 1 year OS was developed based on body components and hematological indicators.

Methods

A retrospective analysis was conducted on 303 patients with locally advanced PDAC or synchronous metastases undergoing first-line chemotherapy, all of whom had undergone pretreatment abdomen-pelvis CT scans. Automatic 3D measurements of subcutaneous and visceral fat volume, skeletal muscle volume, and skeletal muscle density (SMD) were assessed at the L3 vertebral level by an artificial intelligence assisted diagnosis system (HY Medical). Various indicators including TyG-BMI, nutritional indicators [geriatric nutritional risk index (GNRI) and prealbumin], and inflammation indicators [(C-reactive protein (CRP) and neutrophil to lymphocyte ratio (NLR)] were also recorded. All patients underwent follow-up for at least 1 year and a dynamic nomogram for personalized survival prediction was constructed.

Results

We included 211 advanced PDAC patients [mean (standard deviation) age, 63.4 ± 11.2 years; 89 women (42.2) %)]. Factors such as low skeletal muscle index (SMI) (P = 0.011), high visceral to subcutaneous adipose tissue area ratio (VSR) (P < 0.001), high visceral fat index (VFI) (P < 0.001), low TyG-BMI (P = 0.004), and low prealbumin (P = 0.001) were identified as independent risk factors associated with 1 year OS. The area under the curve of the established dynamic nomogram was 0.846 and the calibration curve showed good consistency. High-risk patients (> 211.9 points calculated using the nomogram) had significantly reduced survival rates.

Conclusion

In this study, the proposed nomogram model (with web-based tool) enabled individualized prognostication of OS and could help to guide risk-adapted nutritional treatment for patients with unresectable PDAC or synchronous metastases.

Pancreatic cancer: failures and hopes-a review of new promising treatment approaches

Pancreatic cancer is a challenging disease with limited treatment options and a high mortality rate. Just few therapy advances have been made in recent years. Tumor microenvironment, immunosuppressive features and mutational status represent important obstacles in the improvement of survival outcomes. Up to now, first-line therapy did achieve a median overall survival of less than 12 months and this discouraging data lead clinicians all over the world to focus their efforts on various fields of investigation: 1) sequential cycling of different systemic therapy in order to overcome mechanisms of resistance; 2) discovery of new predictive bio-markers, in order to target specific patient population; 3) combination treatment, in order to modulate the tumor microenvironment of pancreatic cancer; 4) new modalities of the delivery of drugs in order to pass the physical barrier of desmoplasia and tumor stroma. This review shows future directions of treatment strategies in advanced pancreatic cancer through a deep analysis of these recent macro areas of research.

Dual mRNA nanoparticles strategy for enhanced pancreatic cancer treatment and β-elemene combination therapy

Pancreatic ductal adenocarcinoma (PDAC) is notoriously immune-resistant, limiting the clinical efficacy of single-agent immune modulators and thereby necessitating the exploration of multimodal immunotherapy combinations. Traditional approaches combining conventional immune checkpoint inhibitors with neoantigen vaccines have shown some promise in treating PDAC but are often compromised by intratumoral T lymphocyte exhaustion and systemic toxicity. Hence, novel approaches are needed to address these challenges. Herein, we demonstrate that mRNA polymeric nanoparticles encoding anti-PD-1 antibodies in situ at the tumor site enhance the therapeutic efficacy of neoantigen-based mRNA vaccine for PDAC. This mRNA-based, in situ anti-PD-1 antibody production strategy also protects tumor-infiltrating T cells from PD-1 inhibition, potentially reducing the toxicities induced by systemic checkpoint inhibition. Our study may provide an innovative dual mRNA nanoparticle strategy for effective tumor neoantigen immunotherapy, as well as an mRNA cancer combination therapy strategy with other clinically approved drugs (e.g., β-elemene).

Extensive Review of Nanomedicine Strategies Targeting the Tumor Microenvironment in PDAC

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers in the world, mainly because of its powerful pro-connective tissue proliferation matrix and immunosuppressive tumor microenvironment (TME), which promote tumor progression and metastasis. In addition, the extracellular matrix leads to vascular collapse, increased interstitial fluid pressure, and obstruction of lymphatic return, thereby hindering effective drug delivery, deep penetration, and immune cell infiltration. Therefore, reshaping the TME to enhance tumor perfusion, increase deep drug penetration, and reverse immune suppression has become a key therapeutic strategy. Traditional therapies for PDAC, including surgery, radiation, and chemotherapy, face significant limitations. Surgery is challenging due to tumor location and growth, while chemotherapy and radiation are hindered by the dense extracellular matrix and immunosuppressive TME. In recent years, the advancement of nanotechnology has provided new opportunities to improve drug efficacy. Nanoscale drug delivery systems (NDDSs) provide several advantages, including improved drug stability in vivo, enhanced tumor penetration, and reduced systemic toxicity. However, the clinical translation of nanotechnology in PDAC therapy faces several challenges. These include the need for precise targeting and control over drug release, potential immune responses to the nanocarriers, and the scalability and cost-effectiveness of production. This article provides an overview of the latest nanobased methods for achieving better therapeutic outcomes and overcoming drug resistance. We pay special attention to TME-targeted therapy in the context of PDAC, discuss the advantages and limitations of current strategies, and emphasize promising new developments. By emphasizing the enormous potential of NDDSs in improving the treatment outcomes of patients with PDAC, while critically discussing the limitations of traditional therapies and the challenges faced by nanotechnology in achieving clinical breakthroughs, our review paves the way for future research in this rapidly developing field.

Morphological characterization of atypical pancreatic ductal adenocarcinoma with cystic lesion on DCE-CT: a comprehensive retrospective study

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) with cystic features presents significant challenges in achieving an accurate preoperative diagnosis and in implementing appropriate clinical management. The aim of this study was to analyze the dynamic contrast-enhanced computed tomography (DCE-CT) findings of PDACs with cystic lesions and correlate them with histopathological findings.

METHODS

We retrospectively reviewed 40 patients with pathology-proven PDACs exhibiting cystic lesions who underwent preoperative DCE-CT imaging. The CT manifestations were classified into three subtypes based on the morphological characteristics of the cystic lesions: Type 1, small proportion (< 50%) of intratumoral cystic lesions, with or without associated peritumoral cystic lesions; Type 2, large proportion (≥ 50%) of intratumoral cystic lesions, with or without associated peritumoral cystic lesions; Type 3, a solid pancreatic mass with accompanying peritumoral cystic lesions. The DCE-CT findings were analyzed based on location, size, contour, enhancement patterns, and secondary findings, and compared with the corresponding pathological diagnoses.

RESULTS

Among the 40 patients, 23 (57.5%) tumors were located in the pancreatic body or tail. Type 1 was identified in 21 cases, Type 2 in 6 cases, and Type 3 in 13 cases. All masses exhibited a bulging pancreatic contour, with 4 cases showing isoattenuating enhancement on DCE-CT. Secondary signs were present in 87.5% (35/40) of cases. Notably, 15 cases (37.5%) were misdiagnosed or missed. Surgical resection specimens demonstrated common pathological features, including large duct-like cysts and coagulative necrosis.

CONCLUSION

Atypical PDAC with cystic lesions is a relatively uncommon variant that exhibits a range of DCE-CT features, along with distinct pathological characteristics. Familiarity with these imaging features is essential for radiologists in order to minimize the risk of misdiagnosis and guide appropriate clinical management of these challenging cases.

Prediction of peripheral lymph node metastasis (LNM) in thyroid cancer using delta radiomics derived from enhanced CT combined with multiple machine learning algorithms

OBJECTIVES

This study aimed to develop a model for predicting peripheral lymph node metastasis (LNM) in thyroid cancer patients by combining enhanced CT radiomic features with machine learning algorithms. It increased the clinical utility and interpretability of the predictions through SHAP (SHapley Additive exPlanation) values and nomograms for model explanation and visualization.

METHODS

Clinical and enhanced CT image data from 375 patients with thyroid cancer confirmed by postoperative pathology at Xiangyang No. 1 People's Hospital were collected from January 2015 to July 2023. Among them, there were 88 patients in the LNM group and 287 patients in the non-LNM group. The delta radiomic features of the tumours were extracted. Various machine learning algorithms (such as SVM, GBM, RF, XGBoost, KNN, and LightGBM) were trained on the clinical and radiomic feature data sets and used to construct a reliable prediction model. During model training, cross-validation was used to evaluate model performance, and the optimal model was selected. In addition, SHAP values were used to interpret the prediction results of the optimal model, analyse the contribution of each feature to the prediction results, and further develop a nomogram to visually display the prediction results.

RESULTS

Univariate analysis confirmed that sex, Hashimoto's disease, tumour adjacency to the thyroid capsule, pathological subtype, Delta Radscore, and Radscore 1 are risk factors for peripheral lymph node metastasis in thyroid cancer patients. The machine learning model based on enhanced CT radiomics performed well in predicting peripheral lymph node metastasis in thyroid cancer patients. In the test set, the optimal model, SVM, achieved high AUC (0.879), sensitivity (0.849), and specificity (0.769) values. Through SHAP value analysis, the importance and contribution of tumour adjacency to the thyroid capsule, pathological subtype, Delta Radscore, and Radscore 1 in the prediction were clarified, providing a more detailed and intuitive basis for clinical decision-making. The nomogram illustrated the model prediction process, facilitating understanding and application by clinicians.

CONCLUSIONS

This study successfully constructed a model for predicting peripheral lymph node metastasis in thyroid cancer patients on the basis of enhanced CT radiomics combined with machine learning and improved the interpretability and clinical utility of the model through SHAP values and nomograms. The model not only improves the accuracy of predictions but also provides a more scientific and intuitive basis for clinical decision-making, with potential clinical application value.

Advances in multi-omics studies of microvascular invasion in hepatocellular carcinoma

Microvascular invasion (MVI) represents a pivotal independent prognostic factor for the recurrence of hepatocellular carcinoma (HCC) after surgery. It contributes to early intervention for potentially recurrent HCC to enhance patient outcomes and increase survival rates. Traditionally, the diagnosis of MVI has relied on postoperative pathological analysis, and accurate preoperative detection methodologies are lacking. Recent research suggests that multi-omics strategies play a role in definitively diagnosing MVI before surgery and offering personalized selection for clinical decision-making in HCC management. This review meticulously examines a multi-omics approach for the preoperative prediction of MVI in HCC patients, aiming to innovate diagnostic paradigms to anticipate postsurgical recurrence, thereby facilitating earlier and more personalized therapeutic strategies.

A universal model for predicting coronary artery lesions in subgroups of kawasaki disease in China: based on cluster analysis

Objective

Coronary artery lesions (CAL) represent the most severe complication of Kawasaki disease (KD). Currently, there is no standardized method for predicting CAL in KD, and the predictive effectiveness varies among different KD patients. Therefore, our study aims to establish distinct predictive models for CAL complications based on the characteristics of different clusters.

Methods

We employed principal component clustering analysis to categorize 1,795 KD patients into different clustered subgroups. We summarized the characteristics of each cluster and compared the occurrence of CAL components within each cluster. Additionally, we utilized LASSO analysis to further screen for factors associated with CAL. We then constructed CAL predictive models for each subgroup using the selected factors and conducted preliminary validation and assessment.

Results

Through PCA analysis, we identified three clusters in KD. We developed predictive models for each of the three clusters. The AUCs of the three predictive models were 0.789 (95% CI: 0.732-0.845), 0.894 (95% CI: 0.856-0.932), and 0.773 (95% CI: 0.727-0.819), respectively, all demonstrating good predictive performance.

Conclusion

Our study identified the existence of three clusters among KD patients. We developed KD-related CAL predictive models with good predictive performance for each cluster with distinct characteristics. This provides reference for individualized precision treatment of KD patients and aids in the health management of coronary arteries in KD.

PAD4 Inhibitor-Loaded Magnetic Fe3O4 Nanoparticles for Magnetic Targeted Chemotherapy and Magnetic Resonance Imaging of Lung Cancer

Introduction

Lung cancer is a major health concern worldwide owing to its high incidence and mortality rates. Therefore, identification of new therapeutic targets and strategies for lung cancer is critical for improving patient outcomes. Peptidyl arginine deiminase 4 (PAD4) promotes tumor growth and metastasis by catalyzing the citrullination of histones, making it a potential therapeutic target. Although PAD4 inhibitors have shown potential in the treatment of a variety of tumors, existing PAD4 inhibitors lack sufficient specificity and cause substantial systemic adverse reactions. To overcome these challenges, we developed novel YW403@Fe3O4-oxidized carboxymethyl chitosan (OCMC) magnetic nanoparticles (MNPs) that enabled magnetically targeted drug delivery by binding the PAD4 inhibitor YW403 to a ferric oxide magnetic carrier.

Methods

In vitro experiments were conducted using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays, Transwell assays, and flow cytometry to evaluate the activity of the MNPs. In vivo experiments involved magnetic resonance imaging (MRI) assessments and inductively coupled plasma mass spectrometry (ICP-MS) analyses to confirm the tumor targeting and iron metabolism of MNPs. Additionally, immunofluorescence staining was employed to further validate the expression of citrullinated histone H3 (H3cit).

Results

The implementation of this approach enhanced the targeting efficiency of PAD4 inhibitors, consequently reducing the required dosage of chemotherapy and potentially facilitating MRI monitoring. In vitro experiments demonstrated that MNPs exhibited superior activity compared to free drugs when subjected to an applied magnetic field, due to increased uptake of MNPs by tumor cells. In vivo experiments revealed that the application of magnetic fields significantly improved the tumor targeting of MNPs without impacting iron metabolism. By suppressing the expression of citrullinated histone (H3cit), MNPs effectively inhibited tumor growth and metastasis.

Discussion

These findings provide new research ideas for the development of novel anti-tumor nanomaterials and are expected to yield breakthroughs in the treatment of lung cancer.

Integrating OMICS-based platforms and analytical tools for diagnosis and management of pancreatic cancer: a review

Cancer remains the second leading cause of death worldwide, surpassed only by cardiovascular disease. From the different types of cancer, pancreatic cancer (PaC) has one of the lowest survival rates, with a survival rate of about 20% after the first year of diagnosis and about 8% after 5 years. The lack of highly sensitive and specific biomarkers, together with the absence of symptoms in the early stages, determines a late diagnosis, which is associated with a decrease in the effectiveness of medical intervention, regardless of its nature - surgery and/or chemotherapy. This review provides an updated overview of recent studies combining multi-OMICs approaches (e.g., proteomics, metabolomics) with analytical tools, highlighting the synergy between high-throughput molecular data generation and precise analytical tools such as LC-MS, GC-MS and MALDI-TOF MS. This combination significantly improves the detection, quantification and identification of biomolecules in complex biological systems and represents the latest advances in understanding PaC management and the search for effective diagnostic tools. Large-scale data analysis coupled with bioinformatics tools enables the identification of specific genetic mutations, gene expression patterns, pathways, networks, protein modifications and metabolic signatures associated with PaC pathogenesis, progression and treatment response through the integration of multi-OMICs data.

CAT and CXCL8 are crucial cofactors for the progression of nonalcoholic steatohepatitis to hepatocellular carcinoma, the immune infiltration and prognosis of hepatocellular carcinoma

PURPOSE

Hepatocellular carcinoma (HCC) is a malignant tumour characterized by high morbidity and mortality. Immunotherapy is an important treatment newly approved for the treatment for advanced hepatocellular carcinoma. However, how NASH progresses to HCC and the association between the immune signature in HCC and patient prognosis remain unclear.

METHODS

Data from NASH and NASH-HCC patients were obtained from the GEO database. Differentially expressed genes were screened and hub genes were identified. The enrichment analysis, clustering, cibersort, ssGSEA, Xcell and immune checkpoint expression data of the samples were analysed. Survival analysis of dual genes was performed using TCGA liver cancer samples and the lasso regression model, and Cox regression analysis was conducted. Pathology specimens from 21 NASH-associated hepatocellular carcinoma patients were collected, and immunohistochemical staining was used to verify gene expression.

RESULTS

Compared with HCC patients with high CAT and low CXCL8 expression, those with low CAT and high CXCL8 expression had significantly higher levels of infiltration of multiple immune cell types and the common immune checkpoints CD274, PDCD1 and CTLA4. Furthermore, CAT was a protective factor, and CXCL8 was a risk factor for the prognosis of HCC patients.

CONCLUSION

CAT and CXCL8 might impact NASH-HCC progression. HCC patients with low CAT and high CXCL8 expression might have more extensive immune cell infiltration and stronger tumour immune escape. However, probably due to their different effects on CD8 + T cells and reactive oxygen species, increased expression of CAT contributes to improved prognosis in HCC patients, whereas increased expression of CXCL8 leads to a poor prognosis.

A preliminary investigation into the activity and toxicity of an amyloid-based Emodin formulation

Emodin is a natural plant derivative with many therapeutic properties including anti-cancer, anti-apoptosis, and anti-inflammatory effects. However, the delivery of Emodin is quite challenging due to its superhydrophobic properties. Furthermore, conventional systemic delivery approaches often result in side effects. Thus, alternative strategies are important for the successful delivery of Emodin. The goal of this study was to develop a novel Emodin drug depot utilizing peptide amyloids. For the peptides, an aggregation-prone amino acid domain of receptor-interacting serine/threonine-protein kinase 3 (RIP3) protein was used. The RIP3/Emodin amyloid aggregates physicochemical characterization, cellular uptake, effects on toxicity, oxidative stress, and inflammation were investigated. Studies reveal that Emodin-encapsulated RIP3 peptide amyloid aggregates were able to induce significant lung cancer cell toxicity compared to free Emodin. Further, aggregates alone did not exhibit toxicity and or oxidative stress. In addition, the formulation was able to inhibit lipopolysaccharide (LPS) mediated inflammation in macrophage cells. Overall, the studies indicate the potential of RIP3 peptide amyloids as hydrophobic drug depots.

The value of a nomogram model based on CT imaging features in differentiating duodenal gastrointestinal stromal tumors from pancreatic head neuroendocrine tumors

OBJECTIVE

To construct a nomogram model based on multi-slice spiral CT imaging features to predict and differentiate between duodenal gastrointestinal stromal tumors (GISTs) and pancreatic head neuroendocrine tumors (NENs), providing imaging evidence for clinical treatment decisions.

METHODS

A retrospective collection of clinical information, pathological results, and imaging data was conducted on 115 cases of duodenal GISTs and 76 cases of pancreatic head NENs confirmed by surgical pathology at Zhongshan Hospital Fudan University from November 2013 to November 2022. Comparative analysis was performed on the tumor's maximum diameter, shortest diameter, long diameter/short diameter ratio, tumor morphology, tumor border, central position of the lesion, lesion long-axis direction, the relationship between tumor and common bile duct (CBD), duodenal side ulceration of the lesion, calcification, cystic and solid proportion within the tumor, thickened feeding arteries, tumor neovascularization, distant metastasis, and CT values during plain and enhanced scans in arterial and venous phases. Statistical analysis was conducted using t-tests, Mann-Whitney U tests, and χ2 tests. Univariate and multivariate logistic regression analyses were used to identify independent predictors for differentiating duodenal GISTs from pancreatic head NENs. Based on these independent predictors, a nomogram model was constructed, and the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. The nomogram was validated using a calibration curve, and decision curve analysis was applied to assess the clinical application value of the nomogram.

RESULTS

There were significant differences in the duodenal GISTs group and the pancreatic head NENs group in terms of longest diameter (P < 0.001), shortest diameter (P < 0.001), plain CT value (P < 0.001), arterial phase CT value (P < 0.001), venous phase CT value (P = 0.002), lesion long-axis direction (P < 0.001), central position of the lesion (P < 0.001), the relationship between tumor and CBD(< 0.001), border (P = 0.004), calcification (P = 0.017), and distant metastasis (P = 0.018). Multivariate logistic regression analysis identified uncertain location (OR 0.040, 95% CI 0.003-0.549), near the duodenum (OR 0, 95% CI 0-0.009), with the lesion long-axis direction along the pancreas as a reference, along the duodenum (OR 0.106, 95% CI 0.010-1.156) or no significant difference (OR 4.946, 95% CI 0.453-54.017), and the relationship between tumor and CBD (OR 0.013, 95% CI 0.001-0.180), shortest diameter (OR 0.705, 95% CI 0.546-0.909), and calcification (OR 18.638, 95% CI 1.316-263.878) as independent risk factors for differentiating between duodenal GISTs and pancreatic head NENs (all P values < 0.05). The combined diagnostic model's AUC values based on central position of the lesion, calcification, lesion long axis orientation, the relationship between tumor and CBD, shortest diameter, and the joint diagnostic model were 0.937 (0.902-0.972), 0.700(0.624-0.776), 0.717(0.631-0.802), 0.559 (0.473-0.644), 0.680 (0.603-0.758), and 0.991(0.982-0.999), respectively, with a sensitivity of 97.3% and a specificity of 93.0% for the joint diagnostic model. The nomogram model's AUC value was 0.985(0.973-0.996), with a sensitivity and specificity of 94.7% and 93.9%, respectively. The calibration curve indicated good agreement between predicted and actual risks. Decision curve analysis verified the clinical application value of the nomogram.

CONCLUSION

The nomogram model based on CT imaging features effectively differentiates between duodenal GISTs and pancreatic head NENs, aiding in more precise clinical treatment decisions.

Diagnostic Accuracy of Radiomics in the Early Detection of Pancreatic Cancer: A Systematic Review and Qualitative Assessment Using the Methodological Radiomics Score (METRICS)

BACKGROUND/OBJECTIVES

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal malignancy with increasing incidence and low survival rate, primarily due to the late detection of the disease. Radiomics has demonstrated its utility in recognizing patterns and anomalies not perceptible to the human eye. This systematic literature review aims to assess the application of radiomics in the analysis of pancreatic parenchyma images to identify early indicators predictive of PDAC.

METHODS

A systematic search of original research papers was performed on three databases: PubMed, Embase, and Scopus. Two reviewers applied the inclusion and exclusion criteria, and one expert solved conflicts for selecting the articles. After extraction and analysis of the data, there was a quality assessment of these articles using the Methodological Radiomics Score (METRICS) tool. The METRICS assessment was carried out by two raters, and conflicts were solved by a third reviewer.

RESULTS

Ten articles for analysis were retrieved. CT scan was the diagnostic imaging used in all the articles. All the studies were retrospective and published between 2019 and 2024. The main objective of the articles was to generate radiomics-based machine learning models able to differentiate pancreatic tumors from healthy tissue. The reported diagnostic performance of the model chosen yielded very high results, with a diagnostic accuracy between 86.5% and 99.2%. Texture and shape features were the most frequently implemented. The METRICS scoring assessment demonstrated that three articles obtained a moderate quality, five a good quality, and, finally, two articles yielded excellent quality. The lack of external validation and available model, code, and data were the major limitations according to the qualitative assessment.

CONCLUSIONS

There is high heterogeneity in the research question regarding radiomics and pancreatic cancer. The principal limitations of the studies were mainly due to the nature of the trials and the considerable heterogeneity of the radiomic features reported. Nonetheless, the work in this field is promising, and further studies are still required to adopt radiomics in the early detection of PDAC.

CT Imaging of the Pancreas: A Review of Current Developments and Applications

Pancreatic cancer continues to pose daily challenges to clinicians, radiologists, and researchers. These challenges are encountered at each stage of pancreatic cancer management, including early detection, definite characterization, accurate assessment of tumour burden, preoperative planning when surgical resection is possible, prediction of tumour aggressiveness, response to treatment, and detection of recurrence. CT imaging of the pancreas has made major advances in recent years through innovations in research and clinical practice. Technical advances in CT imaging, often in combination with imaging biomarkers, hold considerable promise in addressing such challenges. Ongoing research in dual-energy and spectral photon-counting computed tomography, new applications of artificial intelligence and image rendering have led to innovative implementations that allow now a more precise diagnosis of pancreatic cancer and other diseases affecting this organ. This article aims to explore the major research initiatives and technological advances that are shaping the landscape of CT imaging of the pancreas. By highlighting key contributions in diagnostic imaging, artificial intelligence, and image rendering, this article provides a comprehensive overview of how these innovations are enhancing diagnostic precision and improving outcome in patients with pancreatic diseases.

Machine Learning-Driven Radiomics Analysis for Distinguishing Mucinous and Non-Mucinous Pancreatic Cystic Lesions: A Multicentric Study

The increasing use of high-resolution cross-sectional imaging has significantly enhanced the detection of pancreatic cystic lesions (PCLs), including pseudocysts and neoplastic entities such as IPMN, MCN, and SCN. However, accurate categorization of PCLs remains a challenge. This study aims to improve PCL evaluation by developing and validating a radiomics-based software tool leveraging machine learning (ML) for lesion classification. The model categorizes PCLs into mucinous and non-mucinous types using a custom dataset of 261 CT examinations, with 156 images for training and 105 for external validation. Three experienced radiologists manually delineated the images, extracting 38 radiological and 214 radiomic features using the Pyradiomics module in Python 3.13.2. Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by classification with an Adaptive Boosting (AdaBoost) model trained on the optimized feature set. The proposed model achieved an accuracy of 89.3% in the internal validation cohort and demonstrated robust performance in the external validation cohort, with 90.2% sensitivity, 80% specificity, and 88.2% overall accuracy. Comparative analysis with existing radiomics-based studies showed that the proposed model either outperforms or performs on par with the current state-of-the-art methods, particularly in external validation scenarios. These findings highlight the potential of radiomics-driven machine learning approaches in enhancing PCL diagnosis across diverse patient populations.

Surface Bi-vacancy and corona polarization engineered nanosheets with sonopiezocatalytic antibacterial activity for wound healing

Piezocatalytic therapy is an emerging therapeutic strategy for eradicating drug-resistant bacteria, but suffers from insufficient piezoelectricity and catalytic active site availability. Herein, Bi-vacancies (BiV) and corona polarization were introduced to BiOBr nanosheets to create a BiOBr-BiVP nanoplatform for piezocatalytic antibacterial therapy. This meticulously tailored strategy strengthens the built-in electric field of nanosheets, enhancing piezoelectric potential and charge density and boosting charge separation and migration efficiency. Meanwhile, BiV adeptly adjust the band structure and increase reaction sites. Ultrasonication of nanosheets continuously enables the generation of reactive oxygen species (ROS) and CO, facilitating almost 100% broad-spectrum antibacterial efficacy. BiOBr-BiVP nanosheets demonstrate full bacterial eradication and accelerate wound healing through simultaneous regulation of inflammatory factors, facilitation of collagen deposition, and promotion of angiogenesis. Overall, this ultrasonic-triggered piezocatalytic nanoplatform combines BiV and the corona polarization strategy, providing a robust strategy for amplifying piezocatalytic mediated ROS/CO generation for drug-resistant bacterial eradication.

Nano-Based Strategies Aiming at Tumor Microenvironment for Improved Cancer Therapy

Malignant tumors pose a considerable threat to human life and health. Traditional treatments, such as radiotherapy and chemotherapy, often lack specificity, leading to collateral damage to normal tissues. Tumor microenvironment (TME) is characterized by hypoxia, acidity, redox imbalances, and elevated ATP levels factors that collectively promote tumor growth and metastasis. This review provides a comprehensive overview of the nanoparticles developed in recent years for TME-responsive strategies or TME-modulating methods for tumor therapy. The TME-responsive strategies focus on designing and synthesizing nanoparticles that can interact with the tumor microenvironment to achieve precisely controlled drug release. These nanoparticles activate drug release under specific conditions within the tumor environment, thereby enhancing the efficacy of the drugs while reducing toxicity to normal cells. Moreover, simply eliminating tumor cells does not fundamentally solve the problem. Only by comprehensively regulating the TME to make it unsuitable for tumor cell survival and proliferation can we achieve more thorough therapeutic effects and reduce the risk of tumor recurrence. TME regulation strategies aim to suppress the growth and metastasis of tumor cells by modulating various components within the TME. These strategies not only improve treatment outcomes but also have the potential to lay the foundation for future personalized cancer therapies.

Artificial Intelligence in Pancreatic Imaging: A Systematic Review

The rising incidence of pancreatic diseases, including acute and chronic pancreatitis and various pancreatic neoplasms, poses a significant global health challenge. Pancreatic ductal adenocarcinoma (PDAC) for example, has a high mortality rate due to late-stage diagnosis and its inaccessible location. Advances in imaging technologies, though improving diagnostic capabilities, still necessitate biopsy confirmation. Artificial intelligence, particularly machine learning and deep learning, has emerged as a revolutionary force in healthcare, enhancing diagnostic precision and personalizing treatment. This narrative review explores Artificial intelligence's role in pancreatic imaging, its technological advancements, clinical applications, and associated challenges. Following the PRISMA-DTA guidelines, a comprehensive search of databases including PubMed, Scopus, and Cochrane Library was conducted, focusing on Artificial intelligence, machine learning, deep learning, and radiomics in pancreatic imaging. Articles involving human subjects, written in English, and published up to March 31, 2024, were included. The review process involved title and abstract screening, followed by full-text review and refinement based on relevance and novelty. Recent Artificial intelligence advancements have shown promise in detecting and diagnosing pancreatic diseases. Deep learning techniques, particularly convolutional neural networks (CNNs), have been effective in detecting and segmenting pancreatic tissues as well as differentiating between benign and malignant lesions. Deep learning algorithms have also been used to predict survival time, recurrence risk, and therapy response in pancreatic cancer patients. Radiomics approaches, extracting quantitative features from imaging modalities such as CT, MRI, and endoscopic ultrasound, have enhanced the accuracy of these deep learning models. Despite the potential of Artificial intelligence in pancreatic imaging, challenges such as legal and ethical considerations, algorithm transparency, and data security remain. This review underscores the transformative potential of Artificial intelligence in enhancing the diagnosis and treatment of pancreatic diseases, ultimately aiming to improve patient outcomes and survival rates.

An imaging-based model to predict the malignant potential of intraductal papillary mucinous neoplasm of the pancreas

OBJECTIVES

To develop and validate imaging-based models for predicting the malignancy risk of intraductal papillary mucinous neoplasm (IPMN).

MATERIALS AND METHODS

We retrospectively analyzed data from 241 IPMN patients who underwent preoperative CT and MRI for model development. Cyst size, presence and size of the enhancing mural nodule (EMN), main pancreatic duct (MPD) diameter, thickened/enhancing cyst wall, abrupt MPD caliber change with distal atrophy, and lymphadenopathy were assessed. Multiple logistic regression models predicting malignancy risk were created using either continuous (Model C) or dichotomized variables (Model D) using these imaging features. Validation included internal (n = 55) and external (n = 43) datasets. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and compared with that of the international guideline-based model (Model F).

RESULTS

Model C identified age, EMN size, MPD diameter, and lymphadenopathy as independent predictors on CT, and age and presence and size of EMN on MRI. Model D identified age ≥ 68, cyst size ≥ 31 mm, EMN ≥ 6 mm, MPD ≥ 7 mm, and lymphadenopathy as independent predictors on CT, and age ≥ 68, EMN ≥ 4.5 mm, and lymphadenopathy on MRI. Model C (AUC, 0.763-0.899) performed slightly better than Model D (AUC, 0.753-0.912) without statistical significance. No significant difference was observed between Models C and F (AUC, 0.729-0.952). Combining Model C with obstructive jaundice improved performance (AUC, 0.802-0.941) without statistical significance.

CONCLUSION

Our imaging-based models effectively predicted the malignancy risk of IPMNs, comparable to international consensus guidelines.

CLINICAL RELEVANCE STATEMENT

Imaging features are important for predicting the malignant potential of IPMNs. Our imaging-based model may help determine surgical candidacy for patients with IPMNs.

KEY POINTS

Non-invasively determining the malignant potential of intraductal papillary mucinous neoplasms (IPMNs) allows for appropriate treatment decision-making We identified multiple imaging features that are associated with malignant transformation and developed models for this prediction. Our model performs comparably with international consensus guidelines in predicting the malignant potential of IPMNs.

Advanced Nanomaterials for Cancer Therapy: Gold, Silver, and Iron Oxide Nanoparticles in Oncological Applications

Cancer remains one of the most challenging health issues globally, demanding innovative therapeutic approaches for effective treatment. Nanoparticles, particularly those composed of gold, silver, and iron oxide, have emerged as promising candidates for changing cancer therapy. This comprehensive review demonstrates the landscape of nanoparticle-based oncological interventions, focusing on the remarkable advancements and therapeutic potentials of gold, silver, and iron oxide nanoparticles. Gold nanoparticles have garnered significant attention for their exceptional biocompatibility, tunable surface chemistry, and distinctive optical properties, rendering them ideal candidates for various cancer diagnostic and therapeutic strategies. Silver nanoparticles, renowned for their antimicrobial properties, exhibit remarkable potential in cancer therapy through multiple mechanisms, including apoptosis induction, angiogenesis inhibition, and drug delivery enhancement. With their magnetic properties and biocompatibility, iron oxide nanoparticles offer unique cancer diagnosis and targeted therapy opportunities. This review critically examines the recent advancements in the synthesis, functionalization, and biomedical applications of these nanoparticles in cancer therapy. Moreover, the challenges are discussed, including toxicity concerns, immunogenicity, and translational barriers, and ongoing efforts to overcome these hurdles are highlighted. Finally, insights into the future directions of nanoparticle-based cancer therapy and regulatory considerations, are provided aiming to accelerate the translation of these promising technologies from bench to bedside.

Adrenic acid: A promising biomarker and therapeutic target (Review)

Adrenic acid is a 22‑carbon unsaturated fatty acid that is widely present in the adrenal gland, liver, brain, kidney and vascular system that plays a regulatory role in various pathophysiological processes, such as inflammatory reactions, lipid metabolism, oxidative stress, vascular function, and cell death. Adrenic acid is a potential biomarker for various ailments, including metabolic, neurodegenerative and cardiovascular diseases and cancer. In addition, adrenic acid is influenced by the pharmacological properties of several natural products, such as astragaloside IV, evodiamine, quercetin, kaempferol, Berberine‑baicalin and prebiotics, so it is a promising new target for clinical treatment and drug development. However, the molecular mechanisms by which adrenic acid exerts are unclear. The present study systematically reviewed the biosynthesis and metabolism of adrenic acid, focusing on intrinsic mechanisms that influence the progression of metabolic, cardiovascular and neurological disease. These mechanisms regulate several key processes, including immuno‑inflammatory response, oxidative stress, vascular function and cell death. In addition, the present study explored the potential clinical translational value of adrenic acid as a biomarker and therapeutic target. To the best of our knowledge, the present study is first systematic summary of the mechanisms of action of adrenic acid across a range of diseases. The present study provides understanding of the wide range of metabolic activities of adrenic acid and a basis for further exploring the pathogenesis and therapeutic targets of various diseases.

Global metabolomic profiling of tumor tissue and paired serum samples to identify biomarkers for response to neoadjuvant FOLFIRINOX treatment of human pancreatic cancer

Neoadjuvant chemotherapy (NAT) is increasingly used for the treatment of non-metastatic pancreatic ductal adenocarcinoma (PDAC) and is established as a standard of care for borderline resectable and locally advanced PDAC. However, full exploitation of its clinical benefits is limited by the lack of biomarkers that assess treatment response. To address this unmet need, global metabolomic profiling was performed on tumor tissue and paired serum samples from patients with treatment-naïve (TN; n = 18) and neoadjuvant leucovorin calcium (folinic acid), fluorouracil, irinotecan hydrochloride and oxaliplatin (FOLFIRINOX)-treated (NAT; n = 17) PDAC using liquid chromatography mass spectrometry. Differentially abundant metabolites (DAMs) in TN versus NAT groups were identified and their correlation with various clinical parameters was assessed. Metabolomics profiling identified 40 tissue and five serum DAMs in TN versus NAT PDAC. In general, DAMs associated with amino acid and nucleotide metabolism were lower in NAT compared to TN. Four DAMs-3-hydroxybutyric acid (BHB), 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF), glycochenodeoxycholate and citrulline-were common to both tissue and serum and showed a similar pattern of differential abundance in both groups. A strong positive correlation was observed between serum carbohydrate 19-9 antigen (CA 19-9) and tissue carnitines (C12, C18, C18:2) and N8-acetylspermidine. The reduction in CA 19-9 following NAT correlated negatively with serum deoxycholate levels, and the latter correlated positively with survival. This study revealed neoadjuvant-chemotherapy-induced changes in metabolic pathways in PDAC, mainly amino acid and nucleotide metabolism, and these correlated with reduced CA 19-9 following neoadjuvant FOLFIRINOX treatment.

Qianyang Yuyin granules alleviate hypertension-induced vascular remodeling by inhibiting the phenotypic switch of vascular smooth muscle cells

ETHNOPHARMACOLOGICAL RELEVANCE

Qianyang Yuyin granules (QYYY) have been used clinically to treat hypertension for over two decades. Previous clinical trials have shown that QYYY can improve vascular elastic function in hypertensive patients. However, the underlying pharmacological mechanism is unclear.

AIM OF THE STUDY

To elucidate the effects and mechanisms of QYYY on vascular remodeling using a multidisciplinary approach that includes network pharmacology, proteomics, and both in vitro and in vivo experiments.

MATERIALS AND METHODS

The main components of QYYY were identified using ultra-high-performance liquid chromatography and high-resolution mass spectrometry. Network pharmacology and molecular docking were employed to predict QYYY's primary active ingredients, potential therapeutic targets and intervention pathways in hypertensive vascular remodeling. We induced hypertension in male C57BL/6 mice by infusing angiotensin II (Ang II) via osmotic minipumps, and performed pre-treatment with QYYY or Sacubitril/valsartan (Entresto). Blood pressure was monitored in vivo, followed by the extraction of aortas to examine pathological structural changes and alterations in protein expression patterns. The expression and location of proteins involved in the HIF-1α/TWIST1/P-p65 signaling pathway were investigated, as well as markers of vascular smooth muscle cells (VSMCs) phenotypic switch. In vitro, we studied the effects of QYYY water extract on Ang II-stimulated human aortic VSMCs. We investigated whether QYYY could affect the HIF-1α/TWIST1/P-p65 signaling pathway, thereby ameliorating apoptosis, autophagy, and phenotype switch in VSMCs.

RESULTS

We identified 62 main compounds in QYYY, combined with network pharmacology, speculated 827 potentially active substances, and explored 1021 therapeutic targets. The KEGG pathway analysis revealed that the mechanisms of action associated with QYYY therapy potentially encompass various biological processes, including metabolic pathways, TNF signaling pathways, apoptosis, Ras signaling pathways, HIF-1 signaling pathways, autophagy-animal pathways. In hypertensive mice, QYYY restored abnormally elevated blood pressure, vascular remodeling, and inflammation with a dose-response relationship while altering abnormal protein patterns. In vitro, QYYY could inhibit abnormal proliferation, migration, intracellular Ca2+ accumulation and cytoskeletal changes of VSMCs. It improved mitochondrial function, reduced ROS levels, stabilized membrane potential, prevented cell death, and reduced overproduction of TGF-β1, TNF-a, and IL-1β.

CONCLUSION

QYYY may be able to inhibit the overactivation of the HIF-1α/TWIST1/P-p65 signaling pathway, improve the phenotypic switch, and balance apoptosis and autophagy in VSMCs, thereby effectively improving vascular remodeling caused by hypertension.

Qian Yang Yu Yin Granule prevents hypertensive cardiac remodeling by inhibiting NLRP3 inflammasome activation via Nrf2

ETHNOPHARMACOLOGICAL RELEVANCE

Qian Yang Yu Yin Granule (QYYYG), a traditional Chinese poly-herbal formulation, has been validated in clinical trials to mitigate cardiac remodeling (CR), and cardiac damage in patients with hypertension. However, the specific mechanism remains unclear.

AIM OF THE STUDY

This study explored the potential effects and potential mechanisms of QYYYG on hypertensive CR by combining various experimental approaches.

MATERIALS AND METHODS

Spontaneously hypertensive rats (SHRs) were used as a model of hypertensive CR, followed by QYYYG interventions. Blood pressure, cardiac function and structure, histopathological changes, and myocardial inflammation and oxidative stress were tested to assess the efficacy of QYYYG in SHRs. For in vitro experiments, a cell model of myocardial hypertrophy and injury was constructed with isoprenaline. Cardiomyocyte hypertrophy, oxidative stress, and death were examined after treatment with different concentrations of QYYYG, and transcriptomics analyses were performed to explore the underlying mechanism. Nrf2 and the ROS/NF-κB/NLRP3 inflammasome pathway were detected. Thereafter, ML385 and siRNAs were used to inhibit Nrf2 in cardiomyocytes, so as to verify whether QYYYG negatively regulates the NLRP3 inflammasome by targeting Nrf2, thereby ameliorating the associated phenotypes. Finally, high performance liquid chromatography (HPLC) was conducted to analyze the active ingredients in QYYYG, and molecular docking was utilized to preliminarily screen the compounds with modulatory effects on Nrf2 activities.

RESULTS

QYYYG improved blood pressure, cardiac function, and structural remodeling and attenuated myocardial inflammation, oxidative stress, and cell death in SHRs. The transcriptomics results showed that the inflammatory response might be crucial in pathological CR and that Nrf2, which potentially negatively regulates the process, was upregulated by QYYYG treatment. Furthermore, QYYYG indeed facilitated Nrf2 activation and negatively regulated the ROS/NF-κB/NLRP3 inflammasome pathway, therefore ameliorating the associated phenotypes. In vitro inhibition or knockdown of Nrf2 weakened or even reversed the repressive effect of QYYYG on ISO-induced inflammation, oxidative stress, pyroptosis, and the NLRP3 inflammasome activation. Based on the results of HPLC and molecular docking, 30 compounds, including cafestol, genistein, hesperetin, and formononetin, have binding sites to Keap1-Nrf2 protein and might affect the activity or stability of Nrf2.

CONCLUSION

In conclusion, the alleviatory effect of QYYYG on hypertensive CR is related to its regulation of Nrf2 activation. Specifically, QYYYG blocks the activation of the NLRP3 inflammasome by boosting Nrf2 signaling and depressing myocardial inflammation, oxidative stress, and pyroptosis, thereby effectively ameliorating hypertensive CR.

Identification of key regulators in pancreatic ductal adenocarcinoma using network theoretical approach

Pancreatic Ductal Adenocarcinoma (PDAC) is a devastating disease with poor clinical outcomes, which is mainly because of delayed disease detection, resistance to chemotherapy, and lack of specific targeted therapies. The disease's development involves complex interactions among immunological, genetic, and environmental factors, yet its molecular mechanism remains elusive. A major challenge in understanding PDAC etiology lies in unraveling the genetic profiling that governs the PDAC network. To address this, we examined the gene expression profile of PDAC and compared it with that of healthy controls, identifying differentially expressed genes (DEGs). These DEGs formed the basis for constructing the PDAC protein interaction network, and their network topological properties were calculated. It was found that the PDAC network self-organizes into a scale-free fractal state with weakly hierarchical organization. Newman and Girvan's algorithm (leading eigenvector (LEV) method) of community detection enumerated four communities leading to at least one motif defined by G (3,3). Our analysis revealed 33 key regulators were predominantly enriched in neuroactive ligand-receptor interaction, Cell adhesion molecules, Leukocyte transendothelial migration pathways; positive regulation of cell proliferation, positive regulation of protein kinase B signaling biological functions; G-protein beta-subunit binding, receptor binding molecular functions etc. Transcription Factor and mi-RNA of the key regulators were obtained. Recognizing the therapeutic potential and biomarker significance of PDAC Key regulators, we also identified approved drugs for specific genes. However, it is imperative to subject Key regulators to experimental validation to establish their efficacy in the context of PDAC.

A nomogram to preoperatively predict the aggressiveness of pancreatic neuroendocrine tumors based on CT features and 3D CT radiomic features

OBJECTIVES

Combining Computed Tomography (CT) intuitive anatomical features with Three-Dimensional (3D) CT multimodal radiomic imaging features to construct a model for assessing the aggressiveness of pancreatic neuroendocrine tumors (pNETs) prior to surgery.

METHODS

This study involved 242 patients, randomly assigned to training (170) and validation (72) cohorts. Preoperative CT and 3D CT radiomic features were used to develop a model predicting pNETs aggressiveness. The aggressiveness of pNETs was characterized by a combination of factors including G3 grade, nodal involvement (N + status), presence of distant metastases, and/or recurrence of the disease.

RESULTS

Three distinct predictive models were constructed to evaluate the aggressiveness of pNETs using CT features, 3D CT radiomic features, and their combination. The combined model demonstrated the greatest predictive accuracy and clinical applicability in both the training and validation sets (AUCs (95% CIs) = 0.93 (0.90-0.97) and 0.89 (0.79-0.98), respectively). Subsequently, a nomogram was developed using the features from the combined model, displaying strong alignment between actual observations and predictions as indicated by the calibration curves. Using a nomogram score of 86.06, patients were classified into high- and low-aggressiveness groups, with the high-aggressiveness group demonstrating poorer overall survival and shorter disease-free survival.

CONCLUSION

This study presents a combined model incorporating CT and 3D CT radiomic features, which accurately predicts the aggressiveness of PNETs preoperatively.

Current status and future prospects of molecular imaging in targeting the tumor immune microenvironment

Molecular imaging technologies have significantly transformed cancer research and clinical practice, offering valuable tools for visualizing and understanding the complex tumor immune microenvironment. These technologies allow for the non-invasive examination of key components within the tumor immune microenvironment, including immune cells, cytokines, and stromal cells, providing crucial insights into tumor biology and treatment responses. This paper reviews the latest advancements in molecular imaging, with a focus on its applications in assessing interactions within the tumor immune microenvironment. Additionally, the challenges faced by molecular imaging technologies are discussed, such as the need for highly sensitive and specific imaging agents, issues with data integration, and difficulties in clinical translation. The future outlook emphasizes the potential of molecular imaging to enhance personalized cancer treatment through the integration of artificial intelligence and the development of novel imaging probes. Addressing these challenges is essential to fully realizing the potential of molecular imaging in improving cancer diagnosis, treatment, and patient outcomes.

Construction of feature selection and efficacy prediction model for transformation therapy of locally advanced pancreatic cancer based on CT, 18F-FDG PET/CT, DNA mutation, and CA199

BACKGROUND

Immunotherapy and radiotherapy play crucial roles in the transformation therapy of locally advanced pancreatic cancer; however, the exploration of effective predictive biomarkers has been unsatisfactory. With the rapid development of radiomics, next-generation sequencing, and machine learning, there is hope to identify biomarkers that can predict the efficacy of transformative treatment for locally advanced pancreatic cancer through simple and non-invasive clinical methods. Our study focuses on using computed tomography (CT), positron emission tomography/computed tomography (PET/CT), gene mutations, and baseline carbohydrate antigen 199 (CA199) to identify biomarkers for predicting the efficacy of transformative treatment.

METHODS

We retrospectively collected data from 70 patients with locally advanced pancreatic cancer who had undergone a biopsy for pathological diagnosis. These patients had complete baseline enhanced CT images and baseline CA199 results. Among them, 65 patients had efficacy evaluation results after 4 treatment cycles, 54 patients had complete baseline PET/CT images, 51 patients had complete DNA mutation detection results, and 34 patients had both complete PET/CT images and DNA mutation detection results. Additionally, 47 patients had complete available CT images at baseline, after 2 treatment cycles, and after 4 treatment cycles. We extracted radiomic features from the original lesion-enhanced CT images (including baseline and subsequent follow-up CT scans), radiomic features from baseline 18F-fluoro-2-deoxy-2-D-glucose (18F-FDG) PET, and patient-specific features related to abdominal and visceral fat. We used short-term and long-term treatment efficacy as the prediction outcomes and performed statistical and machine learning-based feature selection and COX regression analysis to identify potentially predictive features. Subsequently, we separately or in combination modeled the CT features, PET features, baseline CA199, and gene mutation data to construct efficacy prediction models. Finally, we investigated the mixed effects model of the dynamic changes in CT features at baseline, after 2 treatment cycles, and after 4 treatment cycles on the prediction of short-term treatment efficacy.

RESULTS

We found that a combination of CT radiomic features, including F1_ gray level co-occurrence matrix (GLCM), F2_gray level run length matrix (GLRLM), F5_neighboring gray tone difference matrix (NGTDM), and F6_Shape, PET radiomic features such as visceral adipose tissue (VAT), tumor-to-liver ratio (T/L), standardized uptake value mean (SUVmean), and GLCM, as well as baseline CA199, can be used to predict short-term treatment efficacy. Baseline CA199, GLCM, IntensityDirect, Shape, and PET/CT features are independent factors for long-term treatment efficacy. In constructing the short-term treatment efficacy prediction model, ensemble learning methods such as adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and RandomForest performed the best. However, in terms of model interpretability, decision tree methods provide the most intuitive display of the predictive details of the model. For the time series data of patients' baseline CT, CT after 2 treatment cycles, and CT after 4 treatment cycles, long short-term memory (LSTM) modeling yielded better predictive models.

CONCLUSION

A multimodal combination of radiomics, DNA mutations, and baseline CA199 can predict the efficacy of transformative treatment in locally advanced pancreatic cancer. Various feature selection methods and multimodal fusion approaches contribute to guiding personalized and precise treatment for pancreatic cancer.

Advancing Radiobiology: Investigating the Effects of Photon, Proton, and Carbon-Ion Irradiation on PANC-1 Cells in 2D and 3D Tumor Models

Introduction: Pancreatic cancer (PC) is one of the most aggressive and lethal malignancies, calling for enhanced research. Pancreatic ductal adenocarcinoma (PDAC) represents 70-80% of all cases and is known for its resistance to conventional therapies. Carbon-ion radiotherapy (CIRT) has emerged as a promising approach due to its ability to deliver highly localized doses and unique radiobiological properties compared to X-rays. In vitro radiobiology has relied on two-dimensional (2D) cell culture models so far; however, these are not sufficient to replicate the complexity of the in vivo tumor architecture. Three-dimensional (3D) models become a paradigm shift, surpassing the constraints of traditional models by accurately re-creating morphological, histological, and genetic characteristics as well as the interaction of tumour cells with the microenvironment. Materials and Methods: This study investigates the survival of pancreatic cancer cells in both 2D and spheroids, a 3D model, following photon, proton, and carbon-ion irradiation by means of clonogenic, MTT, spheroid growth, and vitality assays. Results: Our results demonstrate that carbon ions are more efficient in reducing cancer cell survival compared to photons and protons. In 2D cultures, carbon-ion irradiation reduced cell survival to approximately 15%, compared to 45% with photons and 30% with protons. In the 3D culture model, spheroid growth was similarly inhibited by carbon-ion irradiation; however, the overall survival rates were higher across all irradiation modalities compared to the 2D cultures. Carbon ions consistently showed the highest efficacy in reducing cell viability in both models. Conclusions: Our research highlights the pivotal role of 3D models in unraveling the complexities of pancreatic cancer radiobiology, offering new avenues for designing more effective and precise treatment protocols.

Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors

OBJECTIVES

The objective is to develop and validate intratumoral and peritumoral ultrasomics models utilizing endoscopic ultrasonography (EUS) to predict pathological grading in pancreatic neuroendocrine tumors (PNETs).

METHODS

Eighty-one patients, including 51 with grade 1 PNETs and 30 with grade 2/3 PNETs, were included in this retrospective study after confirmation through pathological examination. The patients were randomly allocated to the training or test group in a 6:4 ratio. Univariate and multivariate logistic regression were used for screening clinical and ultrasonic characteristics. Ultrasomics is ultrasound-based radiomics. Ultrasomics features were extracted from both the intratumoral and peritumoral regions of conventional EUS images. Subsequently, the dimensionality of these radiomics features was reduced using the least absolute shrinkage and selection operator (LASSO) algorithm. A machine learning algorithm, namely multilayer perception (MLP), was employed to construct prediction models using only the nonzero coefficient features and retained clinical features, respectively.

RESULTS

One hundred seven ultrasomics features based on EUS were extracted, and only features with nonzero coefficients were ultimately retained. Among all the models, the combined ultrasomics model achieved the greatest performance, with an AUC of 0.858 (95% CI, 0.7512 - 0.9642) in the training group and 0.842 (95% CI, 0.7061 - 0.9785) in the test group. A calibration curve and a decision curve analysis (DCA) also demonstrated its accuracy and utility.

CONCLUSIONS

The integrated model using EUS ultrasomics features from intratumoral and peritumoral tumors accurately predicts PNETs' pathological grades pre-surgery, aiding personalized treatment planning.

TRIAL REGISTRATION

ChiCTR2400091906.

The known genetic variants of BRCA1, BRCA2 and NOD2 in pancreatitis and pancreatic cancer risk assessment

The single nucleotide polymorphism in NOD2 (rs2066847) is associated with conditions that may predispose to the development of gastrointestinal disorders, as well as the known BRCA1 and BRCA2 variants classified as risk factors in many cancers. In our study, we analyzed these variants in a group of patients with pancreatitis and pancreatic cancer to clarify their role in pancreatic disease development. The DNA was isolated from whole blood samples of 553 patients with pancreatitis, 83 patients with pancreatic cancer, 44 cases of other pancreatic diseases, and 116 healthy volunteers. The NOD2 (rs2066847), BRCA1 (rs80357914) and BRCA2 (rs276174813) were genotyped. The statistically significant 3-fold increased risk of pancreatic cancer was detected among the patients with rs2066847 polymorphism (OR = 2.77, p-value = 0.019). We did not find the studied polymorphisms in BRCA1 (rs80357914) and BRCA2 (rs276174813). However, the adjacent polymorphisms have been detected only in patients with pancreatic diseases. The studied variant in NOD2 occurs more frequently in pancreatic patients and significantly increases the risk of pancreatic cancer. It can be considered as a genetic risk factor that predisposes to cancer development. The analyzed regions in BRCA1 and BRCA2 may be a potential target in further search for a genetic marker of pancreatic diseases.