Elevating pancreatic cystic lesion stratification: Current and future pancreatic cancer biomarker(s)

Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery

Application of deep learning (DL) and machine learning (ML) is rapidly increasing in the medical field. DL is gaining significance for medical image analysis, particularly, in oral and maxillofacial surgeries. Owing to the ability to accurately identify and categorize both diseased and normal soft- and hard-tissue structures, DL has high application potential in the diagnosis and treatment of tumors and in orthognathic surgeries. Moreover, DL and ML can be used to develop prediction models that can aid surgeons to assess prognosis by analyzing the patient's medical history, imaging data, and surgical records, develop more effective treatment strategies, select appropriate surgical modalities, and evaluate the risk of postoperative complications. Such prediction models can play a crucial role in the selection of treatment strategies for oral and maxillofacial surgeries. Their practical application can improve the utilization of medical staff, increase the treatment accuracy and efficiency, reduce surgical risks, and provide an enhanced treatment experience to patients. However, DL and ML face limitations, such as data drift, unstable model results, and vulnerable social trust. With the advancement of social concepts and technologies, the use of these models in oral and maxillofacial surgery is anticipated to become more comprehensive and extensive.

Revolutionizing the Pancreatic Tumor Diagnosis: Emerging Trends in Imaging Technologies: A Systematic Review

Background and Objectives: The pancreas, ensconced within the abdominal cavity, requires a plethora of sophisticated imaging modalities for its comprehensive evaluation, with ultrasonography serving as a primary investigative technique. A myriad of pancreatic pathologies, encompassing pancreatic neoplasia and a spectrum of inflammatory diseases, are detectable through these imaging strategies. Nevertheless, the intricate anatomical confluence and the pancreas's deep-seated topography render the visualization and accurate diagnosis of its pathologies a formidable endeavor. The objective of our paper is to review the best diagnostic imagistic tools for the pancreas. Materials and Methods: we have gathered several articles using Prisma guidelines to determine the best imagistic methods. The imperative of pancreatic scanning transcends its diagnostic utility, proving to be a pivotal element in a multitude of clinical specialties, notably surgical oncology. Within this domain, multidetector computed tomography (MDCT) of the pancreas holds the distinction of being the paramount imaging modality, endorsed for its unrivaled capacity to delineate the staging and progression of pancreatic carcinoma. In synergy with MDCT, there has been a notable advent of avant-garde imaging techniques in recent years. These advanced methodologies, including ultrasonography, endoscopic ultrasonography, contrast-enhanced ultrasonography, and magnetic resonance imaging (MRI) conjoined with magnetic resonance cholangiopancreatography (MRCP), have broadened the horizon of tumor characterization, offering unparalleled depth and precision in oncological assessment. Other emerging diagnostic techniques, such as elastography, also hold a lot of potential and promise for the future of pancreatic imaging. Fine needle aspiration (FNA) is a quick, minimally invasive procedure to evaluate lumps using a thin needle to extract tissue for analysis. It is less invasive than surgical biopsies and usually performed as an outpatient with quick recovery. Its accuracy depends on sample quality, and the risks include minimal bleeding or discomfort. Results, guiding further treatment, are typically available within a week. Elastography is a non-invasive medical imaging technique that maps the elastic properties and stiffness of soft tissue. This method, often used in conjunction with ultrasound or MRI, helps differentiate between hard and soft areas in tissue, providing valuable diagnostic information. It is particularly useful for assessing liver fibrosis, thyroid nodules, breast lumps, and musculoskeletal conditions. The technique is painless and involves applying gentle pressure to the area being examined. The resulting images show tissue stiffness, indicating potential abnormalities. Elastography is advantageous for its ability to detect diseases in early stages and monitor treatment effectiveness. The procedure is quick, safe, and requires no special preparation, with results typically available immediately. Results: The assembled and gathered data shows the efficacy of various techniques in discerning the nature and extent of neoplastic lesions within the pancreas. Conclusions: The most common imaging modalities currently used in diagnosing pancreatic neoplasms are multidetector computed tomography (MDCT), endoscopic ultrasound (EUS), and magnetic resonance imaging (MRI), alongside new technologies, such as elastography.

Development of a Serum-Based MicroRNA Signature for Early Detection of Pancreatic Cancer: A Multicenter Cohort Study

BACKGROUND

A grim prognosis of pancreatic cancer (PCa) was attributed to the difficulty in early diagnosis of the disease.

AIMS

Identifying novel biomarkers for early detection of PCa is thus urgent to improve the overall survival rates of patients.

METHODS

The study was performed firstly by identification of candidate microRNAs (miRNAs) in formalin-fixed, paraffin-embedded tissues using microarray profiles, and followed by validation in a serum-based cohort study to assess clinical utility of the candidates. In the cohorts, a total of 1273 participants from four centers were retrospectively recruited as two cohorts including training and validation cohort. The collected serum specimens were analyzed by real-time polymerase chain reaction.

RESULTS

We identified 27 miRNAs expressed differentially in PCa tissues as compared to the benign. Of which, the top-four was selected as a panel whose diagnostic efficacy was fully assessed in the serum specimens. The panel exhibited superior to CA19-9, CA125, CEA and CA242 in discriminating patients with early stage PCa from healthy controls or non-PCa including chronic pancreatitis as well as pancreatic cystic neoplasms, with the area under the curves (AUC) of 0.971 (95% CI 0.956-0.987) and 0.924 (95% CI 0.899-0.949), respectively. Moreover, the panel eliminated interference from other digestive tumors with a specificity of 90.2%.

CONCLUSIONS

A panel of four serum miRNAs was developed showing remarkably discriminative ability of early stage PCa from either healthy controls or other pancreatic diseases, suggesting it may be developed as a novel, noninvasive approach for early screening of PCa in clinic.

Preoperative differentiation of pancreatic cystic neoplasm subtypes on computed tomography radiomics

Background

Serous cystic neoplasm (SCN), mucinous cystic neoplasm (MCN), and intraductal papillary mucinous neoplasm (IPMN) comprise a large proportion of pancreatic cystic neoplasms (PCNs). Patients with MCN and IPMN require surgery due to the potential of malignant transformation, whereas those with SCN require periodic surveillance. However, the differential diagnosis of patients with PCNs before treatment remains a great challenge for all surgeons. Therefore, the establishment of a reliable diagnostic tool is urgently required for the improvement of precision diagnostics.

Methods

Between February 2015 and December 2020, 143 consecutive patients with PCNs who were confirmed by postoperative pathology were retrospectively included in the study cohort, then randomized into development and test cohorts at a ratio of 7:3. The predictors of preoperative clinical-radiologic parameters were evaluated by univariate and multivariable logistic regression analyses. A total of 1,218 radiomics features were computationally extracted from the enhanced computed tomography (CT) scans of the tumor region, and a radiomics signature was established by the random forest algorithm. In the development cohort, multi- and binary-class radiomics models integrating preoperative variables and radiomics features were constructed to distinguish between the 3 types of PCNs. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the predictive efficiency of the model. An independent internal test cohort was applied to validate the classification models.

Results

All preoperative prediction models were built by integrating the radiomics signature with 13 diagnosis-related radiomics features and 3 important clinical-radiologic parameters: age, sex, and tumor diameter. The multiclass prediction model presented an overall accuracy of 0.804 in the development cohort and 0.707 in the test cohort. The binary-class prediction models displayed higher overall accuracies of 0.853, 0.866, and 0.928 in the development dataset and 0.750, 0.839, and 0.889 in the test dataset. In the test cohort, the binary-class radiomics models showed better predictive performances {AUC =0.914 [95% confidence interval (CI): 0.786 to 1.000], 0.863 (95% CI: 0.714 to 0.941), and 0.926 (95% CI: 0.824 to 1.000)} than the multiclass radiomics model [AUC =0.850 (95% CI: 0.696 to 1.000)], with a large net benefit in the decision curve analysis (DCA). The radiomics-based nomogram provided the correct predicted probability for the diagnosis of PCNs.

Conclusions

The proposed radiomics models with clinical-radiologic parameters and radiomics features help to predict the accurate diagnosis among PCNs to advance personalized medicine.

Biomarkers of obesity-mediated insulin resistance: focus on microRNAs

Obesity and metabolic syndromes are becoming increasingly prevalent worldwide. Insulin resistance (IR) is a common complication of obesity. However, IR occurrence varies across individuals with obesity and may involve epigenetic factors. To rationalize the allocation of healthcare resources, biomarkers for the early risk stratification of individuals with obesity should be identified. MicroRNAs (miRNAs) are closely associated with metabolic diseases and involved in epigenetic regulation. In this review, we have summarized the changes in miRNA expression in the peripheral circulation and tissues of patients and animals with obesity-associated IR over the last 5 years and identified several candidate biomarkers that predict obesity-related IR. There are areas for improvement in existing studies. First, more than the predictive validity of a single biomarker is required, and a biomarker panel needs to be formed. Second, miRNAs are often studied in isolation and do not form a network of signaling pathways. We believe that early biomarkers can help clinicians accurately predict individuals prone to obesity-related IR at an early stage. Epigenetic regulation may be one of the underlying causes of different clinical outcomes in individuals with obesity. Future studies should focus on objectively reflecting the differences in miRNA profile expression in individuals with obesity-related IR, which may help identify more reliable biomarkers. Understanding the metabolic pathways of these miRNAs can help design new metabolic risk prevention and management strategies, and support the development of drugs to treat obesity and metabolic disorders.

Rare Non-Neuroendocrine Pancreatic Tumours

The most common tumour of the pancreas is ductal adenocarcinoma (PDAC). It remains one of the most lethal non-neuroendocrine solid tumours despite the use of a multi-approach strategy. Other, less-common neoplasms, which are responsible for 15% of pancreatic lesions, differ in treatment and prognosis. Due to the low incidence rate, there is a lack of information about the rarest pancreatic tumours. In this review, we described six rare pancreatic tumours: intraductal papillary mucinous neoplasm (IPMN), mucinous cystadenoma (MCN), serous cystic neoplasm (SCN), acinar cell carcinoma (ACC), solid pseudopapillary neoplasm (SPN) and pancreatoblastoma (PB). We distinguished their epidemiology, clinical and gross features, covered the newest reports about courses of treatment and systematised differential diagnoses. Although the most common pancreatic tumour, PDAC, has the highest malignant potential, it is still essential to properly classify and differentiate less-common lesions. It is vital to continue the search for new biomarkers, genetic mutations and the development of more specific biochemical tests for determining malignancy in rare pancreatic neoplasms.

ADAMTS12 promotes migration and epithelial-mesenchymal transition and predicts poor prognosis for pancreatic cancer

BACKGROUND

ADAMTS (a disintegrin and metalloproteinase with thrombospondin-like motifs) family, a group of extracellular multifunctional enzymes, has been proven to play a pivotal role in the tumor. In pancreatic cancer, the role and mechanism of this family remain unclear. The present study aimed to figure out the hub gene of ADAMTSs and explore the exact roles in the prognosis and biological functions in pancreatic ductal adenocarcinoma (PDAC).

METHODS

We used several databases to analyze the ADAMTS family and then screen out the hub genes. The expression of ADAMTS12 in 106 pairs of PDAC tumors and adjacent normal tissues was examined by immunohistochemistry, and its correlations with clinical parameters were further analyzed. The impacts of ADAMTS12 on the migration of PDAC cells were predicted by gene set enrichment analysis and confirmed by transwell assays. The potential impacts of ADAMTS12 on the epithelial-mesenchymal transition (EMT) were identified by database analysis and experimental proof of real-time quantitative polymerase chain reaction (qPCR) and Western blotting.

RESULTS

Our study found that ADAMTS12 was a crucial gene in PDAC, and it was highly expressed in tumor tissues when compared to that in the adjacent tissues. ADATMS12 had predictive value of a poor prognosis for PDAC. The elevation of ADAMTS12 was parallel to the progression of PDAC. Inhibition of ADAMTS12 suppressed the migration of PDAC cells and interfered with the process of EMT.

CONCLUSIONS

ADAMTS12 is a crucial member of ADAMTSs in PDAC and a predictor of poor prognosis. Additionally, based on its impacts on migration and metastasis in PDAC and the relationship with EMT, ADAMTS12 plays a role of an oncogene in PDAC and may be a promising target for treatment.

Salivary Polyamines Help Detect High-Risk Patients with Pancreatic Cancer: A Prospective Validation Study

Pancreatic cancer is one of the most malignant cancer types and has a poor prognosis. It is often diagnosed at an advanced stage because of the absence of typical symptoms. Therefore, it is necessary to establish a screening method for the early detection of pancreatic cancer in high-risk individuals. This is a prospective validation study conducted in a cohort of 1033 Japanese individuals (male, n = 467, age = 63.3 ± 11.5 years; female, n = 566, age = 64.2 ± 10.6 years) to evaluate the use of salivary polyamines for screening pancreatic diseases and cancers. Patients with pancreatic cancer were not included; however, other pancreatic diseases were treated as positive cases for accuracy verification. Of the 135 individuals with elevated salivary polyamine markers, 66 had pancreatic diseases, such as chronic pancreatitis and pancreatic cysts, and 1 had gallbladder cancer. These results suggest that the salivary polyamine panel is a useful noninvasive pancreatic disease screening tool.

Comparative Performance of Endoscopic Ultrasound-Based Techniques in Patients With Pancreatic Cystic Lesions: A Network Meta-Analysis

INTRODUCTION

Evidence on the comparative diagnostic performance of endoscopic ultrasound (EUS)-based techniques for pancreatic cystic lesions (PCLs) is limited. This network meta-analysis comprehensively compared EUS-based techniques for PCL diagnosis.

METHODS

A comprehensive literature search was performed for all comparative studies assessing the accuracy of 2 or more modalities for PCL diagnosis. The primary outcome was the diagnostic efficacy for mucinous PCLs. Secondary outcomes were the diagnostic efficacy for malignant PCLs, diagnostic success rate, and adverse event rate. A network meta-analysis was conducted using the ANOVA model to assess the diagnostic accuracy of each index.

RESULTS

Forty studies comprising 3,641 patients were identified. The network ranking of the superiority index for EUS-guided needle-based confocal laser endomicroscopy (EUS-nCLE) and EUS-guided through-the-needle biopsy (EUS-TTNB) were significantly higher than other techniques for differentiating mucinous PCLs; besides, EUS-TTNB was also the optimal technique in identifying malignant PCLs. The evidence was inadequate for EUS-nCLE diagnosing malignant PCLs and contrast-enhanced harmonic EUS diagnosing both mucinous and malignant PCLs. Glucose showed a high sensitivity but low specificity, and molecular analysis (KRAS, GNAS, and KRAS + GNAS mutations) showed a high specificity but low sensitivity for diagnosing mucinous PCLs. Satisfactory results were not obtained during the evaluation of the efficiency of pancreatic cyst fluid (PCF) biomarkers in detecting malignant PCLs.

DISCUSSION

For centers with relevant expertise and facilities, EUS-TTNB and EUS-nCLE were better choices for the diagnosis of PCLs. Further studies are urgently required for further improving PCF biomarkers and validating the diagnostic performance of the index techniques.

Early Diagnosis of Pancreatic Cancer: Clinical Premonitions, Timely Precursor Detection and Increased Curative-Intent Surgery

BACKGROUND

The overall poor prognosis in pancreatic cancer is related to late clinical detection. Early diagnosis remains a considerable challenge in pancreatic cancer. Unfortunately, the onset of clinical symptoms in patients usually indicate advanced disease or presence of metastasis.

ANALYSIS AND RESULTS

Currently, there are no designated diagnostic or screening tests for pancreatic cancer in clinical use. Thus, identifying risk groups, preclinical risk factors or surveillance strategies to facilitate early detection is a target for ongoing research. Hereditary genetic syndromes are a obvious, but small group at risk, and warrants close surveillance as suggested by society guidelines. Screening for pancreatic cancer in asymptomatic individuals is currently associated with the risk of false positive tests and, thus, risk of harms that outweigh benefits. The promise of cancer biomarkers and use of 'omics' technology (genomic, transcriptomics, metabolomics etc.) has yet to see a clinical breakthrough. Several proposed biomarker studies for early cancer detection lack external validation or, when externally validated, have shown considerably lower accuracy than in the original data. Biopsies or tissues are often taken at the time of diagnosis in research studies, hence invalidating the value of a time-dependent lag of the biomarker to detect a pre-clinical, asymptomatic yet operable cancer. New technologies will be essential for early diagnosis, with emerging data from image-based radiomics approaches, artificial intelligence and machine learning suggesting avenues for improved detection.

CONCLUSIONS

Early detection may come from analytics of various body fluids (eg 'liquid biopsies' from blood or urine). In this review we present some the technological platforms that are explored for their ability to detect pancreatic cancer, some of which may eventually change the prospects and outcomes of patients with pancreatic cancer.

Cancer-specific survival and metastasis in pancreatic mucinous cystadenocarcinoma: A SEER-based cohort study

Aims

This study aimed to investigate the prognostic value of clinical features for cancer-specific survival (CSS) and metastasis in patients with pancreatic mucinous cystadenocarcinoma (MCAC). We further constructed and validated an effective nomogram to predict CSS.

Methods

We screened patients diagnosed with pancreatic MCAC from Surveillance Epidemiology and End Results (SEER) database. Kaplan-Meier curves were used to determine the CSS time. Univariate and multivariate Cox and logistic regression analyses were conducted to identify the prognostic factors for CSS and metastasis. The nomogram was constructed to predict the prognosis of pancreatic MCAC based on the results from the multivariate analysis. We used the concordance index (C-index), the area under the curve (AUC), and the calibration plots to determine the predictive accuracy and discriminability of the nomogram.

Results

Multivariate Cox analysis revealed that age, primary site, grade, and radiotherapy were independent prognostic factors associated with CSS. Multivariate logistic regression analysis revealed that surgery and grade were independent risk factors associated with metastasis. The independent risk factors were included to construct a prognosis prediction model for predicting CSS in patients with pancreatic MCAC. The concordance index (C-index), receiver operating characteristic (ROC) curves, and calibration plots of the training cohort and the validation cohort showed that the nomogram had an acceptable predictive performance.

Conclusion

We established a nomogram that could determine the 3- and 5-year CSS, which could evaluate individual clinical outcomes and provide individualized clinical decisions.

Imaging Modalities for Early Detection of Pancreatic Cancer: Current State and Future Research Opportunities

Simple Summary

While survival rates for many cancers have improved dramatically over the last 20 years, patients with pancreatic cancer have persistently poor outcomes. The majority of patients with pancreatic cancer are not suitable for potentially curative surgery due to locally advanced or metastatic disease stage at diagnosis. Therefore, early detection would potentially improve survival of pancreatic cancer patients through earlier intervention. Here, we present clinical challenges in the early detection of pancreatic cancer, characterise high risk groups for pancreatic cancer and current screening programs in high-risk individuals. The aim of this scoping review is to investigate the role of both established and novel imaging modalities for early detection of pancreatic cancer. Furthermore, we investigate innovative imaging techniques for early detection of pancreatic cancer, but its widespread application requires further investigation and potentially a combination with other non-invasive biomarkers.

Abstract

Pancreatic cancer, one of the most lethal malignancies, is increasing in incidence. While survival rates for many cancers have improved dramatically over the last 20 years, people with pancreatic cancer have persistently poor outcomes. Potential cure for pancreatic cancer involves surgical resection and adjuvant therapy. However, approximately 85% of patients diagnosed with pancreatic cancer are not suitable for potentially curative therapy due to locally advanced or metastatic disease stage. Because of this stark survival contrast, any improvement in early detection would likely significantly improve survival of patients with pancreatic cancer through earlier intervention. This comprehensive scoping review describes the current evidence on groups at high risk for developing pancreatic cancer, including individuals with inherited predisposition, pancreatic cystic lesions, diabetes, and pancreatitis. We review the current roles of imaging modalities focusing on early detection of pancreatic cancer. Additionally, we propose the use of advanced imaging modalities to identify early, potentially curable pancreatic cancer in high-risk cohorts. We discuss innovative imaging techniques for early detection of pancreatic cancer, but its widespread application requires further investigation and potentially a combination with other non-invasive biomarkers.

Liquid Biopsy as a Prognostic and Theranostic Tool for the Management of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinomas (PDAC) represent one of the deadliest cancers worldwide. Survival is still low due to diagnosis at an advanced stage and resistance to treatment. Herein, we review the main types of liquid biopsy able to help in both prognosis and adaptation of treatments.

An Enzyme-Activatable Aggregation-Induced-Emission Probe: Intraoperative Pathological Fluorescent Diagnosis of Pancreatic Cancer via Specific Cathepsin E

Pancreatic cancer (PC) is one of the most devastating malignant tumors. However, fluorescence probes for early clinical diagnosis of PC often encounter difficulties in accuracy and penetrability. In this work, an enzyme-activated aggregation-induced-emission (AIE) probe, QM-HSP-CPP, for high-contrast fluorescence diagnosis of PC is developed by monitoring specific overexpressed enzyme Cathepsin E (CTSE). The probe is composed of an AIE fluorophore QM-COOH (QM = quinoline-malononitrile), CTSE-triggered hydrophobic peptide (HSP), and hydrophilic biocompatible cell penetrating peptide (CPP). The CPP unit can well-modulate the molecular dispersion properties, giving initial fluorescence-off state in the aqueous biosystem, thus endowing high signal-to-noise ratio, and finally overcoming the poor targeting selectivity of traditional AIE probes. CPP can ensure cell/tissue penetrating ability, thus allowing on-site monitoring of endogenous CTSE in PC cells, tissues, and living animal models. When the QM-HSP-CPP probe is specifically cleaved by CTSE, it can generate AIE signals in situ with high-specificity and long-term tracking ability, and successfully achieve intraoperative diagnosis of human PC sections, tracking PC in heterotopic nude mice models. The CTSE-enzyme-triggered AIEgens' liberation strategy improves accuracy and addresses the penetration problem simultaneously, which can expand the database of multitudinous biocompatible AIE-active probes, especially for establishing intraoperative pathological fluorescent diagnosis.

Multi-Phase CT-Based Radiomics Nomogram for Discrimination Between Pancreatic Serous Cystic Neoplasm From Mucinous Cystic Neoplasm

Purpose

This study aimed to develop and verify a multi-phase (MP) computed tomography (CT)-based radiomics nomogram to differentiate pancreatic serous cystic neoplasms (SCNs) from mucinous cystic neoplasms (MCNs), and to compare the diagnostic efficacy of radiomics models for different phases of CT scans.

Materials and Methods

A total of 170 patients who underwent surgical resection between January 2011 and December 2018, with pathologically confirmed pancreatic cystic neoplasms (SCN=115, MCN=55) were included in this single-center retrospective study. Radiomics features were extracted from plain scan (PS), arterial phase (AP), and venous phase (VP) CT scans. Algorithms were performed to identify the optimal features to build a radiomics signature (Radscore) for each phase. All features from these three phases were analyzed to develop the MP-Radscore. A combined model comprised the MP-Radscore and imaging features from which a nomogram was developed. The accuracy of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration tests, and decision curve analysis.

Results

For each scan phase, 1218 features were extracted, and the optimal ones were selected to construct the PS-Radscore (11 features), AP-Radscore (11 features), and VP-Radscore (12 features). The MP-Radscore (14 features) achieved better performance based on ROC curve analysis than any single phase did [area under the curve (AUC), training cohort: MP-Radscore 0.89, PS-Radscore 0.78, AP-Radscore 0.83, VP-Radscore 0.85; validation cohort: MP-Radscore 0.88, PS-Radscore 0.77, AP-Radscore 0.83, VP-Radscore 0.84]. The combination nomogram performance was excellent, surpassing those of all other nomograms in both the training cohort (AUC, 0.91) and validation cohort (AUC, 0.90). The nomogram also performed well in the calibration and decision curve analyses.

Conclusions

Radiomics for arterial and venous single-phase models outperformed the plain scan model. The combination nomogram that incorporated the MP-Radscore, tumor location, and cystic number had the best discriminatory performance and showed excellent accuracy for differentiating SCN from MCN.

Nanoparticle-based delivery systems modulate the tumor microenvironment in pancreatic cancer for enhanced therapy

Pancreatic cancer is one of the most lethal malignant tumors with a low survival rate, partly because the tumor microenvironment (TME), which consists of extracellular matrix (ECM), cancer-associated fibroblasts (CAFs), immune cells, and vascular systems, prevents effective drug delivery and chemoradiotherapy. Thus, modulating the microenvironment of pancreatic cancer is considered a promising therapeutic approach. Since nanoparticles are one of the most effective cancer treatment strategies, several nano-delivery platforms have been developed to regulate the TME and enhance treatment. Here, we summarize the latest advances in nano-delivery systems that alter the TME in pancreatic cancer by depleting ECM, inhibiting CAFs, reversing immunosuppression, promoting angiogenesis, or improving the hypoxic environment. We also discuss promising new targets for such systems. This review is expected to improve our understanding of how to modulate the pancreatic cancer microenvironment and guide the development of new therapies.

Comparison of Radiomic Features in a Diverse Cohort of Patients With Pancreatic Ductal Adenocarcinomas

Background

Significant racial disparities in pancreatic cancer incidence and mortality rates exist, with the highest rates in African Americans compared to Non-Hispanic Whites and Hispanic/Latinx populations. Computer-derived quantitative imaging or “radiomic” features may serve as non-invasive surrogates for underlying biological factors and heterogeneity that characterize pancreatic tumors from African Americans, yet studies are lacking in this area. The objective of this pilot study was to determine if the radiomic tumor profile extracted from pretreatment computed tomography (CT) images differs between African Americans, Non-Hispanic Whites, and Hispanic/Latinx with pancreatic cancer.

Methods

We evaluated a retrospective cohort of 71 pancreatic cancer cases (23 African American, 33 Non-Hispanic White, and 15 Hispanic/Latinx) who underwent pretreatment CT imaging at Moffitt Cancer Center and Research Institute. Whole lesion semi-automated segmentation was performed on each slice of the lesion on all pretreatment venous phase CT exams using Healthmyne Software (Healthmyne, Madison, WI, USA) to generate a volume of interest. To reduce feature dimensionality, 135 highly relevant non-texture and texture features were extracted from each segmented lesion and analyzed for each volume of interest.

Results

Thirty features were identified and significantly associated with race/ethnicity based on Kruskal-Wallis test. Ten of the radiomic features were highly associated with race/ethnicity independent of tumor grade, including sphericity, volumetric mean Hounsfield units (HU), minimum HU, coefficient of variation HU, four gray level texture features, and two wavelet texture features. A radiomic signature summarized by the first principal component partially differentiated African American from non-African American tumors (area underneath the curve = 0.80). Poorer survival among African Americans compared to Non-African Americans was observed for tumors with lower volumetric mean CT [HR: 3.90 (95% CI:1.19–12.78), p=0.024], lower GLCM Avg Column Mean [HR:4.75 (95% CI: 1.44,15.37), p=0.010], and higher GLCM Cluster Tendency [HR:3.36 (95% CI: 1.06–10.68), p=0.040], and associations persisted in volumetric mean CT and GLCM Avg Column after adjustment for key clinicopathologic factors.

Conclusions

This pilot study identified several textural radiomics features associated with poor overall survival among African Americans with PDAC, independent of other prognostic factors such as grade. Our findings suggest that CT radiomic features may serve as surrogates for underlying biological factors and add value in predicting clinical outcomes when integrated with other parameters in ongoing and future studies of cancer health disparities.

Biomarker Risk Score Algorithm and Preoperative Stratification of Patients with Pancreatic Cystic Lesions

BACKGROUND

Pancreatic cysts are incidentally detected in up to 13% of patients undergoing radiographic imaging. Of the most frequently encountered types, mucin-producing (mucinous) pancreatic cystic lesions may develop into pancreatic cancer, while nonmucinous ones have little or no malignant potential. Accurate preoperative diagnosis is critical for optimal management, but has been difficult to achieve, resulting in unnecessary major surgery. Here, we aim to develop an algorithm based on biomarker risk scores to improve risk stratification.

STUDY DESIGN

Patients undergoing surgery and/or surveillance for a pancreatic cystic lesion, with diagnostic imaging and banked pancreatic cyst fluid, were enrolled in the study after informed consent (n = 163 surgical, 67 surveillance). Cyst fluid biomarkers with high specificity for distinguishing nonmucinous from mucinous pancreatic cysts (vascular endothelial growth factor [VEGF], glucose, carcinoembryonic antigen [CEA], amylase, cytology, and DNA mutation) were selected. Biomarker risk scores were used to design an algorithm to predict preoperative diagnosis. Performance was tested using surgical (retrospective) and surveillance (prospective) cohorts.

RESULTS

In the surgical cohort, the biomarker algorithm outperformed the preoperative clinical diagnosis in correctly predicting the final pathologic diagnosis (91% vs 73%; p < 0.000001). Specifically, nonmucinous serous cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN) were correctly classified more frequently by the algorithm than clinical diagnosis (96% vs 30%; p < 0.000008 and 92% vs 69%; p = 0.04, respectively). In the surveillance cohort, the algorithm predicted a preoperative diagnosis with high confidence based on a high biomarker score and/or consistency with imaging from ≥1 follow-up visits.

CONCLUSIONS

A biomarker risk score-based algorithm was able to correctly classify pancreatic cysts preoperatively. Importantly, this tool may improve initial and dynamic risk stratification, reducing overdiagnosis and underdiagnosis.

Preoperative differentiation of serous cystic neoplasms from mucin-producing pancreatic cystic neoplasms using a CT-based radiomics nomogram

PURPOSE

To develop and validate a CT-based radiomics nomogram in preoperative differential diagnosis of SCNs from mucin-producing PCNs.

MATERIAL AND METHODS

A total of 89 patients consisting of 31 SCNs, 30 IPMNs, and 28 MCNs who underwent preoperative CT were analyzed. A total of 710 radiomics features were extracted from each case. Patients were divided into training (n = 63) and validation cohorts (n = 26) with a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) method and logistic regression analysis were used for feature selection and model construction. A nomogram was created from a comprehensive model consisting of clinical features and the fusion radiomics signature. A decision curve analysis was used for clinical decisions.

RESULTS

The radiomics features extracted from CT could assist with the differentiation of SCNs from mucin-producing PCNs in both the training and validation cohorts. The signature of the combination of the plain, late arterial, and venous phases had the largest areas under the curve (AUCs) of 0.960 (95% CI 0.910-1) in the training cohort and 0.817 (95% CI 0.651-0.983) in the validation cohort with good calibration. The value and efficacy of the nomogram was verified using decision curve analysis.

CONCLUSION

A comprehensive nomogram incorporating clinical features and fusion radiomics signature can differentiate SCNs from mucin-producing PCNs.

Application of EUS-based techniques in the evaluation of pancreatic cystic neoplasms

Pancreatic cystic neoplasms (PCNs) are being detected increasingly frequently due to the widespread use of high-resolution abdominal imaging modalities. Some subtypes of PCNs have the potential for malignant transformation. Therefore, accurate diagnosis of PCNs is crucial to determine whether surgical resection or surveillance is the best management strategy. However, the current cross-section imaging modalities are not accurate enough to enable definite diagnoses. In the last decade, EUS-based techniques have emerged, aiming to overcome the limitations of standard cross-section imaging modalities. These novel EUS-based techniques were primarily designed to acquire distinct images to make radiological diagnoses, collect cyst fluid to undergo biochemical or molecular analyses, and obtain tissue to conclude the pathological diagnoses. In this article, we present a comprehensive and critical review of these emerging EUS techniques for the diagnosis of PCNs, with emphasis being placed on the advantages, feasibilities, diagnostic performances, and limitations of these novel techniques.

Inactivation of Pancreatic Stellate Cells by Exendin-4 Inhibits the Migration and Invasion of Pancreatic Cancer Cells

Background

Pancreatic stellate cells (PSCs) are precursor cells of cancer-associated fibroblasts that promote tumor proliferation, invasion, and metastasis. The glucagon-like peptide-1 receptor agonist exendin-4 has been reported to exhibit anticancer effects against several tumor cells; however, the function and mechanism underlying the effects of exendin-4 on pancreatic cancer cells remain unclear.

Methods

Gene expression levels were determined using quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot assay. Cell viability, migration and invasion were assessed using the cell counting kit-8 (CCK-8), wound healing, and transwell assays, respectively. A xenografted tumor model was established in mouse to evaluate the effects of exendin-4 in vivo.

Results

Exendin-4 treatment led to the inactivation of PSCs and suppressed their proliferation and migration. Moreover, we also found that exendin-4 attenuated NF-κB-dependent SDF-1 secretion. Furthermore, pancreatic cancer cells incubated with conditioned medium obtained from exendin-4-treated PSCs showed a decreased ability to proliferate, migrate, and invade as compared to the control cells, which is similar to the effects induced by the CXCR4 inhibitor, AMD3100. Consistent with in vitro results, we also confirmed that exendin-4 indirectly targeted pancreatic cancer cells in vivo by attenuating the function of PSCs and suppressing the deposition of extracellular matrix.

Conclusion

These results revealed that exendin-4-treated PSCs could suppress pancreatic cancer cell proliferation and invasion, offering a potential strategy for the treatment of pancreatic cancer.