Esophageal cancer screening,early detection and treatment:Current insights and future directions

Esophageal cancer ranks among the most prevalent malignant tumors globally,primarily due to its highly aggressive nature and poor survival rates.According to the 2020 global cancer statistics,there were approximately 604000 new cases of esophageal cancer,resulting in 544000 deaths.The 5-year survival rate hovers around a mere 15%-25%.Notably,distinct variations exist in the risk factors associated with the two primary histological types,influencing their worldwide incidence and distribution.Squamous cell carcinoma displays a high incidence in specific regions,such as certain areas in China,where it meets the cost-effect-iveness criteria for widespread endoscopy-based early diagnosis within the local population.Conversely,adenocarcinoma(EAC)represents the most common histological subtype of esophageal cancer in Europe and the United States.The role of early diagnosis in cases of EAC originating from Barrett's esophagus(BE)remains a subject of controversy.The effectiveness of early detection for EAC,particularly those arising from BE,continues to be a debated topic.The variations in how early-stage esophageal carcinoma is treated in different regions are largely due to the differing rates of early-stage cancer diagnoses.In areas with higher incidences,such as China and Japan,early diagnosis is more common,which has led to the advancement of endoscopic methods as definitive treatments.These techniques have demonstrated remarkable efficacy with minimal complications while preserving esophageal functionality.Early screening,prompt diagnosis,and timely treatment are key strategies that can significantly lower both the occurrence and death rates associated with esophageal cancer.

Early diagnosis of esophageal cancer:How to put“early detection”into effect?

This editorial comments on the article by Qu et al in a recent edition of World Journal of Gastrointestinal Oncology,focusing on the importance of early diagnosis in managing esophageal cancer and strategies for achieving“early detection”.The five-year age-standardized net survival for esophageal cancer patients falls short of expectations.Early detection and accurate diagnosis are critical strategies for improving the treatment outcomes of esophageal cancer.While advancements in endoscopic technology have been significant,there seems to be an excessive emphasis on the latest high-end endoscopic devices and various endoscopic resection techniques.Therefore,it is imperative to redirect focus towards proactive early detection strategies for esophageal cancer,investigate the most cost-effective screening methods suitable for different regions,and persistently explore practical solutions to improve the five-year survival rate of patients with esophageal cancer.

Early detection of multiple endocrine neoplasia type 1: A case report

BACKGROUND Multiple endocrine neoplasias(MENs)are a group of hereditary diseases invol-ving multiple endocrine glands,and their prevalence is low.MEN type 1(MEN1)has diverse clinical manifestations,mainly involving the parathyroid glands,gastrointestinal tract,pancreas and pituitary gland,making it easy to miss the clinical diagnosis.CASE SUMMARY We present the case of a patient in whom MEN1 was detected early.A middle-aged male with recurrent abdominal pain and diarrhea was admitted to the hos-pital.Blood tests at admission revealed hypercalcemia and hypophosphatemia,and emission computed tomography of the parathyroid glands revealed a hy-perfunctioning parathyroid lesion.Gastroscopy findings suggested a duodenal bulge and ulceration.Ultrasound endoscopy revealed a hypoechoic lesion in the duodenal bulb.Further blood tests revealed elevated levels of serum gastrin.Surgery was performed,and pathological analysis of the surgical specimens revealed a parathyroid adenoma after parathyroidectomy and a neuroendocrine tumor after duodenal bulbectomy.The time from onset to the definitive diagnosis of MEN1 was only approximately 1 year.CONCLUSION For patients who present with gastrointestinal symptoms accompanied by hyper-calcemia and hypophosphatemia,clinicians need to be alert to the possibility of MEN1.

Colorectal cancer screening:The value of early detection and modern challenges

The screening of colorectal cancer(CRC)is pivotal for both the prevention and treatment of this disease,significantly improving early-stage tumor detection rates.This advancement not only boosts survival rates and quality of life for patients but also reduces the costs associated with treatment.However,the adoption of CRC screening methods faces numerous challenges,including the technical limitations of both noninvasive and invasive methods in terms of sensitivity and specificity.Moreover,socioeconomic factors such as regional disparities,economic conditions,and varying levels of awareness affect screening uptake.The coronavirus disease 2019 pandemic further intensified these challenges,leading to reduced screening participation and increased waiting periods.Additionally,the growing prevalence of early-onset CRC necessitates innovative screening approaches.In response,research into new methodologies,including artificial intelligence-based systems,aims to improve the precision and accessibility of screening.Proactive measures by governments and health organizations to enhance CRC screening efforts are underway,including increased advocacy,improved service delivery,and international cooperation.The role of technological innovation and global health collaboration in advancing CRC screening is undeniable.Technologies such as artificial intelligence and gene sequencing are set to revolutionize CRC screening,making a significant impact on the fight against this disease.Given the rise in early-onset CRC,it is crucial for screening strategies to continually evolve,ensuring their effectiveness and applicability.

Exploring the Role of Serum Cystatin C in Early Detection of Acute Kidney Injury among On-Pump Cardiac Surgery Patients: A Single-Center Investigation in Bangladesh

Background: Acute Kidney Injury (AKI) stands as a prominent postoperative complication in on-pump cardiac surgery, with repercussions on morbidity, mortality, and hospitalization duration. Current diagnostic criteria relying on serum creatinine levels exhibit a delayed identification of AKI, prompting an exploration of alternative biomarkers. Aims and Objectives: This study is designed to overcome diagnostic constraints and explore the viability of serum Cystatin C as an early predictor of Acute Kidney Injury (AKI) in individuals undergoing on-pump cardiac surgery. The investigation aims to establish the relationship between serum Cystatin C levels and the onset of AKI in patients subjected to on-pump cardiac surgery. Primary objectives involve the assessment of the diagnostic effectiveness of serum Cystatin C, its comparison with serum creatinine, and the exploration of its potential for the early identification and treatment of AKI. Methodology: Conducted as a single-center study at the cardiac surgery department of BSMMU in Bangladesh from September 2020 to August 2022, a comparative cross-sectional analysis involved 31 participants categorized into No AKI and AKI groups based on Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Data collection encompassed preoperative, post-CBP (cardiopulmonary bypass) conclusion at 2 hours, postoperative day 1, and postoperative day 2 intervals. Statistical analyses included Chi-squared tests, independent Student’s t-tests, and one-sample t-tests. Significance was set at P Results: The study revealed no significant differences in baseline characteristics between the No AKI and AKI groups, except for CPB time and cross-clamp time. Serum Cystatin C levels in the AKI group exhibited statistical significance at various time points, highlighting its potential as an early detector. Conversely, Serum Creatinine levels in the AKI group showed no statistical significance. The Receiver Operating Characteristic (ROC) curve analysis further supported the efficacy of serum Cystatin C, with an Area under the ROC Curve of 0.864 and a cut-off value of 0.55 (p Conclusion: This study supports the superior utility of serum Cystatin C as an early detector of AKI in on-pump cardiac surgery patients compared to serum creatinine. Its ability to identify AKI several hours earlier may contribute to reduced morbidity, mortality, and healthcare costs. The findings underscore the significance of exploring novel biomarkers for improved post-cardiac surgery renal function assessment.

Barriers in early detection of colorectal cancer and exploring potential solutions

This editorial discusses the literature review article by Tonini and Zanni,the paper was published in January 2024,and the authors provided very interesting conclusions regarding existing barriers to the early diagnosis of colon cancer.Many cancers do not have identifiable precursors,or there are currently no screening tests to find them.Therefore,these cancers do not have preventive screening options.Early detection is crucial for reducing mortality rates by identifying cancer at an earlier stage through screening,as opposed to no screening.Colorectal cancer develops from precancerous lesions,which can be detected early and potentially prevented and cured.Early detection leads to improved survival rates,decreased complications,and reduced healthcare expenses.This editorial provides a brief description of the biology of colon cancer,emphasizing the contrast in outcomes between early detection and late detection.We also describe screening programs around the globe and examine the barriers in each program.Finally,we explore potential future solutions to enhance inclusion in screening programs and improve patient compliance.

Advancing critical care recovery:The pivotal role of machine learning in early detection of intensive care unit-acquired weakness

This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.

Novel biomarkers for early detection of gastric cancer

Gastric cancer(GC)remains a leading cause of cancer-related death worldwide.Less than half of GC cases are diagnosed at an advanced stage due to its lack of early symptoms.GC is a heterogeneous disease associated with a number of genetic and somatic mutations.Early detection and effective monitoring of tumor progression are essential for reducing GC disease burden and mortality.The current widespread use of semi-invasive endoscopic methods and radiologic approaches has increased the number of treatable cancers:However,these approaches are invasive,costly,and time-consuming.Thus,novel molecular noninvasive tests that detect GC alterations seem to be more sensitive and specific compared to the current methods.Recent technological advances have enabled the detection of blood-based biomarkers that could be used as diagnostic indicators and for monitoring postsurgical minimal residual disease.These biomarkers include circulating DNA,RNA,extracellular vesicles,and proteins,and their clinical applications are currently being investigated.The identification of ideal diagnostic markers for GC that have high sensitivity and specificity would improve survival rates and contribute to the advancement of precision medicine.This review provides an overview of current topics regarding the novel,recently developed diagnostic markers for GC.

Advancements in Barrett's esophagus detection:The role of artificial intelligence and its implications

Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utilized endoscopic images to train an AI model,challenging the traditional distinction between endoscopic and histological BE.This approach yielded remarkable results,with the AI system achieving an accuracy of 94.37%,sensitivity of 94.29%,and specificity of 94.44%.The study's extensive dataset enhances the AI model's practicality,offering valuable support to endoscopists by minimizing unnecessary biopsies.However,questions about the applicability to different endoscopic systems remain.The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.

Novel automated non-invasive detection of ocular surface squamous neoplasia using artificial intelligence

Ocular surface squamous neoplasia(OSSN)is a common eye surface tumour,characterized by the growth of abnormal cells on the ocular surface.OSSN includes invasive squamous cell carcinoma(SCC),in which tumour cells penetrate the basement membrane and infiltrate the stroma,as well as non-invasive conjunctival intraepithelial neoplasia,dysplasia,and SCC in-situ thereby presenting a challenge in early detection and diagnosis.Early identification and precise demarcation of the OSSN border leads to straightforward and curative treatments,such as topical medicines,whereas advanced invasive lesions may need orbital exenteration,which carries a risk of death.Artificial intelligence(AI)has emerged as a promising tool in the field of eye care and holds potential for its application in OSSN management.AI algorithms trained on large datasets can analyze ocular surface images to identify suspicious lesions associated with OSSN,aiding ophthalmologists in early detection and diagnosis.AI can also track and monitor lesion progression over time,providing objective measurements to guide treatment decisions.Furthermore,AI can assist in treatment planning by offering personalized recommendations based on patient data and predicting the treatment response.This manuscript highlights the role of AI in OSSN,specifically focusing on its contributions in early detection and diagnosis,assessment of lesion progression,treatment planning,telemedicine and remote monitoring,and research and data analysis.

Clustering-Aided Supervised Malware Detection with Specialized Classifiers and Early Consensus

One of the most common types of threats to the digital world is malicious software.It is of great importance to detect and prevent existing and new malware before it damages information assets.Machine learning approaches are used effectively for this purpose.In this study,we present a model in which supervised and unsupervised learning algorithms are used together.Clustering is used to enhance the prediction performance of the supervised classifiers.The aim of the proposed model is to make predictions in the shortest possible time with high accuracy and f1 score.In the first stage of the model,the data are clustered with the k-means algorithm.In the second stage,the prediction is made with the combination of the classifier with the best prediction performance for the related cluster.While choosing the best classifiers for the given clusters,triple combinations of ten machine learning algorithms(kernel support vector machine,k-nearest neighbor,naive Bayes,decision tree,random forest,extra gradient boosting,categorical boosting,adaptive boosting,extra trees,and gradient boosting)are used.The selected triple classifier combination is positioned in two stages.The prediction time of the model is improved by positioning the classifier with the slowest prediction time in the second stage.The selected triple classifier combination is positioned in two tiers.The prediction time of the model is improved by positioning the classifier with the highest prediction time in the second tier.It is seen that clustering before classification improves prediction performance,which is presented using Blue Hexagon Open Dataset for Malware Analysis(BODMAS),Elastic Malware Benchmark for Empowering Researchers(EMBER)2018 and Kaggle malware detection datasets.The model has 99.74%accuracy and 99.77%f1 score for the BODMAS dataset,99.04%accuracy and 98.63%f1 score for the Kaggle malware detection dataset,and 96.77%accuracy and 96.77%f1 score for the EMBER 2018 dataset.In addition,the tiered positioning of classifiers shortened the average prediction time by 76.13%for the BODMAS dataset and 95.95%for the EMBER 2018 dataset.The proposed method’s prediction performance is better than the rest of the studies in the literature in which BODMAS and EMBER 2018 datasets are used.

Autonomous machine learning for early bot detection in the internet of things

The high costs incurred due to attacks and the increasing number of different devices in the Internet of Things(IoT)highlight the necessity of the early detection of botnets(i.e.,a network of infected devices)to gain an advantage against attacks.However,early botnet detection is challenging because of continuous malware mutations,the adoption of sophisticated obfuscation techniques,and the massive volume of data.The literature addresses botnet detection by modeling the behavior of malware spread,the classification of malicious traffic,and the analysis of traffic anomalies.This article details ANTE,a system for ANTicipating botnEt signals based on machine learning algorithms.The system adapts itself to different scenarios and detects different types of botnets.It autonomously selects the most appropriate Machine Learning(ML)pipeline for each botnet and improves the classification before an attack effectively begins.The system evaluation follows trace-driven experiments and compares ANTE results to other relevant results from the literature over four representative datasets:ISOT HTTP Botnet,CTU-13,CICDDoS2019,and BoT-IoT.Results show an average detection accuracy of 99.06%and an average bot detection precision of 100%.

Diagnostic value of matrix metalloproteinases 2, 7 and 9 in urine for early detection of colorectal cancer

BACKGROUND A noninvasive biomarker with high diagnostic performance is urgently needed for the early diagnosis of colorectal cancer(CRC).AIM To evaluate the diagnostic value of matrix metalloproteinases(MMPs)2,7 and 9 in urine for CRC.METHODS Of 59 healthy controls,47 patients with colon polyps and 82 patients with CRC were included in this study.Carcinoembryonic antigen(CEA)in serum and MMP2,MMP7,and MMP9 in urine were detected.The combined diagnostic model of the indicators was established by binary logistic regression.The receiver operating characteristic curve(ROC)of the subjects was used to evaluate the independent and combined diagnostic value of the indicators.RESULTS The MMP2,MMP7,MMP9,and CEA levels in the CRC group differed significantly from levels in the healthy controls(P<0.05).The levels of MMP7,MMP9,and CEA also differed significantly between the CRC group and the colon polyps group(P<0.05).The area under the curve(AUC)distinguishing between the healthy control and the CRC patients using the joint model with CEA,MMP2,MMP7 and MMP9 was 0.977,and the sensitivity and specificity were 95.10%and 91.50%,respectively.For early-stage CRC,the AUC was 0.975,and the sensitivity and specificity were 94.30%and 98.30%,respectively.For advanced stage CRC,the AUC was 0.979,and the sensitivity and specificity were 95.70%and 91.50%,respectively.Using CEA,MMP7 and MMP9 to jointly established a model distinguishing the colorectal polyp group from the CRC group,the AUC was 0.849,and the sensitivity and specificity were 84.10%and 70.20%,respectively.For early-stage CRC,the AUC was 0.818,and the sensitivity and specificity were 76.30%and 72.30%,respectively.For advanced stage CRC,the AUC was 0.875,and the sensitivity and specificity were 81.80%and 72.30%,respectively.CONCLUSION MMP2,MMP7 and MMP 9 may exhibit diagnostic value for the early detection of CRC and may serve as auxiliary diagnostic markers for CRC.

Epidemic trend,screening,and early detection and treatment of cancer in Chinese population

Epidemiological studies showed the incidence mortality rates of cancer were increasing in recent decades in Chinese population.National and regional preventive programs aim to reduce the health hazards of cancer and focuse the population at high risks for specific cancer, particularly in rural areas and to offer the access to early detection for multlple cancers in urban areas. The early screening, early detection and treatment have been put into operation for the population at risks in rural areas at first, and in the urban areas in recent years. To understand the epidemic patterns and trends of cancer, and the experiences in applying early detection strategies in China, selected literatures were reviewed for brief summary.

Multiple cytokine profiling in serum for early detection of gastric cancer

AIM To investigate the value of multiparameter joint analysis in the early diagnosis of gastric cancer(GC) in clinical practice.METHODS Concentrations of CEA, CA724 and three kinds of cytokines(TNF-α, IL-6 and IL-8) in 176 GC patients, 117 atypical hyperplasia patients, and 204 healthy control individuals were used for building the diagnostic model, then 58 GC patients, 41 atypical hyperplasia patients, and 66 healthy control individuals were enrolled independently. The joints of the indicators were analyzed by binary logistic regression analysis method.RESULTS For discriminating the healthy control group and the GC group, IL-6 had the best diagnostic value, and the area under curve(AUC) of joint analysis was 0.95(0.93-0.97). For the early stage and advanced stage GC, the AUC were 0.95(0.92-0.98) and 0.95(0.92-0.97). For discriminating the atypical hyperplasia group and GC group, CA724 had the best diagnostic value, and the AUC of joint analysis was 0.97(0.95-0.99). For the early stage and advanced stage GC groups, the AUC were 0.98(0.96-0.99) and 0.96(0.94-0.98). After evaluation, for discriminating the GC, early stage GC and advanced cancer group from the healthy control group, the diagnostic sensitivity was 89.66%, 84.21% and 92.31%, respectively, and the specificity was 92.42%, 90.91% and 90.91%. For discriminating the GC, early stage GC and advanced cancer groups from the atypical hyperplasia group, the diagnostic sensitivity was 87.93%, 78.95% and 92.31%, respectively, and the specificity was 87.80%, 85.37% and 90.24%.CONCLUSION We have built a diagnostic model including CEA, CA724, IL-6, IL-8, and TNF-α. It may provide potential assistance as a screening method for the early detection of GC.

Advance in plasma SEPT9 gene methylation assay for colorectal cancer early detection

This review article summarizes the research advances of the plasma-based SEPT9 gene methylation assay for the clinical detection of colorectal cancer and its limitations. Colorectal cancer is a common malignancy with a poor prognosis and a high mortality, for which early detection and diagnosis are particularly crucial for the high-risk groups. Increasing evidence supported that SEPT9 gene methylation is associated with the pathogenesis of colorectal cancer and that detecting the level of methylation of SEPT9 in the peripheral blood can be used for screening of colorectal cancer in susceptible populations. In recent years, the data obtained in clinical studies demonstrated that the SEPT9 gene methylation assay has a good diagnostic performance with regard to both sensitivity and specificity with the advantage of better acceptability, convenience and compliance with serological testing compared with fecal occult blood tests and carcinoembryonic antigen for colorectal cancer(CRC). Furthermore, the combination of multiple methods or markers has become a growing trend for CRC detection and screening. Nevertheless, the clinical availability of the methylated SEPT9 assay is still limited because of the large degree of sample heterogeneity caused by demographic characteristics, pathological features, comorbidities and/or technique selection. Another factor is the cost-effectiveness of colorectal cancer screening strategies that hinders its large-scale application. In addition, improvements in its accuracy in detecting adenomas and premalignant polyps are required.

Advances in pancreatic cancer research: Moving towards early detection

Pancreatic ductal adenocarcinoma(PDAC) is one of the most lethal forms of cancer. Substantial progress has been made in the understanding of the biology of pancreatic cancer, and advances in patient management have been significant. However, most patients(nearly80%) who present with locally advanced or metastatic disease have an extremely poor prognosis. Survival is better for those with malignant disease localized to the pancreas, because surgical resection at present offers the only chance of cure. Therefore, the early detection of pancreatic cancer may benefit patients with PDAC.However, its low rate of incidence and the limitations of current screening strategies make early detection difficult. Recent advances in the understanding of the pathogenesis of PDAC suggest that it is possible to detect PDAC in early stages and even identify precursor lesions. The presence of new-onset diabetes mellitus in the early phase of pancreatic cancer may provide cluesfor its early diagnosis. Advances in the identification of novel circulating biomarkers including serological signatures, autoantibodies, epigenetic markers, circulating tumor cells and microRNAs suggest that they can be used as potential tools for the screening of precursors and early stage PDAC in the future. However, proper screening strategies based on effective screening methodologies need to be tested for clinical application.

Strategies for early detection of resectable pancreatic cancer

Pancreatic cancer is difficult to diagnose at an early stage and generally has a poor prognosis. Surgical resection is the only potentially curative treatment for pancreatic carcinoma. To improve the prognosis of this disease, it is essential to detect tumors at early stages, when they are resectable. The optimal approach to screening for early pancreatic neoplasia has not been established. The International Cancer of the Pancreas Screening Consortium has recently finalized several recommendations regarding the management of patients who are at an increased risk of familial pancreatic cancer. In addition, there have been notable advances in research on serum markers, tissue markers, gene signatures, and genomic targets of pancreatic cancer. To date, however, no biomarkers have been established in the clinical setting. Advancements in imaging modalities touch all aspects of the clinical management of pancreatic diseases, including the early detection of pancreatic masses, their characterization, and evaluations of tumor resectability. This article reviews strategies for screening high-risk groups, biomarkers, and current advances in imaging modalities for the early detection of resectable pancreatic cancer.

Multi-parameter gene expression profiling of peripheral blood for early detection of hepatocellular carcinoma

AIM In our previous study, we have built a nine-gene(GPC3, HGF, ANXA1, FOS, SPAG9, HSPA1 B, CXCR4, PFN1, and CALR) expression detection system based on the Ge XP system. Based on peripheral blood and Ge XP, we aimed to analyze the results of genes expression by different multi-parameter analysis methods and build a diagnostic model to classify hepatocellular carcinoma(HCC) patients and healthy people.METHODS Logistic regression analysis, discriminant analysis, classification tree analysis, and artificial neural network were used for the multi-parameter gene expression analysis method. One hundred and three patients with early HCC and 54 age-matched healthy normal controls were used to build a diagnostic model. Fiftytwo patients with early HCC and 34 healthy people were used for validation. The area under the curve, sensitivity, and specificity were used as diagnostic indicators.RESULTS Artificial neural network of the total nine genes had the best diagnostic value, and the AUC, sensitivity, and specificity were 0.943, 98%, and 85%, respectively. At last, 52 HCC patients and 34 healthy normal controls were used for validation. The sensitivity and specificity were 96% and 86%, respectively.CONCLUSION Multi-parameter analysis methods may increase the diagnostic value compared to single factor analysis and they may be a trend of the clinical diagnosis in the future.

Advances in lung cancer screening and early detection

Lung cancer is associated with a heavy cancer-related burden in terms of patients’physical and mental health worldwide.Two randomized controlled trials,the US-National Lung Screening Trial(NLST)and Nederlands-Leuvens Longkanker Screenings Onderzoek(NELSON),indicated that low-dose CT(LDCT)screening results in a statistically significant decrease in mortality in patients with lung cancer,LDCT has become the standard approach for lung cancer screening.However,many issues in lung cancer screening remain unresolved,such as the screening criteria,high false-positive rate,and radiation exposure.This review first summarizes recent studies on lung cancer screening from the US,Europe,and Asia,and discusses risk-based selection for screening and the related issues.Second,an overview of novel techniques for the differential diagnosis of pulmonary nodules,including artificial intelligence and molecular biomarker-based screening,is presented.Third,current explorations of strategies for suspected malignancy are summarized.Overall,this review aims to help clinicians understand recent progress in lung cancer screening and alleviate the burden of lung cancer.