Early Detection Of Esophageal Cancer Research Articles

Cancer Methylation Biomarker Detection in an Automated, Portable, Multichannel Magnetofluidic Platform.

Early detection of cancer is critical to improving clinical outcomes, especially in territories with limited healthcare resources. DNA methylation biomarkers have shown promise in early cancer detection, but typical workflows require highly trained personnel and specialized equipment for manual and lengthy processing, limiting use in resource-constrained areas. As a potential solution, we introduce the Automated Cartridge-based Cancer Early Screening System (ACCESS), a compact, portable, multiplexed, automated platform that performs droplet magnetofluidic- and methylation-specific qPCR-based assays for the detection of DNA methylation cancer biomarkers. Development of ACCESS focuses on esophageal cancer, which is among the most prevalent cancers in low- and middle-income countries with extremely low survival rates. Upon implementing detection assays for two esophageal cancer methylation biomarkers within ACCESS, we demonstrated successful detection of both biomarkers from esophageal tumor tissue samples from eight esophageal cancer patients while showing specificity in paired normal esophageal tissue samples. These results illustrate ACCESS's potential as an amenable epigenetic diagnostic tool for resource-constrained areas toward early detection of esophageal cancer and potentially other malignancies.

Plasma cell-free DNA 5-hydroxymethylcytosine and whole-genome sequencing signatures for early detection of esophageal cancer

Esophageal cancer is a highly incidence and deadly disease with a poor prognosis, especially in developing countries. Owing to the lack of specific symptoms and early diagnostic biomarkers, most patients are diagnosed with advanced disease, leading to a 5-year survival rate of less than 15%. Early (n = 50) and middle-advanced (n = 50) esophageal squamous cell carcinoma (ESCC) patients, as well as 71 healthy individuals, underwent 5-hydroxymethylcytosine (5hmC) sequencing on their plasma cell-free DNA (cfDNA). A Northern Chinese cohort of cfDNA 5hmC dataset of 150 ESCC patients and 183 healthy individuals were downloaded for validation. A diagnostic model was developed using cfDNA 5hmC signatures and then improved by low-pass whole genome sequencing (WGS) features of cfDNA. Conserved cfDNA 5hmC modification motifs were observed in the two independent ESCC cohorts. The diagnostic model with 5hmC features achieved an AUC of 0.810 and 0.862 in the Southern and Northern cohorts, respectively, with sensitivities of 69.3–74.3% and specificities of 82.4–90.7%. The performance was well maintained in Stage I to Stage IV, with accuracy of 70–100%, but low in Stage 0, 33.3%. Low-pass WGS of cfDNA improved the AUC to 0.934 with a sensitivity of 82.4%, a specificity of 88.2%, and an accuracy of 84.3%, particularly significantly in Stage 0, with an accuracy up to 80%. 5hmC and WGS could efficiently differentiate very early ESCC from healthy individuals. These findings imply a non-invasive and convenient method for ESCC detection when clinical treatments are available and may eventually prolong survival.

Low-toxicity PEG-coated SERS silver nanocubes for diagnosing esophageal cancer

The early detection of esophageal cancer is crucial for enhancing patient survival. Surface-enhanced Raman scattering (SERS), renowned for its precise detection capabilities of low-concentration molecules and organisms, holds immense promise for early diagnosis. However, the use of silver nanoparticles to enhance the Raman signal can disrupt the detection process. To address this problem in this study, we synthesized polyethylene glycol (PEG)-coated silver nanocubes (PEG-AgNCs) as stable, label-free, and low-toxicity nanoprobes for detecting esophageal cancer. AgNCs with sharp corners and edges are selected as the SERS substrate to increase the strength of the Raman signal. To minimize the toxicity of AgNCs, the surface is coated with PEG, which exhibits outstanding biocompatibility. The SERS effectiveness of PEG-AgNCs was evaluated using 4-mercaptobenzoic acid as a Raman probe molecule, and the toxicity of PEG-AgNCs at different concentrations was investigated using human keratinocytes (HaCaT) and human esophageal carcinoma cells (TE-1). Results showed that PEG-AgNCs can effectively reduce the toxicity of AgNCs while maintaining a strong SERS signal. Remarkable differences in Raman spectra were observed between HaCat and TE-1 cells within the spectral range of 588–768 cm−1, indicating the potential of PEG-AgNCs in the early detection of esophageal cancer using Raman detection. Our study highlights the potential of developing low-toxicity SERS biosensors for biological and chemical analyses.

Assessment of hyperspectral imaging and CycleGAN-simulated narrowband techniques to detect early esophageal cancer

The clinical signs and symptoms of esophageal cancer (EC) are often not discernible until the intermediate or advanced phases. The detection of EC in advanced stages significantly decreases the survival rate to below 20%. This study conducts a comparative analysis of the efficacy of several imaging techniques, including white light image (WLI), narrowband imaging (NBI), cycle-consistent adversarial network simulated narrowband image (CNBI), and hyperspectral imaging simulated narrowband image (HNBI), in the early detection of esophageal cancer (EC). In conjunction with Kaohsiung Armed Forces General Hospital, a dataset consisting of 1000 EC pictures was used, including 500 images captured using WLI and 500 images captured using NBI. The CycleGAN model was used to generate the CNBI dataset. Additionally, a novel method for HSI imaging was created with the objective of generating HNBI pictures. The evaluation of the efficacy of these four picture types in early detection of EC was conducted using three indicators: CIEDE2000, entropy, and the structural similarity index measure (SSIM). Results of the CIEDE2000, entropy, and SSIM analyses suggest that using CycleGAN to generate CNBI images and HSI model for creating HNBI images is superior in detecting early esophageal cancer compared to the use of conventional WLI and NBI techniques.

RAE1 promotes nitrosamine-induced malignant transformation of human esophageal epithelial cells through PPARα-mediated lipid metabolism

Esophageal cancer (EC) is the sixth cause of cancer-related deaths and still is a significant public health problem globally. Nitrosamines exposure represents a major health concern increasing EC risks. Exploring the mechanisms induced by nitrosamines may contribute to the prevention and early detection of EC. However, the mechanism of nitrosamine carcinogenesis remains unclear. Ribonucleic acid export 1 (RAE1), has an important role in mediating diverse cancer types, but, to date, there has been no study for any functional role of RAE1 in esophageal carcinogenesis. Here, we successfully verified the nitrosamine-induced malignant transformation cell (MNNG-M) by xenograft tumor model, based on which it was found that RAE1 was upregulation in the early stage of nitrosamine-induced esophageal carcinogenesis and EC tissues. RAE1 knockdown led to severe blockade in G2/M phase and significant inhibition of proliferation of MNNG-M cells, whereas RAE1 overexpression had the opposite effect. In addition, peroxisome proliferator-activated receptor-alpha (PPARα), was demonstrated as a downstream target gene of RAE1, and its down-regulation reduced lipid accumulation, resulting in causing cells accumulation in the G2/M phase. Mechanistically, we found that RAE1 regulates the lipid metabolism by maintaining the stability of PPARα mRNA. Taken together, our study reveals that RAE1 promotes malignant transformation of human esophageal epithelial cells (Het-1A) by regulating PPARα-mediated lipid metabolism to affect cell cycle progression, and offers a new explanation of the mechanisms underlying esophageal carcinogenesis.

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.

Abstract 4321: Clinical-grade early detection of esophageal cancer on data from a non-endoscopic device using deep learning

Abstract Aim: Our goal was to evaluate a novel deep learning system for integrated human-AI review of whole-slide images (WSIs) from the Cytosponge to predict progression from Barrett's esophagus (BE) to esophageal adenocarcinoma (EAC). Background: Fewer than 1% of BE patients advance to EAC, therefore delineating the patients at elevated risk is crucial for prioritizing their surveillance and treatment. Previous work has shown that the Cytosponge, a non-endoscopic test, coupled with biomarkers (TFF3, atypia, p53) can be used to identify and risk-stratify individuals with BE. However, the Cytosponge workflow to date has required a time consuming pathology review. Methods: We collected 1,700 BE Cytosponge WSIs from over 800 patients. We hand-drew over 30,000 digital annotations on the WSIs, a 300-hour collaborative effort between a computer scientist and 4 pathologists. Using these data for training, validation, and testing, we developed a deep learning system to determine whether cellular atypia and/or p53 overexpression is present. We used this system in a machine-assisted (MA) review process to highlight the most diagnostically interesting regions of WSIs for pathologist review. In our proposed system, patients are automatically triaged into high or low confidence positive, and high confidence negative classes for both atypia and p53 status, and only low confidence cases are sent for MA review. Results: Out of 23 architectures, the best atypia model achieves an accuracy of 0.90, and the best p53 model 0.96. The model was concordant with pathologists in 342/425 (80.5%) validation and 301/330 (91.2%) test-set patients. The system also identified 83 validation cases with atypia and/or p53 that were not previously reported by the pathologists. Of these, 29 (35%) were confirmed abnormal with the ground truth endoscopy biopsies (11 high grade dysplasia or intramucosal carcinoma, 14 low grade dysplasia, 5 indefinite for dysplasia). In the test set 29 results were discordant with pathology including 6 (21%) confirmed abnormal on endoscopy biopsies (3 HGD/IMC, 2 LGD, 1 IND). We used the models’ outputs to localize where these “missed” regions lie in the WSIs, and pathologist MA review confirmed the presence of focal abnormalities (cellular atypia +/- p53 aberrance) correctly identified by the algorithm. If pathologists only review cases for which the model had low confidence, this preserves overall performance while reducing pathologist workload over twenty-fold. MA review on the remaining WSIs is 4.7 times faster than standard review for atypia screening and 16.0 times faster for p53. Conclusion: This work shows the potential of an integrated human-AI WSI review process to drastically expedite a time consuming task and improve on pathologist performance when abnormal regions are easy for human eyes to miss. This work therefore lays the groundwork for similar machine-doctor approaches in other areas of medicine. Citation Format: Adam G. Berman, Ahmad Miremadi, Maria O'Donovan, Shalini Malhotra, Monika Tripathi, Rebecca C. Fitzgerald, Florian Markowetz. Clinical-grade early detection of esophageal cancer on data from a non-endoscopic device using deep learning. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4321.

Esophageal cancer and bacterial part of gut microbiota - A multidisciplinary point of view.

There is an urgent need to search for new screening methods that allow early detection of esophageal cancer and thus achieve better clinical outcomes. Nowadays, it is known that the esophagus is not a sterile part of the gastrointestinal tract. It is colonized with various microorganisms therefore a "healthy" esophageal microbiome exists. The dysbiotic changes of esophageal microbiome can lead to the development of esophageal diseases including esophageal cancer. There is a strong consensus in the literature that the intestinal microbiome may be involved in esophageal carcinogenesis. Recently, emphasis has also been placed on the relationship between the oral microbiome and the occurrence of esophageal cancer. According to recent studies, some of the bacteria present in the oral cavity, such as Tannerella forsythia, Streptococcus anginosus, Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, and Fusobacterium nucleatum may contribute to the development of this cancer. Moreover, the oral microbiome of patients with esophageal cancer differs significantly from that of healthy individuals. This opens new insights into the search for a microbiome-associated marker for early identification of patients at high risk for developing this cancer.

The role of brachytherapy in treatment of stage I esophageal cancer: A systematic review

Despite advancements in the early detection of esophageal cancer, optimal radiotherapy methods for treatment of early disease have not yet been determined. Moreover, the benefit of intraluminal brachytherapy on local control or survival remains controversial. We performed a systematic review to establish the role of brachytherapy as boost therapy in stage I esophageal squamous cell carcinoma, and to evaluate associated survival outcomes. A systematic search of three bibliographic databases from January 1950 to January 2019 was conducted. All studies investigating brachytherapy for curative intent were included and palliative treatment was excluded. Primary outcomes included overall survival and disease-free survival (DFS). Secondary outcomes included loco-regional control (LRC) and toxicity grades and/or complications. Two reviewers independently abstracted data and evaluated study quality using grading of recommendations assessment, development, and evaluation, pooled results were presented through risk ratios. A total of 12 retrospective studies met inclusion criteria. The overall quality of evidence yielded a Grade 1C rating (strong recommendation, low quality evidence). Of 525 included patients, 325 patients received both external beam radiation (EBRT), and brachytherapy, 132 underwent EBRT only, and 68 received brachytherapy with and/or without chemoradiation. For patient group treated with EBRT and brachytherapy, 5-year mortality, DFS and LRC were: 43% (27-59%), 63% (49-76%) and 72% (63-80%) respectively. Rates of complications reported included 82.1% Grade 1 esophagitis for a combined external beam radiation and brachytherapy cohort, 12.3% ulcerations, and 3.3% fistulae. Brachytherapy as a combined modality is encouraging, given its relative safety and effectiveness. Further prospective analysis using higher quality evidence is warranted to evaluate oncologic outcomes and survival advantage.

Identification of Early Esophageal Cancer by Semantic Segmentation.

Early detection of esophageal cancer has always been difficult, thereby reducing the overall five-year survival rate of patients. In this study, semantic segmentation was used to predict and label esophageal cancer in its early stages. U-Net was used as the basic artificial neural network along with Resnet to extract feature maps that will classify and predict the location of esophageal cancer. A total of 75 white-light images (WLI) and 90 narrow-band images (NBI) were used. These images were classified into three categories: normal, dysplasia, and squamous cell carcinoma. After labeling, the data were divided into a training set, verification set, and test set. The training set was approved by the encoder–decoder model to train the prediction model. Research results show that the average time of 111 ms is used to predict each image in the test set, and the evaluation method is calculated in pixel units. Sensitivity is measured based on the severity of the cancer. In addition, NBI has higher accuracy of 84.724% when compared with the 82.377% accuracy rate of WLI, thereby making it a suitable method to detect esophageal cancer using the algorithm developed in this study.

Evolutionary Neural Architecture Search for Automatic Esophageal Lesion Identification and Segmentation

Automatic esophageal lesion identification (ESEI) is of great importance to clinically aid the endoscopists with the early detection of esophageal cancer. However, accurate identification of esophageal lesion is challenging due to the varying shape, size, illumination condition, and complex background with artifacts in endoscopic images. Although deep neural network based approaches have considerably boosted the performance by automatically learning features from esophageal images, the configuration of the network architecture is highly dependent on domain expertise and is a daunting task to be manually tuned. In this article, we propose an evolutionary algorithm based approach to search for the optimal multitask network architecture for ESEI. Different from existing studies, we first design a multitask network search space, which considers the lesion identification as two steps including esophageal image classification and esophageal lesion segmentation. In particular, the input image resolution is covered in the search space, and the classification utilizes both downsampled and upsampled features. Besides, to avoid scratch training of sampled network architectures in the evolutionary algorithm, the one-shot supernet strategy is developed for searching the optimal network architecture. Results from the performed experiments on a collected sizeable clinical esophageal image dataset show that the proposed method improves on the state of the art on all measured metrics.

Cellulose Sponge, the Detector of Esophageal Cancer: Innovation for Early Detection of Esophageal Cancer without Biopsy?: A Mini Review

Patients with esophageal cancer each year can reach 400,000 people. Inefficient screening methods and worsening symptoms, patients often come late. Squamous cell carcinoma, which is the cause of esophageal cancer, has percentage of 84% of all cancer incidences. So far, the current screening strategy is endoscopy with biopsy. This screening has the main side effect of bleeding in metaplastic area. Cellulose Sponge, the Detector of Esophageal Cancer (CaSPER), can be used for screening without a biopsy using a cellulose sponge. The method used in this mini review is an evidence-based method that focuses on evaluating pre-existing journals. The result is that CaSPER is able to provide strong cellular results of 98%, specificity of 100%, and sensitivity of 97%. Capsules made of glucose and cytosponge of cellulose will bring the metaplastic cells to the sponge. This screening is feasible, safe, comfortable, and without side effects. Using trefoil factor 3 as biomarker is able to distinguish between goblet and pseudogoblet cells. CaSPER is minimally invasive, cheaper, and easily accepted, so that in the future it is hoped that it can be mass produced, especially for areas with high esophageal cancer.

Novel epigenetic network biomarkers for early detection of esophageal cancer

BackgroundEarly detection of esophageal cancer is critical to improve survival. Whilst studies have identified biomarkers, their interpretation and validity is often confounded by cell-type heterogeneity.ResultsHere we applied systems-epigenomic and cell-type deconvolution algorithms to a discovery set encompassing RNA-Seq and DNA methylation data from esophageal adenocarcinoma (EAC) patients and matched normal-adjacent tissue, in order to identify robust biomarkers, free from the confounding effect posed by cell-type heterogeneity. We identify 12 gene-modules that are epigenetically deregulated in EAC, and are able to validate all 12 modules in 4 independent EAC cohorts. We demonstrate that the epigenetic deregulation is present in the epithelial compartment of EAC-tissue. Using single-cell RNA-Seq data we show that one of these modules, a proto-cadherin module centered around CTNND2, is inactivated in Barrett’s Esophagus, a precursor lesion to EAC. By measuring DNA methylation in saliva from EAC cases and controls, we identify a chemokine module centered around CCL20, whose methylation patterns in saliva correlate with EAC status.ConclusionsGiven our observations that a CCL20 chemokine network is overactivated in EAC tissue and saliva from EAC patients, and that in independent studies CCL20 has been found to be overactivated in EAC tissue infected with the bacterium F. nucleatum, a bacterium that normally inhabits the oral cavity, our results highlight the possibility of using DNAm measurements in saliva as a proxy for changes occurring in the esophageal epithelium. Both the CTNND2/CCL20 modules represent novel promising network biomarkers for EAC that merit further investigation.

Plasma Cell-Free DNA 5-Hydroxymethylcytosine and Whole-Genome Sequencing Signatures for Early Detection of Esophageal Cancer

Plasma Cell-Free DNA 5-Hydroxymethylcytosine and Whole-Genome Sequencing Signatures for Early Detection of Esophageal Cancer

Application of artificial intelligence-assisted endoscopic detection of early esophageal cancer

Application of artificial intelligence-assisted endoscopic detection of early esophageal cancer

Discovery and validation of methylation signatures in circulating cell-free DNA for early detection of esophageal cancer: a case-control study

BackgroundPlasma cell-free DNA (cfDNA) methylation has shown promising results in the early detection of multiple cancers recently. Here, we conducted a study to investigate the performance of cfDNA methylation in the early detection of esophageal cancer (ESCA).MethodsSpecific methylation markers for ESCA were identified and optimized based on esophageal tumor and paired adjacent tissues (n = 24). Age-matched participants with ESCA (n = 85), benign esophageal diseases (n = 10), and healthy controls (n = 125) were randomized into the training and test sets to develop a classifier to differentiate ESCA from healthy controls and benign esophageal disease. The classifier was further validated in an independent plasma cohort of ESCA patients (n = 83) and healthy controls (n = 98).ResultsIn total, 921 differentially methylated regions (DMRs) between tumor and adjacent tissues were identified. The early detection classifier based on those DMRs was first developed and tested in plasma samples, discriminating ESCA patients from benign and healthy controls with a sensitivity of 76.2% (60.5–87.9%) and a specificity of 94.1% (85.7–98.4%) in the test set. The performance of the classifier was consistent irrespective of sex, age, and pathological diagnosis (P > 0.05). In the independent plasma validation cohort, similar performance was observed with a sensitivity of 74.7% (64.0–83.6%) and a specificity of 95.9% (89.9–98.9%). Sensitivity for stage 0–II was 58.8% (44.2–72.4%).ConclusionWe demonstrated that the cfDNA methylation patterns could distinguish ESCAs from healthy individuals and benign esophageal diseases with promising sensitivity and specificity. Further prospective evaluation of the classifier in the early detection of ESCAs in high-risk individuals is warranted.

Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer.

Simple SummaryDetection of early esophageal cancer is important to improve patient survival, however, early diagnosis of the cancer cells is difficult, even for experienced endoscopists. This article provides a new method by using hyperspectral imaging and a deep learning diagnosis model to classify and diagnose esophageal cancer using a single-shot multibox detector. The accuracy of the results when using an RGB image in WLI was 83% and while using the spectrum data the accuracy was increased to 88%. There was an increase of 5% in WLI. The accuracy of the results when using an RGB image in NBI was 86% and while using the spectrum data the accuracy was increased to 91%. There was an increase of 5% in NBI. This study proves that the accuracy of prediction when using the spectrum data has been significantly improved and the diagnosis of narrow-band endoscopy data is more sensitive than that of white-light endoscopy.This study uses hyperspectral imaging (HSI) and a deep learning diagnosis model that can identify the stage of esophageal cancer and mark the locations. This model simulates the spectrum data from the image using an algorithm developed in this study which is combined with deep learning for the classification and diagnosis of esophageal cancer using a single-shot multibox detector (SSD)-based identification system. Some 155 white-light endoscopic images and 153 narrow-band endoscopic images of esophageal cancer were used to evaluate the prediction model. The algorithm took 19 s to predict the results of 308 test images and the accuracy of the test results of the WLI and NBI esophageal cancer was 88 and 91%, respectively, when using the spectral data. Compared with RGB images, the accuracy of the WLI was 83% and the NBI was 86%. In this study, the accuracy of the WLI and NBI was increased by 5%, confirming that the prediction accuracy of the HSI detection method is significantly improved.

Blood Circulating miRNA Pairs as a Robust Signature for Early Detection of Esophageal Cancer

Esophageal cancer (EC) is a common malignant tumor in the digestive system which is often diagnosed at the middle and late stages. Noninvasive diagnosis using circulating miRNA as biomarkers enables accurate detection of early-stage EC to reduce mortality. We built a diagnostic signature consisting of four miRNA pairs for the early detection of EC using individualized Pairwise Analysis of Gene Expression (iPAGE). Profiling of miRNA expression identified 496 miRNA pairs with significant relative expression change. Four miRNA pairs consistently selected from LASSO were used to construct the final diagnostic model. The performance of the signature was validated using two independent datasets, yielding both AUCs and PRCs over 0.99. Furthermore, precision, recall, and F-score were also evaluated for clinical application, when a fixed threshold is given, resulting in all the scores are larger than 0.92 in the training set, test set, and two validation sets. Our results suggested that the 4-miRNA signature is a new biomarker for the early diagnosis of patients with EC. The clinical use of this signature would have improved the detection of EC for earlier therapy and more favorite prognosis.

Early Esophageal Cancer detection using Deep learning Techniques. (Review Article)

Esophageal cancer is one of the deadliest diseases for humans, since it is discovered in very advanced stages. As result, pathologists are increasingly relying in image recognition and artificial intelligence tools to aid in the early identification and evaluation of this lesion. We examined a number of papers that dealt with this issue during the time span in order to shed light on the studies that were performed in this area (2017 and 2020). We have looked at experiments that used Convolutional Neural Network (CNN) technologies in the study of endoscopic images to help with early detection or diagnosis of esophageal cancer and its various forms. More research on esophageal malignant growth is required, as well as improving the disease’s indicative existence and employing more proven techniques for feature selection/extraction of endoscopic images. The aim of this review is to highlight the research conducted on endoscopic images of the esophagus using deep learning algorithms, including CNN, Support Vector Machine (SVM), Random Forests (RF) and other techniques that were used to design the Computer-Aided Detection (CAD) system. In this review we covered some but not all articles that was of great contact with our master’s thesis research in this regard.