Endoscopic Ultrasonography Diagnosis of Early Pancreatic Cancer

Potential of Carbohydrate Antigen 19-9 and Serum Apolipoprotein A2-Isoforms in the Diagnosis of Stage 0 and IA Pancreatic Cancer

Apolipoprotein A2-ATQ/AT (apoA2-ATQ/AT) is a new biomarker for diagnosing pancreatic cancer (PC). In this study, the value of blood carbohydrate antigen 19-9 (CA19-9) and apoA2-ATQ/AT levels in diagnosing stage 0 and IA PC was evaluated. During 2014-2021, 12 patients with stage 0 PC and 12 patients with IA PC (average age: 73.8 years) underwent resection at JA Onomichi General Hospital. In addition, the data of 200 healthy controls were collected from a community-based cohort study. Levels of two apoA2-isoforms were measured using enzyme-linked immunosorbent assay (ELISA) with specific antibodies to calculate the apoA2-i Index as a surrogate value for apoA2-ATQ/AT. The cutoff value for the apoA2-i Index was determined to be 62.9 μg/mL. CA19-9 levels were also measured through ELISA. Among all 24 patients with PC, the positivity rates for apoA2-i and CA19-9 were 33.3% and 25.0%, respectively. The positivity rates for apoA2-i and CA19-9 were 16.7% and 8.3% in patients with stage 0 PC and 50.0% and 41.7% in those with stage IA, respectively. For CA19-9-negative patients, the apoA2-i positivity rate was 9.1% in stage 0 and 42.9% in stage IA. The combined positivity rate for both markers was 16.7% in stage 0 and 66.7% in stage IA. Imaging findings in apoA2-i- and CA19-9-positive patients included pancreatic duct dilatation (87.5%/100%), duct stenosis (75.0%/50%), and atrophy (87.5%/66.7%). The imaging findings of this study suggest that apoA2-i may enhance the sensitivity for detecting CA19-9-negative stage 0 and IA PC, and complementary measurements with CA19-9 may be valuable for diagnosing early-stage PC. Therefore, minute PC with pancreatic duct dilation, duct stenosis, and atrophy may exhibit a high positivity rate, aiding differential diagnosis.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

A novel method for detection of pancreatic Ductal Adenocarcinoma using explainable machine learning

BACKGROUND AND OBJECTIVE

Pancreatic Ductal Adenocarcinoma (PDAC) is a form of pancreatic cancer that is one of the primary causes of cancer-related deaths globally, with less than 10 % of the five years survival rate. The prognosis of pancreatic cancer has remained poor in the last four decades, mainly due to the lack of early diagnostic mechanisms. This study proposes a novel method for detecting PDAC using explainable and supervised machine learning from Raman spectroscopic signals.

METHODS

An insightful feature set consisting of statistical, peak, and extended empirical mode decomposition features is selected using the support vector machine recursive feature elimination method integrated with a correlation bias reduction. Explicable features successfully identified mutations in Kirsten rat sarcoma viral oncogene homolog (KRAS) and tumor suppressor protein53 (TP53) in the fingerprint region for the first time in the literature. PDAC and normal pancreas are classified using K-nearest neighbor, linear discriminant analysis, and support vector machine classifiers.

RESULTS

This study achieved a classification accuracy of 98.5% using a nonlinear support vector machine. Our proposed method reduced test time by 28.5 % and saved 85.6 % memory utilization, which reduces complexity significantly and is more accurate than the state-of-the-art method. The generalization of the proposed method is assessed by fifteen-fold cross-validation, and its performance is evaluated using accuracy, specificity, sensitivity, and receiver operating characteristic curves.

CONCLUSIONS

In this study, we proposed a method to detect and define the fingerprint region for PDAC using explainable machine learning. This simple, accurate, and efficient method for PDAC detection in mice could be generalized to examine human pancreatic cancer and provide a basis for precise chemotherapy for early cancer treatment.

A review study on early detection of pancreatic ductal adenocarcinoma using artificial intelligence assisted diagnostic methods

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive, chemo-refractory and recalcitrant cancer and increases the number of deaths. With just around 1 in 4 individuals having respectable tumours, PDAC is frequently discovered when it is in an advanced stage. Accordingly, ED of PDAC improves patient survival. Subsequently, this paper reviews the early detection of PDAC, initially, the work presented an overview of PDAC. Subsequently, it reviews the molecular biology of pancreatic cancer and the development of molecular biomarkers are represented. This article illustrates the importance of identifying PDCA, the Immune Microenvironment of Pancreatic Cancer. Consequently, in this review, traditional and non-traditional imaging techniques are elucidated, traditional and non-traditional methods like endoscopic ultrasound, Multidetector CT, CT texture analysis, PET-CT, magnetic resonance imaging, diffusion-weighted imaging, secondary signs of pancreatic cancer, and molecular imaging. The use of artificial intelligence in pancreatic cancer, novel MRI techniques, and the future directions of AI for PDAC detection and prognosis is then described. Additionally, the research problem definition and motivation, current trends and developments, state of art of survey, and objective of the research are demonstrated in the review. Consequently, this review concluded that Artificial Intelligence Assisted Diagnostic Methods with MRI images can be proposed in future to improve the specificity and the sensitivity of the work, and to classify malignant PDAC with greater accuracy.

Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis

Pancreatic cancer (PC) has a low incidence rate but a high mortality, with patients often in the advanced stage of the disease at the time of the first diagnosis. If detected, early neoplastic lesions are ideal for surgery, offering the best prognosis. Preneoplastic lesions of the pancreas include pancreatic intraepithelial neoplasia and mucinous cystic neoplasms, with intraductal papillary mucinous neoplasms being the most commonly diagnosed. Our study focused on predicting PC by identifying early signs using noninvasive techniques and artificial intelligence (AI). A systematic English literature search was conducted on the PubMed electronic database and other sources. We obtained a total of 97 studies on the subject of pancreatic neoplasms. The final number of articles included in our study was 44, 34 of which focused on the use of AI algorithms in the early diagnosis and prediction of pancreatic lesions. AI algorithms can facilitate diagnosis by analyzing massive amounts of data in a short period of time. Correlations can be made through AI algorithms by expanding image and electronic medical records databases, which can later be used as part of a screening program for the general population. AI-based screening models should involve a combination of biomarkers and medical and imaging data from different sources. This requires large numbers of resources, collaboration between medical practitioners, and investment in medical infrastructures.

Pancreatic duct imaging during aging

As part of the aging process, fibrotic changes, fatty infiltration, and parenchymal atrophy develop in the pancreas. The pancreatic duct also becomes wider with age. This article provides an overview of the diameter of the pancreatic duct in different age groups and different examination methods. Knowledge of these data is useful to avoid misinterpretations regarding the differential diagnosis of chronic pancreatitis, obstructive tumors, and intraductal papillary mucinous neoplasia (IPMN).

Usefulness of Practitioner-Led Pancreatic Cancer Screening

The 5-year survival rate for pancreatic cancer has improved (10%) but remains worse than that for other cancers. Early pancreatic cancer diagnosis is challenging, and delayed diagnosis can delay treatment, which impairs survival. Practitioners do not promptly refer cases to a general hospital, causing delayed discovery. Herein, we aimed to examine the usefulness of the Pancreatic Cancer Project in Matsue, whose objective is to detect pancreatic cancer in patients presenting at any medical institution in Matsue City. Clinical data were extracted from medical records, and abdominal ultrasonography and tumor marker blood level assessments were performed (n = 234; median age, 71 [range, 41–94] years; 51% male). Cases with abnormal abdominal ultrasonography or blood test findings were referred for specialist imaging and followed up. The pancreatic cancer detection rate was 6.0% (n = 14); all cases were referred to a general hospital by practitioners within 1 month. Patients had stage IA (n = 1), IIA (n = 6), IIB (n = 2), III (n = 1), and IV (n = 4) disease. Overall, pancreatic cancer could be detected at an earlier stage (I–II), but referral to a general hospital by visiting practitioners should be prompt. The Pancreatic Cancer Project in Matsue may help improve the detection and prognosis of pancreatic cancer.

Advances in biomarkers and techniques for pancreatic cancer diagnosis

Pancreatic cancer is the most lethal type of malignancy and is characterized by high invasiveness without severe symptoms. It is difficult to detect PC at an early stage because of the low diagnostic accuracy of existing routine methods, such as abdominal ultrasound, CT, MRI, and endoscopic ultrasound (EUS). Therefore, it is of value to develop new diagnostic techniques for early detection with high accuracy. In this review, we aim to highlight research progress on novel biomarkers, artificial intelligence, and nanomaterial applications on the diagnostic accuracy of pancreatic cancer.

Oxidative Stress Markers Are Associated with a Poor Prognosis in Patients with Pancreatic Cancer

Pancreatic cancer is a malignancy of rising prevalence, especially in developed countries where dietary patterns and sedentariness favor its onset. This malady ranks seventh in cancer-related deaths in the world, although it is expected to rank second in the coming years, behind lung cancer. The low survival rate is due to the asymptomatic course of the early stages, which in many cases leads to metastases when becoming evident in advanced stages. In this context, molecular pathology is on the way towards finding new approaches with biomarkers that allow a better prognosis and monitoring of patients. So the present study aims to evaluate a series of molecular biomarkers, PARP1, NOX1, NOX2, eNOS and iNOS, as promising candidates for prognosis and survival by using immunohistochemistry. The analysis performed in 41 patients with pancreatic cancer showed a correlation between a high expression of all these components with a low survival rate, with high statistical power for all. In addition, a 60-month longitudinal surveillance program was managed, accompanied by several clinical parameters. The derivative Kaplan–Meier curves indicated a low cumulative survival rate as well. Ultimately, our research emphasized the value of these molecules as survival-associated biomarkers in pancreatic cancer, offering new gates for clinical management.

Hypoxia-Related Gene FUT11 Promotes Pancreatic Cancer Progression by Maintaining the Stability of PDK1

Background

Hypoxia is associated with the development of pancreatic cancer (PC). However, genes associated with hypoxia response and their regulatory mechanism in PC cells were unclear. The current study aims to investigate the role of the hypoxia associated gene fucosyltransferase 11 (FUT11) in the progression of PC.

Methods

In the preliminary study, bioinformatics analysis predicted FUT11 as a key hypoxia associated gene in PC. The expression of FUT11 in PC was evaluated using quantitative real-time PCR (qRT-PCR), Western blot and immunohistochemistry. The effects of FUT11 on PC cells proliferation and migration under normoxia and hypoxia were evaluated using Cell Counting Kit 8, 5-ethynyl-2'-deoxyuridine (EDU) assay, colony formation assay and transwell assay. The effects of FUT11 in vivo was examined in mouse tumor models of liver metastasis and subcutaneous xenograft. Furthermore, Western blot, luciferase assay and immunoprecipitation were performed to explore the regulatory relationship among FUT11, hypoxia-inducible factor 1α (HIF1α) and pyruvate dehydrogenase kinase 1 (PDK1) in PC.

Results

FUT11 was markedly increased of PC cells with hypoxia, upregulated in the PC clinical tissues, and predicted a poor outcome of PC patients. Inhibition of FUT11 reduced PC cell growth and migratory ability of PC cells under normoxia and hypoxia conditions in vitro, and growth and tumor cell metastasis in vivo. FUT11 bound to PDK1 and regulated the expression PDK1 under normoxia and hypoxia. FUT11 interacted with PDK1 and decreased the ubiquitination of PDK1, lead to the activation of AKT/mTOR signaling pathway. FUT11 knockdown significantly increased the degradation of PDK1 under hypoxia, while treatment with MG132 can relieve the degradation of PDK1 induced by FUT11 knockdown. Overexpression of PDK1 in PC cells under hypoxia conditions reversed the suppressive impacts of FUT11 knockdown on PC cell growth and migration. In addition, HIF1α bound to the promoter of FUT11 and increased its expression, as well as co-expressed with FUT11 in PC tissues. Furthermore, overexpression of FUT11 partially rescued the suppressive effects of HIF1α knockdown on PC cell growth and migration in hypoxia condition.

Conclusion

Our data implicate that hypoxia-induced FUT11 contributes to proliferation and metastasis of PC by maintaining the stability of PDK1, thus mediating activation of AKT/mTOR signaling pathway, and suggest that FUT11 could be a novel and effective target for the treatment of pancreatic cancer.

Clinical Analysis of Early-Stage Pancreatic Cancer and Proposal for a New Diagnostic Algorithm: A Multicenter Observational Study

Early diagnosis of pancreatic ductal adenocarcinoma (PDAC) is challenging but essential for improving its poor prognosis. We established a multicenter study to clarify the clinicopathological features, and to propose new algorithm for early diagnosis of PDAC. Ninety-six patients with stage 0 and IA PDAC were enrolled from 13 high-volume centers. Overall, 70% of the patients were asymptomatic. The serum pancreatic enzyme levels were abnormal in half of the patients. The sensitivity of endoscopic ultrasonography (EUS) for detecting small PDAC was superior to computed tomography and magnetic resonance imaging (MRI) (82%, 58%, and 38%, respectively). Indirect imaging findings were useful to detect early-stage PDAC; especially, main pancreatic duct stenosis on MRI had the highest positive rate of 86% in stage 0 patients. For preoperative pathological diagnosis, the sensitivity of endoscopic retrograde cholangiopancreatography (ERCP)-associated pancreatic juice cytology was 84%. Among the stage IA patients, EUS-guided fine-needle aspiration revealed adenocarcinoma in 93% patients. For early diagnosis of PDAC, it is essential to identify asymptomatic patients and ensure close examinations of indirect imaging findings and standardization of preoperative pathological diagnosis. Therefore, a new diagnostic algorithm based on tumor size and imaging findings should be developed.