Diagnosis Of Esophageal Cancer Research Articles

Exploratory Study of a New Technique of Pixelated Chromoendoscopy in the Diagnosis of Early Esophageal Cancer.

Chromoendoscopy is an effective method for early screening of esophageal cancer, but diagnosis can depend on subjective judgment. The study aimed to explore a new technique of pixelated chromoendoscopy in the diagnosis of early esophageal cancer. The study included patients with symptoms of esophageal cancer who attended Jiangyin People's Hospital between January 2015 and July 2021. Chromoendoscopy was performed on each patient. The images then underwent digital analysis; the lesion area (the sensitive region) was pixelated by dividing it into the smallest image unit and the red, green, and blue color components. The diagnostic performance of pixelated chromoendoscopy was evaluated by calculating the area under the receiver operating characteristic. The study finally enrolled 86 patients (aged 51.34 ± 5.82y), including 54 males and 32 females. Pathologic diagnosis identified 54 cases in the cancer group and 32 cases in the non-cancer group. Traditional judgment had a diagnostic sensitivity of 70.73% and specificity was 75.00%. Pixelated chromoendoscopy sensitivity was 80.49%, and specificity was 83.33%. The area under the receiver operating characteristic was 0.814, at a cutoff value of 0.625, indicating a good prediction effect. These results showed that pixelated chromoendoscopy might improve the rate of esophageal cancer diagnoses from early screening.

Revolutionizing healthcare by use of artificial intelligence in esophageal carcinoma - a narrative review.

Esophageal cancer is a major cause of cancer-related mortality worldwide, with significant regional disparities. Early detection of precursor lesions is essential to improve patient outcomes. Artificial intelligence (AI) techniques, including deep learning and machine learning, have proved to be of assistance to both gastroenterologists and pathologists in the diagnosis and characterization of upper gastrointestinal malignancies by correlating with the histopathology. The primary diagnostic method in gastroenterology is white light endoscopic evaluation, but conventional endoscopy is partially inefficient in detecting esophageal cancer. However, other endoscopic modalities, such as narrow-band imaging, endocytoscopy, and endomicroscopy, have shown improved visualization of mucosal structures and vasculature, which provides a set of baseline data to develop efficient AI-assisted predictive models for quick interpretation. The main challenges in managing esophageal cancer are identifying high-risk patients and the disease's poor prognosis. Thus, AI techniques can play a vital role in improving the early detection and diagnosis of precursor lesions, assisting gastroenterologists in performing targeted biopsies and real-time decisions of endoscopic mucosal resection or endoscopic submucosal dissection. Combining AI techniques and endoscopic modalities can enhance the diagnosis and management of esophageal cancer, improving patient outcomes and reducing cancer-related mortality rates. The aim of this review is to grasp a better understanding of the application of AI in the diagnosis, treatment, and prognosis of esophageal cancer and how computer-aided diagnosis and computer-aided detection can act as vital tools for clinicians in the long run.

Diagnostic Accuracy Of Barium Swallow For Dysphagia, Keeping Rigid Esophagoscopy As The Gold Standard

Objective: To determine the diagnostic accuracy of Barium Swallow in detection of patients presenting with dysphagia.
 Study Design: Cross-sectional validation study.
 Study Setting & Duration: Department of Otorhinolaryngology, Head & Neck Surgery, District Headquarter Hospital Rawalpindi from 01-09- 2022 to 01-03-2023.
 Materials and Methods: Approval of the study was obtained from the Hospital Ethical Committee. A total of 111 patients both male and female patients were selected. The patients suffering from dysphagia as per operational definitions and who have reported for work-up to the Department of ENT, District Headquarters Hospital, Rawalpindi, and fulfill the complete inclusion and exclusion criteria, were selected. Informed consent was obtained from all the patients. Patients were selected by consecutive non-probability sampling technique. The data was analyzed using SPSS 24.
 Results: A total of 111 patients were included in this study. The mean age of these patients was 50.79 ± 13.01 years, ranging from 28 to 70 years. The frequency distribution of females 70.27 % was found to be more than that of males 29.73 %. Majority of patients' barium swallow (74.77%) revealed pathologies, while only a small percentage of patients (25.23%) had normal barium swallow. Most of patients (87.39%) had pathologies found during rigid esophagoscopy, while just a small number (12.61%) had normal rigid esophagoscopy. Comparing both investigating tools, esophagoscopy discovered 87.39% of pathologies while Barium swallow detected 74.77%, indicating that esophagoscopy was a more accurate procedure. Patients had esophageal web 55 (25.2%) on barium swallow and 69 (62.2%) on the Rigid esophagoscopy. Barium Swallow had esophageal stricture 28 (52.2) and no Pathology was detected in 28 (25.2 %) patients. As well as Rigid esophagoscopy had esophageal growth13 (11.7), esophageal stricture 15 (13.5), and no Pathology was detected in 14 (12.6 %). Rigid esophagoscopy is more efficient in detecting esophageal pathology than Barium Swallow. In Barium swallows most patients had esophageal web 55 (25.2%) than the esophageal stricture 28 (52.2) and no pathology was detected 28 (25.2). In rigid esophagoscopy most patients had esophageal web 69 (62.2%) than the esophageal growth13 (11.7), esophageal stricture 15 (13.5) and no pathology detected 14 (12.6).
 Conclusion: A range of diseases are associated with dysphagia can be found in patients. Two often used diagnostic methods are barium swallow and rigid esophagoscopy. Both Barium swallow and Rigid esophagoscopy are successful in the diagnosis of esophageal cancer. The use of a Rigid esophagoscopy is still a gold standard diagnostic and therapeutic tool for upper aerodigestive tract pathologies.

Chinese Guidelines on Perioperative Management of Resectable Esophageal Cancer (2023 edition)

The standardization of surgical treatment has become indispensable in the overall outcome of esophageal cancer care in China, but there is a notable deficiency of clinical guidelines regarding esophageal surgery. The Chinese National Cancer Center organized esophageal surgery experts from the Chinese Association of Thoracic Surgeons (CATS), Chinese Society for Thoracic and Cardiovascular Surgery (CSTCVS), and Chinese Society for Disease of the Esophagus (CSDE) to develop and establish the Chinese Guidelines on Perioperative Management of Resectable Esophageal Cancer (2023 edition). This guideline comprehensively covers all aspects of surgical treatment for esophageal cancer, including conceptual clarification, preoperative diagnosis, clinical staging, indications for surgery, multidisciplinary comprehensive treatment, surgical treatment modalities, and perioperative management. A total of 39 critical clinical questions are categorized into six domains above. This guideline follows the globally accepted approach for developing recommendations and takes into account the most recent research evidence as well as China's unique national circumstances. Its main aim is to serve as a comprehensive resource for standardizing the diagnosis and surgical treatment of esophageal cancer in China, with the ultimate goal of substantially improving the overall outcomes.

Development and validation of a novel risk prediction algorithm to estimate 10-year risk of oesophageal cancer in primary care: prospective cohort study and evaluation of performance against two other risk prediction models.

Methods to identify patients at increased risk of oesophageal cancer are needed to better identify those fortargeted screening. We aimed to derive and validate novel risk prediction algorithms (CanPredict) to estimate the 10-year risk of oesophageal cancer and evaluate performance against two other risk prediction models. Prospective open cohort study using routinely collected data from 1804 QResearch® general practices. We used 1354 practices (12.9M patients) to develop the algorithm. We validated the algorithm in 450 separate practices from QResearch (4.12M patients) and 355 Clinical Practice Research Datalink (CPRD) practices (2.53M patients). The primary outcome was an incident diagnosis of oesophageal cancer found in GP, mortality, hospital, or cancer registry data. Patients were aged 25-84 years and free of oesophageal cancer at baseline. Cox proportional hazards models were used with prediction selection to derive risk equations. Risk factors included age, ethnicity, Townsend deprivation score, body mass index (BMI), smoking, alcohol, family history, relevant co-morbidities and medications. Measures of calibration, discrimination, sensitivity, and specificity were calculated in the validation cohorts. There were 16,384 incident cases of oesophageal cancer in the derivation cohort (0.13% of 12.9M). The predictors in the final algorithms were: age, BMI, Townsend deprivation score, smoking, alcohol, ethnicity, Barrett's oesophagus, hiatus hernia, H.pylori infection, use of proton pump inhibitors, anaemia, lung and blood cancer (with breast cancer in women). In the QResearch validation cohort in women the explained variation (R2) was 57.1%; Royston's D statistic 2.36 (95% CI 2.26-2.46); C statistic 0.859 (95% CI 0.849-0.868) and calibration was good. Results were similar in men. For the 20% at highest predicted risk, the sensitivity was 76%, specificity was 80.1% and the observed risk at 10 years was 0.76%. The results from the CPRD validation were similar. We have developed and validated a novel prediction algorithm to quantify the absolute risk of oesophageal cancer. The CanPredict algorithms could be used to identify high risk patients for targeted screening. Innovate UK and CRUK (grant 105857).

Identification of hub genes and microRNAs with prognostic values in esophageal cancer by integrated analysis

Esophageal cancer (EC) is the eighth most common cancer in the world, and the sixth most common cause of cancer-related mortality. The aim of the present study was to identify cell and molecular mechanisms involved in EC, and to provide the potential targets for diagnosis and treatment. Here, a microarray dataset (GSE20347) was screened to find differentially expressed genes (DEGs). Different bioinformatic methods were used to analyze the identified DEGs. The up-regulated DEGs were significantly involved in different biological processes and pathways including extracellular matrix organization and ECM-receptor interaction. FN1, CDK1, AURKA, TOP2A, FOXM1, BIRC5, CDC6, UBE2C, TTK, and TPX2 were identified as the most important genes among the up-regulated DEGs. Our analysis showed that has-miR-29a-3p, has-miR-29b-3p, has-miR-29c-3p, and has-miR-767–5p had the largest number of common targets among the up-regulated DEGs. These findings strengthen the understanding of EC development and progression, as well as representing potential markers for EC diagnosis and treatment.

DESIGN AND ANALYSIS OF IODINE STAINING AND NBI IN THE DIAGNOSTIC MODEL AND DIAGNOSIS OF EARLY ESOPHAGEAL CANCER AND ITS PRECANCEROUS LESIONS

Objective: This study aimed to compare the differences between narrowband imaging (NBI) and Lugo’s iodine staining endoscopy (LCE) for detecting and elucidating the site, clarity, and diagnostic accuracy of early esophageal cancer and precancerous lesions. Methods: We included patients with a high risk of developing esophageal cancer who visited the Zhongjiang County People’s Hospital between October 2020 and October 2022. Endoscopic examination was performed on each study participant. White-light endoscopy was used to observe and locate the diseased mucosa, after which NBI mode and LCE staining were used to observe the boundary between the diseased and normal mucosa. Abnormal lesions were found, and biopsies were performed on the identifiable diseased parts for pathological examination. Inflammation, LGIN, HGIN, and early esophageal cancer were diagnosed, and the differences between them were compared in terms of the two enhanced-image endoscopy techniques. A semantic segmentation model based on deep learning was adopted to assist in the diagnosis of early esophageal cancer and accurately locate cancerous areas. In order to improve its accurate diagnostic rate, we also built a semantic segmentation network model to assist in the computer-aided diagnosis of early esophageal cancer. Results: A total of 69 cases of early esophageal cancer were included in this study. The patients were aged 40–75 years, with an average age of [Formula: see text] years. Most early esophageal cancer lesions were located in the middle of the esophagus in 45/69 (65.2%) cases. LCE obtained higher clarity of lesion boundaries than NBI (59.4% versus 45.0% and 27.7% versus 15.9%, respectively), and the proportion of unclear lesions was lower (8.6% versus 21.7% and 4.3% versus 17.4%, respectively; [Formula: see text]). For early esophageal cancer and precancerous lesions, the missed-diagnosis rate of white-light endoscopy was 20%, that of NBI was 3.15%, and that of LCE was 0%, with statistical significance ([Formula: see text]). The rate of missed diagnosis of LGIN (three cases) by NBI was 3.15%, which was not significantly different from that of LCE ([Formula: see text]). We found that the lesion area could be more accurately determined using deep learning models to segment NBI images. By constructing a deep learning model for the diagnosis and classification of esophageal cancer, its diagnostic rate rose to 99.5%. Conclusion: The age range of patients with early esophageal cancer in Zhongjiang County, Sichuan Province, was 40–75 years, and tumors mostly occurred in the middle of the esophagus. The boundary clarity of early esophageal carcinomas was higher on LCE than that on NBI. The diagnostic accuracies of NBI and LCE are much higher than those of conventional white-light endoscopy. Therefore, both LCE and NBI are helpful for detecting early esophageal cancer and precancerous lesions. Moreover, the diagnostic rate of clinical esophageal cancer can be effectively improved using a dedicated deep learning network model.

406. A SYSTEMATIC REVIEW AND META-ANALYSIS ON THE USE OF ARTIFICIAL INTELLIGENCE IN THE ENDOSCOPIC DIAGNOSIS OF ESOPHAGEAL CANCER

Abstract Introduction Endoscopy plays a key role in the surveillance of patients with Barrett’s esophagus and the diagnosis of esophageal cancer. The subtleties of endoscopic interpretation are subject to clinical expertise, diagnostic skill, and thus human error. Therefore, artificial intelligence (AI) is increasingly being incorporated into endoscopy in order to improve diagnostic accuracy and early detection. This systematic review and meta-analysis consolidates the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. Methods The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies which described the role of AI in supporting endoscopic video or image diagnosis of esophageal cancer were included. A meta-analysis was also performed. Results 45 studies formed the qualitative and quantitative review. 14 studies with 1590 patients in total assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma (ESCC)- the pooled sensitivity and specificity were 91.2% (84.3–95.2%) and 80% (64.3–89.9%). A further, 9 studies consisting of 478 patients overall explored the capabilities of AI in supporting the endoscopic diagnosis of esophageal adenocarcinoma (EAC), with a pooled sensitivity and specificity of 93.1% (86.8–96.4%) and 86.9% (81.7–90.7%). AI was often superior to endoscopists when diagnosing ESCC and improved the overall accuracy of both novice and expert endoscopists. Conclusion AI technology, as an adjunct to endoscopy has proven to be beneficial in early, accurate detection of esophageal malignancy. Multiple studies have shown superior diagnostic capabilities when comparing AI technology to endoscopists alone. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.

Application of novel non-endoscopic device in the screening and early diagnosis of esophageal cancer

Esophageal cancer (EC) is a dreadful disease with a poor prognosis and poses heavy health burden worldwide. Developing effective methods to identify high-risk individuals is urgently needed for preliminary screening before endoscopy. The novel non-endoscopic device has the potential advantages of low cost, simple operation, and minimal invasiveness. Approximately 90% of participants can swallow the device successfully with high safety profiles, and sufficient esophageal exfoliated cells can be collected for cytological examination and biomarker detection. Cytological examination based on the device combined with trefoil factor 3 (TFF3) protein or DNA methylation examinations could effectively screen Barrett's esophagus-associated dysplasia and early esophageal adenocarcinoma, but large prospective studies are needed to further validate the diagnostic value of this device to improve the quality of evidence. Although the device-based cytological examination in combination with biomarker detection holds promise in the early screening of esophageal squamous dysplasia and early esophageal squamous cell carcinoma, related research is still in its infancy, and there is still a lack of sufficient evidence for population screening in China. Active research into the application of this novel non-endoscopic device in EC screening and early diagnosis is of great significance for optimizing EC screening strategies and improving the early diagnosis of EC.

Trends in Epidemiology of Esophageal Cancer in the US, 1975-2018

Esophageal cancer (EC) is the 7th most common cancer worldwide and 14th in the US. More data are needed to study the changing incidence patterns of its 2 primary histologic subtypes, squamous cell carcinoma of the esophagus (SCE) and adenocarcinoma of the esophagus (ACE). To examine temporal trends in incidence rates of EC, ACE, and SCE from 1975 through 2018. In this population-based cross-sectional study, data were derived from 9 Surveillance, Epidemiology, and End Results (SEER) registries from January 1975 through December 2018 and from all 21 registries for January 2000 through December 2018 for patients with a diagnosis of EC from 1975 through 2018 (International Classification of Disease-Oncology, Third Edition codes). Age-adjusted incidence rates (AAIRs) of EC, ACE, and SCE were calculated. The timing and magnitude of the annual percentage change (APC) in incidence were examined using Joinpoint regression analyses. Data analysis was started in 2021 and updated and completed in 2023. The APC for age-adjusted EC incidence rates as stratified by histology, anatomical location, stage, sex, age, race and ethnicity, and geographic region. A total of 47 648 patients with a diagnosis of EC were retained for analysis. These included 22 419 (47.1%) with a diagnosis of SCE, 22 217 (46.6%) with ACE, and 3012 (6.3%) with other subtypes. The AAIR for EC changed from 4.14 per 100 000 population in 1975 to 4.18 in 2018, AAIRs of SCE declined from 3.06 in 1975 to 1.15 in 2018 as well as for ACE, and AAIRs increased from 0.42 in 1975 to 2.78 in 2018. From 1975 through 2004, EC incidence significantly increased (APC, 0.53; 95% CI, 0.4 to 0.7) but significantly decreased (APC, -1.03; 95% CI, -1.3 to -0.7) from then until 2018. The APC of SCE significantly continued to decline (-2.80, 95% CI, -3.0 to -2.6), and ACE increased from 2000 to 2006 (APC, 2.51; 95% CI, 1.0 to 4.0) but has since stabilized from 2006 to 2018. The results of this cross-sectional study suggest that the incidence of EC modestly declined since 2004 and that the incidence of SCE continued to decline while the incidence rate of ACE plateaued for more than a decade. Understanding factors associated with plateaued rates of ACE may help inform public health interventions.

Poly(A)-specific ribonuclease protein promotes the proliferation, invasion and migration of esophageal cancer cells.

Bioinformatics analysis showed that the expression of the poly(A)-specific ribonuclease (PARN) gene in gastric cancer, head and neck squamous cell carcinoma, melanoma, cervical cancer and lung squamous cell carcinoma tissues was significantly higher than that in normal tissues and was associated with high stage and poor prognosis. The expression of the PARN gene in esophageal cancer (EC) tissue is also significantly higher than that in normal tissues, but the effect of PARN on the proliferation, migration and invasion of EC cells remains unclear. To investigate the relationship between PARN and the proliferation, migration and invasion of EC cells. The EC tissues of 91 patients after EC surgery and 63 paired precancerous healthy tissues were collected. PARN mRNA levels were measured using a tissue microarray, and the PARN expression level was evaluated using immunohistochemistry to analyze the relationship between PARN expression and clinicopathologic features as well as the survival and prognosis of patients. In addition, the effects of PARN gene knockout on tumor cell proliferation, invasion and migration were studied by using shRNA during the in vitro culture of EC cell lines Eca-109 and TE-1, and the effects of the PARN gene on tumor growth in vivo were verified by a xenotransplantation nude mice model. The expression of PARN in EC tissues was higher than that in adjacent normal tissues, and the level of PARN expression was significantly positively correlated with lymphatic metastasis. Patients with high PARN levels had poor overall survival. BIM, IGFBP-5 and p21 levels were significantly increased in the PARN knockout group, while the expression levels of the antiapoptotic proteins Survivin and sTNF-R1 were significantly decreased in the apoptotic antibody array data. In addition, the expression levels of Akt, p-Akt, PIK3CA and CCND1 in the downstream signaling pathway regulating EC progression were significantly decreased. The culture of EC cell lines confirmed that the apoptosis rate of EC cells was significantly increased, the growth and proliferation of tumor cells were significantly inhibited, and the invasion and migration ability of tumor cells were significantly decreased after PARN gene knockout. In vivo experiments of BALB/c nude mice transfected with Eca-109 cells expressing control shRNA (sh-NC) and PARN shRNA (sh-PARN) showed that the tumor volume and weight of nude mice treated with sh-PARN were significantly decreased compared with those of nude mice treated with sh-NC, indicating that PARN knockdown significantly inhibited tumor growth in vivo. PARN has antiapoptotic effects on EC cells and promotes their proliferation, invasion and migration, which is associated with the development of EC and poor patient prognosis. PARN may become a potential target for the diagnosis, prognosis prediction and treatment of EC.

Artificial intelligence in esophageal cancer diagnosis and treatment: where are we now?-a narrative review.

Artificial intelligence (AI) use is becoming increasingly prevalent directly or indirectly in daily clinical practice, including esophageal cancer (EC) diagnosis and treatment. Although the limits of its adoption and their clinical benefits are still unknown, any physician related to EC patients' management should be aware of the status and future perspectives of AI use in their field. The purpose of this review is to summarize the existing literature regarding the role of AI in diagnosis and treatment of EC. We have focused on the aids AI entails in the management of this pathology and we have tried to offer an updated perspective to maximize current applications and to identify potential future uses of it. Data concerning AI applied to EC diagnosis and treatment is not limited, including direct (those specifically related to them) and indirect (those referring to other specialties as radiology or pathology), applications. However, the clinical relevance of the discussed and presented models is still unknown. We performed a research in PubMed of English and Spanish written studies from January 1970 to June 2022. Information regarding the role of AI in EC diagnosis and treatment has increased exponentially in recent years. Several models, including different variables and features have been investigated and some of them internally and externally validated. However, the main challenge remains to apply and introduce all these data into clinical practice, and, as some of the discussed studies argue, if the models are able to enhance experienced endoscopists' judgement. Although AI use is increasing steadily in different medical specialties, the truth is, most of the time, the gap between model development and clinical implementation is not closed. Learning to understand the routinely application of AI, as well as future improvements, would lead to a broadened adoption. Physicians should be aware of the multiple current clinical uses of AI in EC diagnosis and treatment and should take part in their clinical application and future developments to enhance patient care.

Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging.

One of the leading causes of cancer deaths is esophageal cancer (EC) because identifying it in early stage is challenging. Computer-aided diagnosis (CAD) could detect the early stages of EC have been developed in recent years. Therefore, in this study, complete meta-analysis of selected studies that only uses hyperspectral imaging to detect EC is evaluated in terms of their diagnostic test accuracy (DTA). Eight studies are chosen based on the Quadas-2 tool results for systematic DTA analysis, and each of the methods developed in these studies is classified based on the nationality of the data, artificial intelligence, the type of image, the type of cancer detected, and the year of publishing. Deeks' funnel plot, forest plot, and accuracy charts were made. The methods studied in these articles show the automatic diagnosis of EC has a high accuracy, but external validation, which is a prerequisite for real-time clinical applications, is lacking.

Progress of magnetic resonance imaging radiomics in preoperative lymph node diagnosis of esophageal cancer.

Esophageal cancer, also referred to as esophagus cancer, is a prevalent disease in the cardiothoracic field and is a leading cause of cancer-related mortality in China. Accurately determining the status of lymph nodes is crucial for developing treatment plans, defining the scope of intraoperative lymph node dissection, and ascertaining the prognosis of patients with esophageal cancer. Recent advances in diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging (MRI) have improved the effectiveness of MRI for assessing lymph node involvement, making it a beneficial tool for guiding personalized treatment plans for patients with esophageal cancer in a clinical setting. Radiomics is a recently developed imaging technique that transforms radiological image data from regions of interest into high-dimensional feature data that can be analyzed. The features, such as shape, texture, and waveform, are associated with the cancer phenotype and tumor microenvironment. When these features correlate with the clinical disease outcomes, they form the basis for specific and reliable clinical evidence. This study aimed to review the potential clinical applications of MRI-based radiomics in studying the lymph nodes affected by esophageal cancer. The combination of MRI and radiomics is a powerful tool for diagnosing and treating esophageal cancer, enabling a more personalized and effectual approach.

Preparing Well for Esophageal Endoscopic Detection Using a Hybrid Model and Transfer Learning.

Early detection of esophageal cancer through endoscopic imaging is pivotal for effective treatment. However, the intricacies of endoscopic diagnosis, contingent on the physician's expertise, pose challenges. Esophageal cancer features often manifest ambiguously, leading to potential confusions with other inflammatory esophageal conditions, thereby complicating diagnostic accuracy. In recent times, computer-aided diagnosis has emerged as a promising solution in medical imaging, particularly within the domain of endoscopy. Nonetheless, contemporary AI-based diagnostic models heavily rely on voluminous data sources, limiting their applicability, especially in scenarios with scarce datasets. To address this limitation, our study introduces novel data training strategies based on transfer learning, tailored to optimize performance with limited data. Additionally, we propose a hybrid model integrating EfficientNet and Vision Transformer networks to enhance prediction accuracy. Conducting rigorous evaluations on a carefully curated dataset comprising 1002 endoscopic images (comprising 650 white-light images and 352 narrow-band images), our model achieved exceptional outcomes. Our combined model achieved an accuracy of 96.32%, precision of 96.44%, recall of 95.70%, and f1-score of 96.04%, surpassing state-of-the-art models and individual components, substantiating its potential for precise medical image classification. The AI-based medical image prediction platform presents several advantageous characteristics, encompassing superior prediction accuracy, a compact model size, and adaptability to low-data scenarios. This research heralds a significant stride in the advancement of computer-aided endoscopic imaging for improved esophageal cancer diagnosis.

The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis.

Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise, diagnostic skill, and thus human error. Artificial intelligence (AI) in endoscopy is increasingly bridging this gap. This systematic review and meta-analysis consolidate the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases and articles on the role of AI in the endoscopic diagnosis of esophageal cancer management were included. A meta-analysis was also performed. Fourteen studies (1590 patients) assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma-the pooled sensitivity and specificity were 91.2% (84.3-95.2%) and 80% (64.3-89.9%). Nine studies (478 patients) assessed AI capabilities of diagnosing esophageal adenocarcinoma with the pooled sensitivity and specificity of 93.1% (86.8-96.4) and 86.9% (81.7-90.7). The remaining studies formed the qualitative summary. AI technology, as an adjunct to endoscopy, can assist in accurate, early detection of esophageal malignancy. It has shown superior results to endoscopists alone in identifying early cancer and assessing depth of tumor invasion, with the added benefit of not requiring a specialized skill set. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.

Nanodrugs systems for therapy and diagnosis of esophageal cancer.

With the increasing incidence of esophageal cancer, its diagnosis and treatment have become one of the key issues in medical research today. However, the current diagnostic and treatment methods face many unresolved issues, such as low accuracy of early diagnosis, painful treatment process for patients, and high recurrence rate after recovery. Therefore, new methods for the diagnosis and treatment of esophageal cancer need to be further explored, and the rapid development of nanomaterials has brought new ideas for solving this problem. Nanomaterials used as drugs or drug delivery systems possess several advantages, such as high drug capacity, adjustably specific targeting capability, and stable structure, which endow nanomaterials great application potential in cancer therapy. However, even though the nanomaterials have been widely used in cancer therapy, there are still few reviews on their application in esophageal cancer, and systematical overview and analysis are deficient. Herein, we overviewed the application of nanodrug systems in therapy and diagnosis of esophageal cancer and summarized some representative case of their application in diagnosis, chemotherapy, targeted drug, radiotherapy, immunity, surgery and new therapeutic method of esophageal cancer. In addition, the nanomaterials used for therapy of esophageal cancer complications, esophageal stenosis or obstruction and oesophagitis, are also listed here. Finally, the challenge and the future of nanomaterials used in cancer therapy were discussed.

Impact of race/ethnicity and socioeconomic status on incident and prevalent esophageal cancer in patients with Barrett's esophagus.

The impact of race/ethnicity (RE) or socioeconomic status (SES) on progression from Barrett's esophagus (BE) to esophageal cancer (EC) is not well established. We aimed to evaluate the association between demographic factors and SES on EC diagnosis in an ethnically diverse BE cohort. Patients aged 18-63 with incident BE diagnosed in October 2015-March 2020 were identified in the Optum Clinformatics DataMart Database. Patients were followed until the diagnosis of prevalent EC <1year or incident EC ≥1year from BE diagnosis, or until the end of their continuous enrollment period. Cox proportional hazards analysis was used to determine associations between demographics, SES factors, BE risk factors, and EC. Demographics of the 12,693 patients included mean age of BE diagnosis 53.0 (SD 8.5) years, 56.4% male, 78.3% White/10.0% Hispanic/6.4% Black/3.0% Asian. The median follow-up was 26.8 (IQR 19.0-42.0) months. In total, 75 patients (0.59%) were diagnosed with EC (46 [0.36%] prevalent EC; 29 [0.23%] incident EC), and 74 patients (0.58%) developed high-grade dysplasia (HGD) (46 [0.36%] prevalent HGD; 28 [0.22%] incident HGD). Adjusted HR (95% CI) for prevalent EC comparing household net worth ≥$150,000 vs. <$150,000 was 0.57 (0.33-0.98). Adjusted HRs (95% CI) for prevalent and incident EC comparing non-White vs. White patients were 0.93 (0.47-1.85) and 0.97 (0.21-3.47), respectively. In summary,a lower SES, captured by the household net worth, was associated with prevalent EC. There was no significant difference in prevalent or incident EC among White vs. non-White patients. EC progression in BE may be similar among racial/ethnic groups, but SES disparities may impact BE outcomes.

The Prognostic Predictors of Airway Stenting in Malignant Airway Involvement From Esophageal Carcinoma.

In locoregional esophageal carcinoma (EC), airway involvement is the most common route of extraesophageal metastasis. The prognosis remains poor even with a multimodality approach. Although airway stenting is well known for restoration of the airway, the survival benefit is still lacking. A total of 37 of patients with airway involvement from EC who underwent airway stenting at a single institution from 2015 to 2020 were retrospectively reviewed. Survival curves after stent placement among different groups were analyzed using Kaplan-Meier method. Of 37 patients, 34 were male, and the mean age was 58.9 years (42 to 80). EC was commonly located at midesophagus (51.4%). The site of airway involvement was left main bronchus (48.6%), trachea (32.4%), multiple sites (16.2%), and right main bronchus (2.7%). The nature of airway involvement was tumor invasion (91.9%), compression (62.2%), and fistula (37.8%). Twenty-three patients (62.2%) had airway involvement at the time of esophageal cancer diagnosis. Only 4 patients underwent esophageal stenting. The median survival time after stent placement was 97 days (5 to 539). Chemotherapy and/or radiotherapy were given before stent placement in 18 patients (48.6%). Treatment-naive before airway stenting and diagnosis of airway involvement at the same time of EC diagnosis were independent predictors for the increased survival after stent placement (P<0.05). Poststent treatment was associated with improved survival (P=0.002). In patients with malignant airway involvement from EC who underwent airway stenting, the prognostic predictors for improved survival were treatment-naive status, receiving treatment after airway stenting, and early-onset of airway involvement.

Relation of Statin Use with Esophageal Cancer.

The present study evaluated the association of long-term statin use with the diagnosis and mortality of esophageal cancer in a Korean population. The Korean National Health Insurance Service-Health Screening Cohort from 2002 to 2019 was enrolled. Esophageal cancer patients were matched with control participants for demographic variables. The statin prescription histories were collected and grouped into <180 days, 180 to 545 days, and >545 days of duration. Propensity score overlap weighting was applied to minimize the bias between the esophageal cancer and control groups. The odds ratios (ORs) of the duration of statin use for esophageal cancer were analyzed using propensity score overlap weighted multivariable logistic regression analysis. The esophageal cancer group was classified as dead and surviving patients, and the ORs of the duration of statin use for the mortality of esophageal cancer were analyzed in an identical manner. Secondary analyses were conducted according to comorbid factors. Patients with esophageal cancer did not show lower odds for the duration of statin prescription in the overall study population (OR = 1.30, 95% CI = 1.03-1.65, p = 0.027 for 180 to 545 days and OR = 1.29, 95% CI = 1.08-1.55, p = 0.006 for >545 days). Subgroups of nonsmokers, past and current smokers, alcohol consumption ≥ 1 time a week, SBP < 140 mmHg and DBP < 90 mmHg, fasting blood glucose ≥ 100 mg/dL, total cholesterol ≥ 200 mg/dL, CCI score = 0, and nondyslipidemia history demonstrated low odds for the duration of statin prescription. Both types of statins, hydrophilic and lipophilic statins, were not related to a lower rate of esophageal cancer. The mortality of esophageal cancer was not associated with the duration of statin prescription. A subgroup with total cholesterol ≥ 200 mg/dL showed lower odds of statin prescription for mortality of esophageal cancer. The duration of statin prescription was not related to a lower rate or mortality of esophageal cancer in the adult Korean population.