Detection Of Esophageal Cancer Research Articles

DIAGNOSTIC ACCURACY OF MULTISPIRAL COMPUTED TOMOGRAPHY IN DETECTING AND STAGING OF ESOPHAGEAL AND GASTRIC CANCER

Background. The sensitivity and specificity of multislice computed tomography is of great importance in the detection of gastric and esophageal cancer, and also expands the possibilities of preoperative staging using computed tomography. Materials and methods. A retrospective study at the A.N. Syzganov National Scientific Center of Surgery from 2022 to 2024, included 121 patients: 48 females (39.6%), 73 males (60.3%), with an average age of 60 years. Sensitivity, specificity were calculated to assess diagnostic accuracy. Esophageal and gastric cancer staging via computed tomography was done using the TNM classification and compared with esophagogastroduodenoscopy, pathohistological examination results. Results. The sensitivity of computed tomography was 96.49%, the specificity was 85.71%. Esophagogastroduodenoscopy showed a sensitivity of 79.75%, specificity of 95.24%. Conclusion. Computed tomography is highly informative, sensitive in detecting esophageal and gastric cancer, with superior diagnostic accuracy compared to esophagogastroduodenoscopy. Given the detection of esophageal and gastric cancer at T2 and T3 stages, along with the presence of distant metastases in some patients, implementing protocols for early diagnosis is advisable.

369. IMPACT OF THE COVID-19 PANDEMIC ON ESOPHAGEAL CANCER PATIENTS UNDERGOING ESOPHAGECTOMY: AN ACS-NSQIP DATABASE ANALYSIS

Abstract Background To cope with the increasing number of infected patients, medical access was restricted, and public facilities were closed throughout the COVID-19 pandemic. Early detection and treatment of esophageal cancer is critical for improved prognosis. However, the impact of pandemic-related medical access restrictions has not be evaluated to date for esophageal cancer. Therefore, the purpose of this research is to determine how the lockdown policy affected the surgical outcome of patients with esophageal cancer. Methods We analyzed patients who underwent esophagectomy for esophageal cancer between 2016-2022 using data obtained from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), including the procedure targeted esophagectomy database. The patients were stratified into two groups based on their operation date relative to pandemic (BEFORE COVID group: 2016-2019 vs AFTER COVID group: 2020-2022). Demographic data and perioperative outcomes were compared between the two groups. Statistical analysis included Chi-square tests for categorical variables and independent student T-tests for continuous data, with a significance threshold set at p < 0.05. Results Between 2016-2022, 7,198 esophageal cancer patients underwent esophagectomy. Clinical T4 increased significantly, but clinical N and pathologic T and N stages showed no impact. PET scan imaging, neoadjuvant chemotherapy and radiotherapy remained unchanged. The utilization of a minimally invasive approach rose substantially from 32.6% to 47.2% (p < 0.001). Positive margin rates increased from 7.5% to 9% (p = 0.03), and anastomotic leak rates increased from 14.1% to 17.2% (p < 0.001). Operative time, blood loss, and conversion rate showed no significant differences. Perioperative complications, including organ space surgical site infections and myocardial infarctions, significantly increased after COVID-19. Conclusion The COVID-19 pandemic altered the clinical staging of esophageal cancer patients undergoing esophagectomy, but it did not disrupt access to essential investigations and treatment. A notable increase in the implementation of minimally invasive techniques was observed after the pandemic. However, significant impacts were noticed on operative outcomes, including positive margin and leak rates.

Dissecting the Prediagnostic Journey to Identify Opportunities for Early Detection of Esophageal Cancer: Findings From a High-Risk Area in Rural China

Dissecting the Prediagnostic Journey to Identify Opportunities for Early Detection of Esophageal Cancer: Findings From a High-Risk Area in Rural China

Importance of early detection of esophageal cancer before the tumor progresses too much for effective treatment

This editorial comments on an article by Qu et al published in the World Journal of Gastrointestinal Oncology . It focuses on the importance of early detection of esophageal cancer, including recurrence or secondary malignancy after chemoradiotherapy (CRT). Endoscopic resection is the first choice for treatment for esophageal cancer remaining within the mucous membrane, while surgery or radical CRT are treatment options for advanced stages depending on the patient’s general condition and desire. Although these treatments are potentially curative, they are more invasive than endoscopic resection. Early-stage esophageal cancer is often asymptomatic and difficult to detect. Uniform periodic endoscopy is unrealistic. Although less burdensome tests exist, including liquid biopsy and urinary biomarkers, these have not yet been widely used in clinical practice. Early detection is important after radical CRT because the local recurrence rate is higher than that after surgery. However, endoscopic resection or photodynamic therapy is indicated if detected in the early stages, and positive results have been reported. Early detection of esophageal cancer is crucial. Endoscopy is the main diagnostic method; however, new and less burdensome methods should be established to ensure early treatment for patients with esophageal cancer.

Hybrid model-based early diagnosis of esophageal disorders using convolutional neural network and refined logistic regression

Accurate diagnosis of the stage of esophageal disorders is crucial in the treatment planning for patients with esophageal cancer and in improving the 5-year survival rate. The progression of esophageal cancer typically begins with precancerous esophageal disorders such as gastroesophageal reflux disease (GERD), esophagitis, and non-dysplasia Barrett’s esophagus, eventually advancing to low- and high-dysplasia Barrett’s esophagus and ultimately to esophageal adenocarcinoma (EAC). The majority of prior research efforts have primarily focused on the identification of general gastrointestinal (GI) tract diseases and the detection of esophageal cancer, with limited attention to the diverse spectrum of esophageal disorders. To address this gap, an innovative framework called Hybrid Model-Based Esophageal Disorder Diagnosis (HMEDD) is developed in this work. The primary goal of HMEDD is to enable early diagnosis of various esophageal disorders using gastroscopic images. HMEDD combines the feature extraction capabilities of an Esophageal Convolutional Neural Network (EsoNet) with the high classification accuracy of a Refined Logistic Regression (RLR) model. EsoNet comprises 14 weight layers and kernels 3×3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left(3\ imes 3\\right)$$\\end{document} used for high-level deep feature learning. Esophageal disorders are classified using the RLR model, which is developed by fine-tuning hyperparameters in the traditional Logistic Regression (LR) model using Random Search Cross-Validation (RandomizedSearchCV). HMEDD is extensively validated using a data set containing numerous esophageal abnormalities captured through gastroscopic images. The results of this work demonstrate the effectiveness of HMEDD in accurately classifying different esophageal disorders, with an impressive accuracy of 92.15%. These findings will assist physicians in the accurate early diagnosis of esophageal disorders, ultimately preventing their progression to cancer.

Ex Vivo Optical Coherence Tomography combined with near infrared targeted fluorescence: towards in-vivo esophageal cancer detection

Ex Vivo Optical Coherence Tomography combined with near infrared targeted fluorescence: towards in-vivo esophageal cancer detection

Challenges in early detection and endoscopic resection of esophageal cancer: There is a long way to go.

The publication by Qu et al provided a comprehensive discussion about the epidemiology, etiology, histopathology, early detection, and endoscopic treatment of esophageal carcinoma (EC) and summarized the progress in the advanced technologies for screening and endoscopic resection for EC. In this editorial, we will provide deeper insight into the challenges that hinder practical application of these advanced technologies along with the role of these technologies in upper endoscopy quality. More efforts need to be made to overcome the challenges and add the value of these technologies in upper endoscopy quality. Clinical outcomes of management strategies after noncurative endoscopic dissection for early EC patients need further investigation. The experiences with noncurative endoscopic resection of other organs may have certain implications for noncurative resection of early EC.

Salivary metabolomic biomarkers for esophageal and gastric cancers by liquid chromatography-mass spectrometry.

Early detection of esophageal and gastric cancers is essential for patients' prognosis; however, optimal noninvasive screening tests are currently not available. Saliva is a biofluid that is readily available, allowing for frequent screening tests. Thus, we explored salivary diagnostic biomarkers for esophageal and gastric cancers using metabolomic analyses. Saliva samples were collected from patients with esophageal (n = 50) and gastric cancer (n = 63), and patients without cancer as controls (n = 20). Salivary metabolites were analyzed by liquid chromatography-mass spectrometry to identify salivary biomarkers. We also examined the metabolic profiles of gastric cancer tissues and compared them with the salivary biomarkers. The sensitivity of the diagnostic models based on salivary biomarkers was assessed by comparing it with that of serum tumor markers. Additionally, using postoperative saliva samples collected from patients with gastric cancer, we analyzed the changes in the biomarkers' concentrations before and after surgery. Cytosine was detected as a salivary biomarker for gastric cancer, and cytosine, 2-oxoglutarate, and arginine were detected as salivary biomarkers for esophageal cancer. Cytidine, a cytosine nucleotide, showed decreased concentrations in gastric cancer tissues. The sensitivity of the diagnostic models for esophageal and gastric cancers was 66.0% and 47.6%, respectively, while that of serum tumor markers was 40%. Salivary cytosine concentration increased significantly postoperatively relative to the preoperative value. In summary, we identified salivary biomarkers for esophageal and gastric cancers, which showed diagnostic sensitivity at least comparable to that of serum tumor markers. Salivary metabolomic tests could be promising screening tests for these types of cancer.

Evolution of the treatment of esophageal cancer: artificial intelligence and the role of sentinel lymph node assessment in esophageal cancer

Sentinel lymph node (SLN) biopsy has revolutionized the staging and prognosis of breast cancer and melanoma. Because of the complicated lymphatic network around the esophagus, the utility of SLN biopsy for esophageal cancer is less clear. The accuracy of SLN mapping in esophageal cancer depends on tumor site, disease stage, use of neoadjuvant therapy, and patient characteristics. SLN biopsy may improve staging and result in less morbidity in patients with early esophageal cancer, compared with radical lymphadenectomy and esophagectomy. A recent study that investigated hybrid tracers in sentinel node navigation surgery (SNNS) demonstrated promising results for the detection of peritumoral SLNs. However, evidence that firmly establishes the concept of the SLN for esophageal cancer is still lacking. Big data analytics and artificial intelligence have been associated with improvements in the detection and prognosis of esophageal cancer. This review considers the roles of the evolving technologies of SLN biopsy and artificial intelligence, which together have the potential to further improve prognoses and outcomes for patients with esophageal cancer. Additional investigation is necessary to establish standardized protocols and to determine the long-term effectiveness of these approaches in settings involving neoadjuvant therapy and advanced-stage disease.

Supersulfide metabolome of exhaled breath condensate for diagnostic biomarkers of esophageal cancer.

e15027 Background: The early estimate and detection of esophageal cancer (EC) is essentially required for continuous esophagogastroduodenoscopy and histopathological diagnosis. However, endoscopic examination is sometimes invasive, which may cause somehow limited clinical application and compliance. Also, the traditional blood tumor marker is unsuitable for a cancer screening. In recent years, the energy production by sulfur respiration in hypoxic environments in cancer tissues is getting spotlighted in tumor biology. The current study is aimed to evaluate usefulness of sulfur containing compounds as new biomarkers for EC by using blood samples and exhaled breath condensate (EBC) that can be readily obtained in a non-invasive fashion. Methods: Fifty EC patients were included in the study between June 2021 and September 2021. EBC and plasma samples were collected before starting any medicinal remedy including medication, chemotherapy, radiation, surgery, and endoscopic and dentistry maneuvers. Thirty healthy controls were recruited if they did not have any cancer history, surgery and aged over 50s preferred age for esophageal cancer. All samples were stored at -80℃ immediately after appropriate preparation until the following inspection. Then, sulfur metabolome analysis using LC-MS/MS was performed to see the difference of metabolic profile between the two groups. We used Student t tests for comparing two groups, and one -way ANOVA test for more than two groups, with significance set at P<0.05. To evaluate the diagnostic value of some metabolites, ROC curves and the AUC were calculated. Results: The supersulfide metabolic profiles were remarkably different between EC patients and controls. Especially, cysteine persulfide (CysSSH) was significantly increased in both EBC and plasma of the EC patients. The sensitivity and specificity of CysSSH in EBC were 60% and 96%, respectively, with an AUC of 0.71. The sensitivity and specificity of CysSSH in plasma were 49% and 96%, respectively, with an AUC of 0.73. In addition, homocysteine persulfide (HomoCysSSH) in plasma showed more significant difference than CysSSH and was able to distinguish EC patients from healthy subjects with AUC of 0.93 (sensitivity, 89%; specificity, 96%). The level of HomoCysSSH was correlated with that of CysSSH in EBC. The subgroup analysis for histological subtypes and tumor progression showed that both two metabolites were increased in EC patients regardless of histological type and disease’ progression. In particular, the amount of homoCysSSH in plasma was statistically higher in early-stage EC than that of controls (p<0.0001). Conclusions: Supersulfides like CysSSH and HomoCysSSH could be potential biomarkers for detecting EC. Cysteine persulfide from EBC may serve as a valuable non-invasive biomarker with similar detectable ability compared with several biomarkers of blood samples that are currently reported.

Economic analysis of combined multi-cancer detection blood test and usual care.

e23055 Background: A previous multi-cancer detection blood test (MCDBT) in six prevalent and deadly cancers including colorectal, esophageal, liver, lung, ovarian, and pancreatic cancers, has been reported to decrease late-stage incidence and increase 5-year survival rate. However, the economic cost-effectiveness of integrating the MCDBT with usual care and the factors influencing it have not been investigated. Methods: An interception model was utilized to estimate the stage distribution at the time of cancer diagnosis, while a decision-tree model was developed to assess the clinical and economic outcomes of annual MCDBT for individuals aged 50 to 79 in the United States. The performance metrics for the MCDBT were based on the findings of a previous study called THUNDER. The incidence data were derived from the Surveillance, Epidemiology, and End Results Program (SEER), and the associated costs were obtained from SEER-Medicare. Other input data was estimated based on information available in the published literature. Results: In addition to the usual care, the integration of the MCDBT resulted in a shift of 27.9% in cancer cases from stage III-IV to stage I-II, which led to a decrease in per-cancer treatment cost by $5971 and an increase in quality-adjusted life-years by 0.10 per cancer case. The major factors that influenced the incremental cost-effectiveness ratio (ICER) included price, incidence, specificity, and sensitivity. The MCDBT with a price less than $800 showed cost-effectiveness with an ICER below three times the per capita GDP of the United States in the average-risk population. In a high-risk population with the incidence two-fold higher than the average-risk population, the ICER decreased by 58.3%. When compared the influence of specificity and sensitivity on the ICER of the MCDBT, a 5% increase in specificity resulted a decrease in ICER by 48.8%, while a 5% increase in sensitivity only reduced the ICER by 12.7%, suggesting the importance of high specificity in MCDBT. Conclusions: The MCDBT with a price less than $800 showed cost-effectiveness in the detection of colorectal, esophageal, liver, lung, ovarian, and pancreatic cancers. Price, incidence of the intended-use population, specificity and sensitivity were the major factors that influenced the cost-effectiveness.

Genome-Scale Multimodal Analysis of Cell-Free DNA Whole-Methylome Sequencing for Noninvasive Esophageal Cancer Detection.

Simultaneous profiling of cell-free DNA (cfDNA) methylation and fragmentation features to improve the performance of cfDNA-based cancer detection is technically challenging. We developed a method to comprehensively analyze multimodal cfDNA genomic features for more sensitive esophageal squamous cell carcinoma (ESCC) detection. Enzymatic conversion-mediated whole-methylome sequencing was applied to plasma cfDNA samples extracted from 168 patients with ESCC and 251 noncancer controls. ESCC characteristic cfDNA methylation, fragmentation, and copy number signatures were analyzed both across the genome and at accessible cis-regulatory DNA elements. To distinguish ESCC from noncancer samples, a first-layer classifier was developed for each feature type, the prediction results of which were incorporated to construct the second-layer ensemble model. ESCC plasma genome displayed global hypomethylation, altered fragmentation size, and chromosomal copy number alteration. Methylation and fragmentation changes at cancer tissue-specific accessible cis-regulatory DNA elements were also observed in ESCC plasma. By integrating multimodal genomic features for ESCC detection, the ensemble model showed improved performance over individual modalities. In the training cohort with a specificity of 99.2%, the detection sensitivity was 81.0% for all stages and 70.0% for stage 0-II. Consistent performance was observed in the test cohort with a specificity of 98.4%, an all-stage sensitivity of 79.8%, and a stage 0-II sensitivity of 69.0%. The performance of the classifier was associated with the disease stage, irrespective of clinical covariates. This study comprehensively profiles the epigenomic landscape of ESCC plasma and provides a novel noninvasive and sensitive ESCC detection approach with genome-scale multimodal analysis.

Evaluation of Spectrum-Aided Visual Enhancer (SAVE) in Esophageal Cancer Detection Using YOLO Frameworks.

The early detection of esophageal cancer presents a substantial difficulty, which contributes to its status as a primary cause of cancer-related fatalities. This study used You Only Look Once (YOLO) frameworks, specifically YOLOv5 and YOLOv8, to predict and detect early-stage EC by using a dataset sourced from the Division of Gastroenterology and Hepatology, Ditmanson Medical Foundation, Chia-Yi Christian Hospital. The dataset comprised 2741 white-light images (WLI) and 2741 hyperspectral narrowband images (HSI-NBI). They were divided into 60% training, 20% validation, and 20% test sets to facilitate robust detection. The images were produced using a conversion method called the spectrum-aided vision enhancer (SAVE). This algorithm can transform a WLI into an NBI without requiring a spectrometer or spectral head. The main goal was to identify dysplasia and squamous cell carcinoma (SCC). The model's performance was evaluated using five essential metrics: precision, recall, F1-score, mAP, and the confusion matrix. The experimental results demonstrated that the HSI model exhibited improved learning capabilities for SCC characteristics compared with the original RGB images. Within the YOLO framework, YOLOv5 outperformed YOLOv8, indicating that YOLOv5's design possessed superior feature-learning skills. The YOLOv5 model, when used in conjunction with HSI-NBI, demonstrated the best performance. It achieved a precision rate of 85.1% (CI95: 83.2-87.0%, p < 0.01) in diagnosing SCC and an F1-score of 52.5% (CI95: 50.1-54.9%, p < 0.01) in detecting dysplasia. The results of these figures were much better than those of YOLOv8. YOLOv8 achieved a precision rate of 81.7% (CI95: 79.6-83.8%, p < 0.01) and an F1-score of 49.4% (CI95: 47.0-51.8%, p < 0.05). The YOLOv5 model with HSI demonstrated greater performance than other models in multiple scenarios. This difference was statistically significant, suggesting that the YOLOv5 model with HSI significantly improved detection capabilities.

DNA methylation markers in esophageal cancer.

Esophageal cancer (EC) is a prevalent malignancy characterized by a low 5-year survival rate, primarily attributed to delayed diagnosis and limited therapeutic options. Currently, early detection of EC heavily relies on endoscopy and pathological examination, which pose challenges due to their invasiveness and high costs, leading to low patient compliance. The detection of DNA methylation offers a non-endoscopic, cost-effective, and secure approach that holds promising prospects for early EC detection. To identify improved methylation markers for early EC detection, we conducted a comprehensive review of relevant literature, summarized the performance of DNA methylation markers based on different input samples and analytical methods in EC early detection and screening. This review reveals that blood cell free DNA methylation-based method is an effective non-invasive method for early detection of EC, although there is still a need to improve its sensitivity and specificity. Another highly sensitive and specific non-endoscopic approach for early detection of EC is the esophageal exfoliated cells based-DNA methylation analysis. However, while there are substantial studies in esophageal adenocarcinoma, further more validation is required in esophageal squamous cell carcinoma. In conclusion, DNA methylation detection holds significant potential as an early detection and screening technology for EC.

Surgical Management of Brain Metastasis from Esophageal Cancer: A Systematic Review and Single-Center Experience

Brain metastases from esophageal cancer (BMEC) are rare and aggressive, with limited literature on optimal treatment modalities and a standard of care yet to be established. The objective of this study was to systematically review existing literature and perform a retrospective analysis of our institution's patients to evaluate the influence of different treatment modalities on patient outcomes. A systematic review of the literature following Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines and a retrospective review of our institutional experience with BMEC were both conducted. Data based on mean survival,histology, metastasis location, and treatment modality were abstracted. A total of 48 studies representing 136 patients with BMEC were identified, in addition to the 11 patients treated at our institution. There were a total of 100 males (12 unreported), with a median age of 62.2 at diagnosis in our systematic review, along with 8 males with a median age of 62 in our institutional review. Collectively, survival rates observed based on histology were not similar (squamous cell carcinoma: 9.2months, adenocarcinoma: 13.4months), however, based on treatment modalities (surgery: 11.6months, radiation: 10.4months, chemotherapy: 12.3months), and metastasis location (supratentorial: 10.5months, infratentorial: 9.9months), the survival times were comparable. Our review suggests that causes of death were often independent of brain metastases highlighting the need for further studies on early detection and prevention of primary esophageal cancer, as well as improved treatment modalities for BMECs.

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.

DeepCraftFuse: visual and deeply-learnable features work better together for esophageal cancer detection in patients with Barrett’s esophagus

DeepCraftFuse: visual and deeply-learnable features work better together for esophageal cancer detection in patients with Barrett’s esophagus

Esophageal cancer detection via non-contrast CT and deep learning.

Esophageal cancer is the seventh most frequently diagnosed cancer with a high mortality rate and the sixth leading cause of cancer deaths in the world. Early detection of esophageal cancer is very vital for the patients. Traditionally, contrast computed tomography (CT) was used to detect esophageal carcinomas, but with the development of deep learning (DL) technology, it may now be possible for non-contrast CT to detect esophageal carcinomas. In this study, we aimed to establish a DL-based diagnostic system to stage esophageal cancer from non-contrast chest CT images. In this retrospective dual-center study, we included 397 primary esophageal cancer patients with pathologically confirmed non-contrast chest CT images, as well as 250 healthy individuals without esophageal tumors, confirmed through endoscopic examination. The images of these participants were treated as the training data. Additionally, images from 100 esophageal cancer patients and 100 healthy individuals were enrolled for model validation. The esophagus segmentation was performed using the no-new-Net (nnU-Net) model; based on the segmentation result and feature extraction, a decision tree was employed to classify whether cancer is present or not. We compared the diagnostic efficacy of the DL-based method with the performance of radiologists with various levels of experience. Meanwhile, a diagnostic performance comparison of radiologists with and without the aid of the DL-based method was also conducted. In this study, the DL-based method demonstrated a high level of diagnostic efficacy in the detection of esophageal cancer, with a performance of AUC of 0.890, sensitivity of 0.900, specificity of 0.880, accuracy of 0.882, and F-score of 0.891. Furthermore, the incorporation of the DL-based method resulted in a significant improvement of the AUC values w.r.t. of three radiologists from 0.855/0.820/0.930 to 0.910/0.955/0.965 (p = 0.0004/<0.0001/0.0068, with DeLong's test). The DL-based method shows a satisfactory performance of sensitivity and specificity for detecting esophageal cancers from non-contrast chest CT images. With the aid of the DL-based method, radiologists can attain better diagnostic workup for esophageal cancer and minimize the chance of missing esophageal cancers in reading the CT scans acquired for health check-up purposes.

Impact of the COVID-19 pandemic on esophageal cancer resource allocation: a systematic review.

The coronavirus disease 2019 (COVID-19) pandemic challenged global infrastructure. Healthcare systems were forced to reallocate resources toward the frontlines. In this systematic review, we analyze the impact of resource reallocation during the COVID-19 pandemic on the diagnosis, management, and outcomes of esophageal cancer (EC) patients. PubMed and Embase were systematically searched for articles investigating the impact of the COVID-19 pandemic on EC patients. Of the 1,722 manuscripts initially screened, 23 met the inclusion criteria. Heterogeneity of data and outcomes reporting prohibited aggregate analysis. Reduced detection of EC and considerable variability in disease stage at presentation were noted during the COVID-19 pandemic. EC patients experienced delays in diagnostic and preoperative staging investigations but surgical resection was not associated with greater short-term morbidity or mortality. Modeling the impact of pandemic-related delays in EC care predicts significant reductions in survival with associated economic losses in the coming years. Amidst resource scarcity during the COVID-19 pandemic, the multidisciplinary management of patients with EC was affected at multiple stages in the care pathway. Although the complete ramifications of reductions in EC diagnosis and delays in care remain unclear, EC surgery was able to safely continue as a result of collaboration between centers, strict adherence to COVID-19 protective measures, and reallocation of healthcare resources towards the same. Ultimately, when healthcare systems are pushed to the brink, the downstream consequences of resource reallocation require judicious analysis to optimize overall patient outcomes.

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.