Model for Identifying Individuals at Risk for Esophageal Adenocarcinoma

Prediction of esophageal cancer risk based on genetic variants and environmental risk factors in Chinese population

BACKGROUND

Results regarding whether it is essential to incorporate genetic variants into risk prediction models for esophageal cancer (EC) are inconsistent due to the different genetic backgrounds of the populations studied. We aimed to identify single-nucleotide polymorphisms (SNPs) associated with EC among the Chinese population and to evaluate the performance of genetic and non-genetic factors in a risk model for developing EC.

METHODS

A meta-analysis was performed to systematically identify potential SNPs, which were further verified by a case-control study. Three risk models were developed: a genetic model with weighted genetic risk score (wGRS) based on promising SNPs, a non-genetic model with environmental risk factors, and a combined model including both genetic and non-genetic factors. The discrimination ability of the models was compared using the area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI). The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to assess the goodness-of-fit of the models.

RESULTS

Five promising SNPs were ultimately utilized to calculate the wGRS. Individuals in the highest quartile of the wGRS had a 4.93-fold (95% confidence interval [CI]: 2.59 to 9.38) increased risk of EC compared with those in the lowest quartile. The genetic or non-genetic model identified EC patients with AUCs ranging from 0.618 to 0.650. The combined model had an AUC of 0.707 (95% CI: 0.669 to 0.743) and was the best-fitting model (AIC = 750.55, BIC = 759.34). The NRI improved when the wGRS was added to the risk model with non-genetic factors only (NRI = 0.082, P = 0.037).

CONCLUSIONS

Among the three risk models for EC, the combined model showed optimal predictive performance and can help to identify individuals at risk of EC for tailored preventive measures.

Performance of Prediction Models for Esophageal Squamous Cell Carcinoma in General Population: A Systematic Review and External Validation Study

INTRODUCTION

Prediction models for esophageal squamous cell carcinoma (ESCC) need to be proven effective in the target population before they can be applied to population-based endoscopic screening to improve cost-effectiveness. We have systematically reviewed ESCC prediction models applicable to the general population and performed external validation and head-to-head comparisons in a large multicenter prospective cohort including 5 high-risk areas of China (Fei Cheng, Lin Zhou, Ci Xian, Yang Zhong, and Yan Ting).

METHODS

Models were identified through a systematic review and validated in a large population-based multicenter prospective cohort that included 89,753 participants aged 40-69 years who underwent their first endoscopic examination between April 2017 and March 2021 and were followed up until December 31, 2022. Model performance in external validation was estimated based on discrimination and calibration. Discrimination was assessed by C-statistic (concordance statistic), and calibration was assessed by calibration plot and Hosmer-Lemeshow test.

RESULTS

The systematic review identified 15 prediction models that predicted severe dysplasia and above lesion (SDA) or ESCC in the general population, of which 11 models (4 SDA and 7 ESCC) were externally validated. The C-statistics ranged from 0.67 (95% confidence interval 0.66-0.69) to 0.70 (0.68-0.71) of the SDA models, and the highest was achieved by Liu et al (2020) and Liu et al (2022). The C-statistics ranged from 0.51 (0.48-0.54) to 0.74 (0.71-0.77), and Han et al (2023) had the best discrimination of the ESCC models. Most models were well calibrated after recalibration because the calibration plots coincided with the x = y line.

DISCUSSION

Several prediction models showed moderate performance in external validation, and the prediction models may be useful in screening for ESCC. Further research is needed on model optimization, generalization, implementation, and health economic evaluation.

Advances in Screening for Barrett Esophagus and Esophageal Adenocarcinoma

Esophageal adenocarcinoma (EAC), the primary form of esophageal cancer in the United States, is a lethal cancer with exponentially increasing incidence. Screening for Barrett esophagus (BE), the only known precursor to EAC, followed by endoscopic surveillance to detect dysplasia and early-stage EAC and subsequent endoscopic treatment (to prevent progression of dysplasia to EAC and to treat early-stage EAC effectively) is recommended by several society guidelines. Sedated endoscopy (the primary current tool for BE screening) is both invasive and expensive, limiting its widespread use. In this review, we aim to provide a comprehensive review of recent innovations in the nonendoscopic detection of BE and EAC. These include swallowable cell sampling devices combined with protein and epigenetic biomarkers (which are now guideline endorsed as alternatives to sedated endoscopy), tethered capsule endomicroscopy, emerging peripheral blood-sampled molecular biomarkers, and exhaled volatile organic compounds. We also summarize progress and challenges in assessing BE and EAC risk, which is an important complementary component of the process for the clinical implementation of these innovative nonendoscopic tools, and propose a new paradigm for the strategy to reduce EAC incidence and mortality.

Predicting Incident Adenocarcinoma of the Esophagus or Gastric Cardia Using Machine Learning of Electronic Health Records

BACKGROUND & AIMS

Tools that can automatically predict incident esophageal adenocarcinoma (EAC) and gastric cardia adenocarcinoma (GCA) using electronic health records to guide screening decisions are needed.

METHODS

The Veterans Health Administration (VHA) Corporate Data Warehouse was accessed to identify Veterans with 1 or more encounters between 2005 and 2018. Patients diagnosed with EAC (n = 8430) or GCA (n = 2965) were identified in the VHA Central Cancer Registry and compared with 10,256,887 controls. Predictors included demographic characteristics, prescriptions, laboratory results, and diagnoses between 1 and 5 years before the index date. The Kettles Esophageal and Cardia Adenocarcinoma predictioN (K-ECAN) tool was developed and internally validated using simple random sampling imputation and extreme gradient boosting, a machine learning method. Training was performed in 50% of the data, preliminary validation in 25% of the data, and final testing in 25% of the data.

RESULTS

K-ECAN was well-calibrated and had better discrimination (area under the receiver operating characteristic curve [AuROC], 0.77) than previously validated models, such as the Nord-Trøndelag Health Study (AuROC, 0.68) and Kunzmann model (AuROC, 0.64), or published guidelines. Using only data from between 3 and 5 years before index diminished its accuracy slightly (AuROC, 0.75). Undersampling men to simulate a non-VHA population, AUCs of the Nord-Trøndelag Health Study and Kunzmann model improved, but K-ECAN was still the most accurate (AuROC, 0.85). Although gastroesophageal reflux disease was strongly associated with EAC, it contributed only a small proportion of gain in information for prediction.

CONCLUSIONS

K-ECAN is a novel, internally validated tool predicting incident EAC and GCA using electronic health records data. Further work is needed to validate K-ECAN outside VHA and to assess how best to implement it within electronic health records.

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

Background

Methods to identify patients at increased risk of oesophageal cancer are needed to better identify those for targeted 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.

Methods

Prospective open cohort study using routinely collected data from 1804 QResearch® general practices. We used 1354 practices (12.9 M patients) to develop the algorithm. We validated the algorithm in 450 separate practices from QResearch (4.12 M patients) and 355 Clinical Practice Research Datalink (CPRD) practices (2.53 M 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.

Finding

There were 16,384 incident cases of oesophageal cancer in the derivation cohort (0.13% of 12.9 M). 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.

Interpretation

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.

Funding

Innovate UK and CRUK (grant 105857).

Missed opportunities to screen for Barrett's esophagus in the primary care setting of a large health system

BACKGROUND AND AIMS

The rate of esophageal adenocarcinoma (EAC) is rising. This is partly due to the lack of identification of Barrett's esophagus (BE), the main risk factor for EAC. Identifying neoplastic BE can allow for endoscopic therapy to prevent EAC. Our aim was to determine how many patients eligible for screening are actually being screened for BE in the primary care setting of a large health system.

METHODS

A digital search algorithm was constructed using the established gastroenterology guidelines and the Kunzmann model for screening for BE. The algorithm was then applied to the electronic medical record of all patients seen in the primary care setting of the health system. A manual review of charts of the identified patients was performed to confirm the high-risk status and determine if screening occurred.

RESULTS

Of 936,371 primary care charts analyzed by the algorithm, 3535 patients (.4%) were determined to be high-risk for BE. Of these 3535 patients, only 1077 (30%) were screened for BE in clinical practice with endoscopy. The algorithm identified 2458 (70%) additional high-risk patients. Of the patients screened in clinical practice, 105 (10%) were found to have BE (10% with neoplasia).

CONCLUSIONS

Numerous screening opportunities for BE are missed in the primary care setting of a large health system. Collaboration between gastroenterology and primary care services is needed to improve the screening rate.

Electronic Health Record-Based Absolute Risk Prediction Model for Esophageal Cancer in the Chinese Population: Model Development and External Validation

BACKGROUND

China has the largest burden of esophageal cancer (EC). Prediction models can be used to identify high-risk individuals for intensive lifestyle interventions and endoscopy screening. However, the current prediction models are limited by small sample size and a lack of external validation, and none of them can be embedded into the booming electronic health records (EHRs) in China.

OBJECTIVE

This study aims to develop and validate absolute risk prediction models for EC in the Chinese population. In particular, we assessed whether models that contain only EHR-available predictors performed well.

METHODS

A prospective cohort recruiting 510,145 participants free of cancer from both high EC-risk and low EC-risk areas in China was used to develop EC models. Another prospective cohort of 18,441 participants was used for validation. A flexible parametric model was used to develop a 10-year absolute risk model by considering the competing risks (full model). The full model was then abbreviated by keeping only EHR-available predictors. We internally and externally validated the models by using the area under the receiver operating characteristic curve (AUC) and calibration plots and compared them based on classification measures.

RESULTS

During a median of 11.1 years of follow-up, we observed 2550 EC incident cases. The models consisted of age, sex, regional EC-risk level (high-risk areas: 2 study regions; low-risk areas: 8 regions), education, family history of cancer (simple model), smoking, alcohol use, BMI (intermediate model), physical activity, hot tea consumption, and fresh fruit consumption (full model). The performance was only slightly compromised after the abbreviation. The simple and intermediate models showed good calibration and excellent discriminating ability with AUCs (95% CIs) of 0.822 (0.783-0.861) and 0.830 (0.792-0.867) in the external validation and 0.871 (0.858-0.884) and 0.879 (0.867-0.892) in the internal validation, respectively.

CONCLUSIONS

Three nested 10-year EC absolute risk prediction models for Chinese adults aged 30-79 years were developed and validated, which may be particularly useful for populations in low EC-risk areas. Even the simple model with only 5 predictors available from EHRs had excellent discrimination and good calibration, indicating its potential for broader use in tailored EC prevention. The simple and intermediate models have the potential to be widely used for both primary and secondary prevention of EC.

Diagnosis and Management of Barrett's Esophagus

Barrett's esophagus is a metaplastic change of esophageal mucosa, which can be characterized by its salmon-colored lining and the presence of columnar epithelium with goblet cells. It is a well-established precancerous state of esophageal adenocarcinoma, a tumor with very poor survival rates, which incidence is rapidly growing. Despite numerous research, the debate about its diagnosis and management is still ongoing. This article aims to provide an overview of the current recommendations and new discoveries regarding the subject.

Development and validation of a multivariable risk factor questionnaire to detect oesophageal cancer in 2-week wait patients

INTRODUCTION

Oesophageal cancer is associated with poor health outcomes. Upper GI (UGI) endoscopy is the gold standard for diagnosis but is associated with patient discomfort and low yield for cancer. We used a machine learning approach to create a model which predicted oesophageal cancer based on questionnaire responses.

METHODS

We used data from 2 separate prospective cross-sectional studies: the Saliva to Predict rIsk of disease using Transcriptomics and epigenetics (SPIT) study and predicting RIsk of diSease using detailed Questionnaires (RISQ) study. We recruited patients from National Health Service (NHS) suspected cancer pathways as well as patients with known cancer. We identified patient characteristics and questionnaire responses which were most associated with the development of oesophageal cancer. Using the SPIT dataset, we trained seven different machine learning models, selecting the best area under the receiver operator curve (AUC) to create our final model. We further applied a cost function to maximise cancer detection. We then independently validated the model using the RISQ dataset.

RESULTS

807 patients were included in model training and testing, split in a 70:30 ratio. 294 patients were included in model validation. The best model during training was regularised logistic regression using 17 features (median AUC: 0.81, interquartile range (IQR): 0.69-0.85). For testing and validation datasets, the model achieved an AUC of 0.71 (95% CI: 0.61-0.81) and 0.92 (95% CI: 0.88-0.96) respectively. At a set cut off, our model achieved a sensitivity of 97.6% and specificity of 59.1%. We additionally piloted the model in 12 patients with gastric cancer; 9/12 (75%) of patients were correctly classified.

CONCLUSIONS

We have developed and validated a risk stratification tool using a questionnaire approach. This could aid prioritising patients at high risk of having oesophageal cancer for endoscopy. Our tool could help address endoscopic backlogs caused by the COVID-19 pandemic.

Gastroesophageal Reflux Disease Is Not a Great Screening Criterion: Time to Move on to Other Strategies for Controlling the Burden of Esophageal Adenocarcinoma

ABSTRACT

Gastroesophageal reflux disease (GERD) is key in the pathogenesis of Barrett's esophagus and esophageal adenocarcinoma (EAC). Endoscopic screening of select individuals with GERD symptoms for Barrett's esophagus and EAC has been recommended, but the great majority of patients with EAC had never undergone prior screening, despite over a million esophagogastroduodenoscopies (EGDs) performed annually in the United States among individuals with GERD symptoms. This is likely due to a conflation among providers regarding diagnostic EGD in those with refractory symptoms and screening EGD. An alternative approach is needed that de-emphasizes GERD to avoid confusion and increase uptake of appropriate screening.

Limitations of Heartburn and Other Societies' Criteria in Barrett's Screening for Detecting De Novo Esophageal Adenocarcinoma

BACKGROUND & AIMS

Despite extensive Barrett's esophagus (BE) screening efforts, most patients with esophageal adenocarcinoma (EAC) present de novo. It is unclear how much of this problem is the result of insensitivity or poor applications of current screening guidelines. We aimed to evaluate the sensitivity of guidelines by determining the proportion of prevalent EAC cases that meet the American College of Gastroenterology (ACG) or the British Society of Gastroenterology (BSG) guidelines for BE screening and determine whether changes to criteria would enhance detection.

METHODS

A retrospective single-center cohort from the United States (n = 663) and a prospective multicenter cohort from the United Kingdom (n = 645) were collected and analyzed independently. Screening eligibility was determined as patients with chronic reflux and at least 2 or more risk factors as defined by the guidelines. We calculated the proportion of screening-eligible patients and then compared BE/EAC risk factors between screening-eligible and screening-ineligible patients using the chi-squared or Student t test as appropriate.

RESULTS

In the Mayo clinic cohort there were 54.9% EAC cases and in the UK cohort there were 38.9% EAC cases that were not identified by ACG or BSG screening criteria, respectively. Among patients who did not meet the screening criteria, lack of heartburn was observed in 86.5% in the Mayo clinic cohort and in 61.4% in the UK cohort. Other risk factors that were lacking included obesity (defined as a body mass index of ≥30 kg/m²) and family history of EAC. Eliminating chronic reflux from the ACG/BSG criteria improved eligibility for screening from 45.1% to 81.3% (P < .001) in the Mayo Clinic cohort and from 61.1% (n = 394) to 81.5% (n = 526; P < .001) in the UK cohort. However, reflux may be difficult to ascertain from the history, and by including proton pump inhibitor use status in addition to the BSG criteria, screening eligibility improved by 10.0% in the UK cohort (n = 459; P < .001).

CONCLUSIONS

ACG/BSG BE screening guidelines have limited our ability to detect prevalent EAC. An optimized approach to identifying the individuals most suitable for EAC screening needs to be implemented, particularly one that does not rely on chronic reflux symptoms.

Patient Factors Associated With Gastroesophageal Reflux Disease Diagnostic Evaluation Strategies: A Retrospective Cohort Study Using Real-World Evidence From a Large U.S. Medical Claims Database

Background and Aims

Barrett's esophagus (BE) screening is not highly utilized in the United States, and there are few data describing providers' approach to screening. To fill this gap and guide the implementation of future BE screening strategies, we studied evaluation practice patterns for gastroesophageal reflux disease (GERD) by nongastroenterologists.

Methods

We performed a retrospective cohort study of patients with chronic GERD using health claims data from the United States between 2005 and 2019. We used up to 5 years of data after the diagnosis of chronic GERD to determine patient factors associated with completion of a gastroenterology encounter. We also identified patient factors associated with whether the first gastroenterology encounter was a direct-access upper endoscopy or an office visit.

Results

We identified 484,023 patients diagnosed with chronic GERD by a nongastroenterology provider. The cumulative incidence of completing a gastroenterology encounter within 5 years was 38.7%. Gastrointestinal symptoms, such as dysphagia (adjusted hazard ratio [aHR] = 2.11, 95% confidence interval [CI] = 1.94-2.30), abdominal pain (aHR = 1.89, 95% CI = 1.85-1.94), and melena (aHR = 1.73, 95% CI = 1.65-1.82), were strongly associated with completion of a gastroenterology encounter. The patient factors strongly associated with direct-access upper endoscopy included dysphagia (aHR = 1.68, 95% CI = 1.52-1.85), weight loss (aHR = 1.46, 95% CI = 1.28-1.63), and melena (aHR = 1.42, 95% CI = 1.28-1.56).

Conclusion

A total of 38.7% of patients with chronic GERD complete a gastroenterology encounter within 5 years of diagnosis, and gastrointestinal alarm symptoms are the most strongly associated factors for receiving gastroenterology care. These findings highlight the importance of incorporating primary care providers in the development of new BE screening programs.

Outcomes of upper gastrointestinal cancer screening in high-risk individuals: a population-based prospective study in Northeast China

OBJECTIVES

The strategy for upper gastrointestinal cancer (UGC) screening has not yet been determined, especially in northeast China.

DESIGN

The sample was from an ongoing prospective population-based cohort for cancer screening.

PARTICIPANTS

This study belonged to the Chinese Urban Cancer Screening Program. The analysis was based on the recruitment of participants aged 40-74 in Northeast China from 2016 to 2017. Totally, 39 369 eligible participants were recruited, 8772 were evaluated to be at high risk for UGC, 1957 underwent endoscopy.

OUTCOMES

χ² test and multifactor logistic regression model was performed to analyse influencing factors of participation rate. Receiver operating characteristic curve analysis was applied to evaluate the diagnostic power of the high-risk assessment. The Cox regression model was used to estimate hazard ratio (HR) for the potential value.

RESULTS

The high-risk rate was 22.28% and the participation rate of endoscopy screening was 22.31%. Factors such as age at 45-59 years, female sex, high level of education, occupation for professional and technical personnel, former drinking, secondary smoking, less physical activity, history of trauma or mental depression, history of upper gastrointestinal system disease and family history of UGC were associated with increased participation in endoscopy screening (all the p<0.05). There were five UGCs, 86 oesophageal precancerous lesions and 145 gastric precancerous lesions, and the detection rates were 0.26%, 4.39% and 7.41%, respectively. The detection rate for both oesophageal and gastric lesions increased with age and was higher for men than for women (all the p<0.05). After a 3-year follow-up, 30 UGCs had been diagnosed and the high risk of UGC increased the mortality risk ratio (HR: 1.90, 95% confidence interval (CI) 1.41 to 2.56).

CONCLUSION

The participation rate and outcomes of UGC screening were promising in our study and will provide important reference for evaluating value of UGC screening in China.

Cost-effectiveness of risk-stratified endoscopic screening for esophageal cancer in high-risk areas of China: a modeling study

BACKGROUND AND AIMS

Risk-stratified endoscopic screening (RSES) has been suggested to improve screening efficiency. We aimed to assess the cost-effectiveness of RSES and identify the optimal risk-score thresholds for once in a lifetime esophageal cancer (EC) screening in high-risk areas of China.

METHODS

From a healthcare system perspective, a Markov model was constructed to compare the cost-effectiveness of 13 RSES strategies (under different score thresholds for EC risk), universal endoscopic screening strategy, and no screening. Six cohorts of 100,000 participants with different screening ages (40-65 years) were followed up to age 77 years. The incremental cost-effectiveness ratio (ICER), that is, incremental costs per quality-adjusted life-year (QALY) gained, was the primary outcome.

RESULTS

Compared with no screening, as the score threshold was lowered, additionally gained QALYs increased, with 49 to 172 QALYs and 329 to 1147 QALYs gained from screening performed at 40 and 65 years, respectively. RSES in all age scenarios had ICERs less than the gross domestic product (GDP) per capita, and 11 RSES strategies with score thresholds of 3 to 13 had lower ICERs than universal endoscopic screening. At a willingness-to-pay threshold of the GDP per capita (U.S.$10,276/QALY), RSES at score thresholds of 8 or 9 and universal endoscopic screening were the most cost-effective strategies at ages <55 and ≥55 years, respectively.

CONCLUSIONS

RSES is cost-effective, and score thresholds of 8 or 9 should be considered for screening ages <55 years. For individuals aged ≥55 years, universal endoscopic screening is the optimal strategy.

Risk Prediction Model for Esophageal Cancer Among General Population: A Systematic Review

Objective: The risk prediction model is an effective tool for risk stratification and is expected to play an important role in the early detection and prevention of esophageal cancer. This study sought to summarize the available evidence of esophageal cancer risk predictions models and provide references for their development, validation, and application. Methods: We searched PubMed, EMBASE, and Cochrane Library databases for original articles published in English up to October 22, 2021. Studies that developed or validated a risk prediction model of esophageal cancer and its precancerous lesions were included. Two reviewers independently extracted study characteristics including predictors, model performance and methodology, and assessed risk of bias and applicability with PROBAST (Prediction model Risk Of Bias Assessment Tool). Results: A total of 20 studies including 30 original models were identified. The median area under the receiver operating characteristic curve of risk prediction models was 0.78, ranging from 0.68 to 0.94. Age, smoking, body mass index, sex, upper gastrointestinal symptoms, and family history were the most commonly included predictors. None of the models were assessed as low risk of bias based on PROBST. The major methodological deficiencies were inappropriate date sources, inconsistent definition of predictors and outcomes, and the insufficient number of participants with the outcome. Conclusions: This study systematically reviewed available evidence on risk prediction models for esophageal cancer in general populations. The findings indicate a high risk of bias due to several methodological pitfalls in model development and validation, which limit their application in practice.

Risk prediction models for esophageal cancer: A systematic review and critical appraisal

BACKGROUND AND AIMS

Esophageal cancer risk prediction models allow for risk-stratified endoscopic screening. We aimed to assess the quality of these models developed in the general population.

METHODS

A systematic search of the PubMed and Embase databases from January 2000 through May 2021 was performed. Studies that developed or validated a model of esophageal cancer in the general population were included. Screening, data extraction, and risk of bias (ROB) assessment by the Prediction model Risk Of Bias Assessment Tool (PROBAST) were performed independently by two reviewers.

RESULTS

Of the 13 models included in the qualitative analysis, 8 were developed for esophageal squamous cell carcinoma (ESCC) and the other 5 were developed for esophageal adenocarcinoma (EAC). Only two models conducted external validation. In the ESCC models, cigarette smoking was included in each model, followed by age, sex, and alcohol consumption. For EAC models, cigarette smoking and body mass index were included in each model, and gastroesophageal reflux disease, uses of acid-suppressant medicine, and nonsteroidal anti-inflammatory drug were exclusively included. The discriminative performance was reported in all studies, with C statistics ranging from 0.71 to 0.88, whereas only six models reported calibration. For ROB, all the models had a low risk in participant and outcome, but all models showed high risk in analysis, and 60% of models showed a high risk in predictors, which resulted in all models being classified as having overall high ROB. For model applicability, about 60% of these models had an overall low risk, with 30% of models of high risk and 10% of models of unclear risk, concerning the assessment of participants, predictors, and outcomes.

CONCLUSIONS

Most current risk prediction models of esophageal cancer have a high ROB. Prediction models need further improvement in their quality and applicability to benefit esophageal cancer screening.

Development and Validation of an Esophageal Squamous Cell Carcinoma Risk Prediction Model for Rural Chinese: Multicenter Cohort Study

BACKGROUND

There are rare prediction models for esophageal squamous cell carcinoma (ESCC) for rural Chinese population. We aimed to develop and validate a prediction model for ESCC based on a cohort study for the population.

METHODS

Data of 115,686 participants were collected from esophageal cancer (EC) early diagnosis and treatment of cancer program as derivation cohort while data of 54,750 participants were collected as validation cohort. Risk factors considered included age, sex, smoking status, alcohol drinking status, body mass index (BMI), tea drinking status, marital status, annual household income, source of drinking water, education level, and diet habit. Cox proportional hazards model was used to develop ESCC prediction model at 5 years. Calibration ability, discrimination ability, and decision curve analysis were analyzed in both derivation and validation cohort. A score model was developed based on prediction model.

RESULTS

One hundred eighty-six cases were diagnosed during 556,949.40 person-years follow-up in the derivation cohort while 120 cases from 277,302.70 in the validation cohort. Prediction model included the following variables: age, sex, alcohol drinking status, BMI, tea drinking status, and fresh fruit. The model had good discrimination and calibration performance: R 2, D statistic, and Harrell's C statistic of prediction model were 43.56%, 1.70, and 0.798 in derivation cohort and 45.19%, 1.62, and 0.787 in validation cohort. The calibration analysis showed good coherence between predicted probabilities and observed probabilities while decision curve analysis showed clinical usefulness. The score model was as follows: age (3 for 45-49 years old; 4 for 50-54 years old; 7 for 55-59 years old; 9 for 60-64 years; 10 for 65-69 years), sex (5 for men), BMI (1 for ≤25), alcohol drinking status (2 for alcohol drinkers), tea drinking status (2 for tea drinkers), and fresh fruit (2 for never) and showed good discrimination ability with area under the curve and its 95% confidence interval of 0.792 (0.761,0.822) in the deviation cohort and 0.773 (0.736,0.811) in the validation cohort. The calibration analysis showed great coherence between predicted probabilities and observed probabilities.

CONCLUSIONS

We developed and validated an ESCC prediction model using cohort study with good discrimination and calibration capability which can be used for EC screening for rural Chinese population.

Improved esophageal squamous cell carcinoma screening effectiveness by risk-stratified endoscopic screening: evidence from high-risk areas in China

Background

Risk‐stratified endoscopic screening (RSES), which offers endoscopy to those with a high risk of esophageal cancer, has the potential to increase effectiveness and reduce endoscopic demands compared with the universal screening strategy (i.e., endoscopic screening for all targets without risk prediction). Evidence of RSES in high‐risk areas of China is limited. This study aimed to estimate whether RSES based on a 22‐score esophageal squamous cell carcinoma (ESCC) risk prediction model could optimize the universal endoscopic screening strategy for ESCC screening in high‐risk areas of China.

Methods

Eight epidemiological variables in the ESCC risk prediction model were collected retrospectively from 26,618 individuals aged 40‐69 from three high‐risk areas of China who underwent endoscopic screening between May 2015 and July 2017. The model's performance was estimated using the area under the curve (AUC). Participants were categorized into a high‐risk group and a low‐risk group with a cutoff score having sensitivities of both ESCC and severe dysplasia and above (SDA) at more than 90.0%.

Results

The ESCC risk prediction model had an AUC of 0.80 (95% confidence interval: 0.75–0.84) in this external population. We found that a score of 8 (ranging from 0 to 22) had a sensitivity of 94.2% for ESCC and 92.5% for SDA. The RSES strategy using this threshold score would allow 50.6% of endoscopies to be avoided and save approximately US$ 0.59 million compared to universal endoscopic screening among 26,618 participants. In addition, a higher prevalence of SDA (1.7% vs. 0.9%), a lower number need to screen (60 vs. 111), and a lower average cost per detected SDA (US$ 3.22 thousand vs. US$ 5.45 thousand) could have been obtained by the RSES strategy.

Conclusions

The RSES strategy based on individual risk has the potential to optimize the universal endoscopic screening strategy in ESCC high‐risk areas of China.

Global burden and epidemiology of Barrett oesophagus and oesophageal cancer

Oesophageal cancer is a global health problem; in 2018 there were more than 572,000 people newly diagnosed with oesophageal cancer worldwide. There are two main histological subtypes of oesophageal cancer, oesophageal adenocarcinoma (EAC) and oesophageal squamous cell carcinoma (ESCC), and there has been a dramatic shift in its epidemiology. While the incidence of EAC and its precursor lesion, Barrett oesophagus, has increased in Western populations over the past four decades, the incidence of ESCC has declined in most parts of the world over the same period. ESCC still accounts for the vast majority of all oesophageal cancer cases diagnosed worldwide each year. Prognosis for patients with oesophageal cancer is strongly related to stage at diagnosis. As most patients are diagnosed with late-stage disease, overall 5-year survival for oesophageal cancer remains <20%. Knowledge of epidemiology and risk factors for oesophageal cancer is essential for public health and clinical decisions about risk stratification, screening and prevention. The goal of this Review is to establish the current epidemiology of oesophageal cancer, with a particular focus on the Western world and the increasing incidence of EAC and Barrett oesophagus.

Validation of Tools for Predicting Incident Adenocarcinoma of the Esophagus or Esophagogastric Junction

INTRODUCTION

Guidelines suggest screening of individuals who are at increased risk of esophageal adenocarcinoma (EAC). Tools for identifying patients at risk of Barrett's esophagus have been validated. Here, we aimed to compare and validate the tools for the primary outcomes of interest: EAC and esophagogastric junction adenocarcinoma (EGJAC).

METHODS

Retrospective longitudinal analysis of the Kaiser Permanente Northern California Multiphasic Health Checkup Cohort, a community-based cohort including 206,974 patients enrolled between 1964 and 1973 followed through 2016. Baseline questionnaires and anthropometrics classified predictor variables for each tool and were linked to cancer registry outcomes. Analyses used logistic regression, Cox proportional hazards regression, and Kaplan-Meier survival curves.

RESULTS

We identified 168 incident EAC cases and 151 EGJAC cases at a mean of 32 years after enrollment (mean follow-up among controls 26 years). Gastroesophageal reflux disease (GERD) symptoms predicted incident EAC (hazard ratio 2.66; 95% confidence interval 1.01, 7.00), but not EGJAC. The Nord-Trøndelag Health Study tool, Kunzmann tool, and Michigan Barrett's Esophagus pREdiction Tool were more accurate than GERD for predicting EAC, with individuals in the fourth quartile of Kunzmann having 17-fold the risk of those in the 1st quartile (hazard ratio = 16.7, 95% confidence interval = 4.72, 58.8). Each tool also predicted incident EGJAC with smaller magnitudes of effect.

DISCUSSION

The study independently validated 4 tools for predicting incident EAC and EGJAC in a large community-based population. The Kunzmann tool appears best calibrated; all appear preferable to using GERD alone for risk stratification. Future studies should determine how best to implement such tools into clinical practice.

Progress in Screening for Barrett's Esophagus: Beyond Standard Upper Endoscopy

The rapid increase in the incidence of esophageal adenocarcinoma in Western populations over the past 4 decades and its associated poor prognosis, unless detected early has generated great interest in screening for the precursor lesion Barrett's esophagus (BE). Recently, there have been significant developments in imaging-based modalities and esophageal cell-sampling devices coupled with biomarker assays. In this review, the authors discuss the rationale for screening for BE and the factors to consider for targeting the at-risk population. They also explore future avenues for research in this area.

An initial screening strategy based on epidemiologic information in esophageal cancer screening: a prospective evaluation in a community-based cancer screening cohort in rural China

BACKGROUND AND AIMS

In China, regional organized esophageal cancer screening programs have been implemented since 2005. However, the implementation of these screening programs is still facing some urgent challenges, especially concerning identifying high-risk individuals. This study aimed to evaluate the risk stratification potential of the current initial assessment strategy used in a mass esophageal squamous cell carcinoma (ESCC) screening program in China.

METHODS

A total of 43,875 participants without a previous cancer history enrolled in a mass ESCC screening program in China from 2007 to 2010 who had initial assessment results were included in this study and were followed until December 31, 2015. Eight potential risk factors for ESCC were evaluated in the initial assessment strategy. A comprehensive evaluation of the association of the initial assessment results with ESCC risk was performed by propensity score matching and Cox regression analysis.

RESULTS

During a median follow-up of 5.5 years, 272 individuals developed ESCC. The high-risk population assessed at baseline had a higher risk of ESCC than the non-high-risk population, with a hazard ratio (HR) of 3.11 (95% confidence interval (CI), 2.33-4.14) after adjustment for sex, age, education level, income level, and body mass index. In addition, the initial assessment results of the high-risk population were significantly associated with the risk of all esophageal cancers (HR, 3.30; 95% CI, 2.51-4.33) and upper gastrointestinal cancers (HR, 3.03; 95% CI, 2.43-3.76).

CONCLUSIONS

The initial screening tool in a mass ESCC screening program in China, consisting of 8 accessible variables in epidemiologic surveys, could be helpful for the selection of asymptomatic individuals for priority ESCC screening.

Circulating Levels of Inflammatory and Metabolic Biomarkers and Risk of Esophageal Adenocarcinoma and Barrett Esophagus: Systematic Review and Meta-analysis

Associations between circulating levels of obesity-related biomarkers and risk of esophageal adenocarcinoma and Barrett esophagus have been reported, but the results are inconsistent. A literature search until October 2018 in MEDLINE and EMBASE was performed. Pooled ORs with 95% confidence intervals (CI) were estimated for associations between 13 obesity-related inflammatory and metabolic biomarkers and risk of esophageal adenocarcinoma or Barrett esophagus using random effect meta-analyses. Among 7,641 studies, 19 were eligible for inclusion (12 cross-sectional, two nested case-control, and five cohort studies). Comparing the highest versus lowest categories of circulating biomarker levels, the pooled ORs were increased for leptin (OR, 1.68; 95% CI, 0.95-2.97 for Barrett esophagus), glucose (OR, 1.12; 95% CI, 1.03-1.22 for esophageal adenocarcinoma), insulin (OR, 1.47; 95% CI, 1.06-2.00 for Barrett esophagus), C-reactive protein (CRP; OR, 2.06; 95% CI, 1.28-3.31 for esophageal adenocarcinoma), IL6 (OR, 1.50; 95% CI, 1.03-2.19 for esophageal adenocarcinoma), and soluble TNF receptor 2 (sTNFR-2; OR, 3.16; 95% CI, 1.76-5.65 for esophageal adenocarcinoma). No associations were identified for adiponectin, ghrelin, insulin-like growth factor 1, insulin-like growth factor-binding protein 3, triglycerides, IL8, or TNFα. Higher circulating levels of leptin, glucose, insulin, CRP, IL6, and sTNFR-2 may be associated with an increased risk of esophageal adenocarcinoma or Barrett esophagus. More prospective studies are required to identify biomarkers that can help select high-risk individuals for targeted prevention and early detection.

Selection of high-risk individuals for esophageal cancer screening: A prediction model of esophageal squamous cell carcinoma based on a multicenter screening cohort in rural China

The mortality benefit of esophageal squamous cell carcinoma (ESCC) screening has been reported in several studies; however, the results of ESCC screening programs in China are suboptimal. Our study aimed to develop an ESCC risk prediction model to identify high-risk individuals for population-based esophageal cancer screening. In total, 86 745 participants enrolled in a population-based esophageal cancer screening program in rural China between 2007 and 2012 were included in the present study and followed up until December 31, 2015. Models for identifying individuals at risk of ESCC within 3 years were created using logistic regressions. The area under the receiver operating curve (AUC) was determined to estimate the model's overall performance. A total of 298 individuals were diagnosed with ESCC within 3 years after baseline. The model of ESCC included the predictors of age, sex, family history of upper gastrointestinal cancer, smoking status, alarming symptoms of retrosternal pain, back pain or neck pain, consumption of salted food and fresh fruits and disease history of peptic ulcer or esophagitis (AUC of 0.81; 95% confidence interval: 0.78-0.83). Compared to the current prescreening strategy in our program, the cut-off value of 10 in the score-based model could result in 3.11% fewer individuals subjected to endoscopies and present higher sensitivity, slightly higher specificity and lower number needed to screen. This score-based risk prediction model of ESCC based on eight epidemiological risk factors could increase the efficiency of the esophageal cancer screening program in rural China.

Validation and Comparison of Tools for Selecting Individuals to Screen for Barrett's Esophagus and Early Neoplasia

BACKGROUND & AIMS

Guidelines suggest endoscopic screening of individuals who are at increased risk for Barrett's esophagus (BE) and esophageal adenocarcinoma. Tools based on clinical factors are available for identifying patients at risk, but only some have been validated. We aimed to compare and validate available tools.

METHODS

We performed a prospective study of 1241 patients, ages 40 to 79 years, presenting either for their first esophagogastroduodenoscopy (EGD) or their first endoscopic therapy of early neoplastic BE, from April 2015 through June 2018. We calculated risk scores for 6 previously published tools (the Gerson, Locke, Thrift, Michigan BE pREdiction Tool [M-BERET], Nord-Trøndelag Health Study [HUNT], and Kunzmann tools). We also investigated the accuracy of frequency and duration of gastroesophageal reflux disease (GERD), using data from a randomly selected 50% of patients undergoing their first EGD. We compared the ability of all these tools to discriminate patients with BE and early neoplasia from patients without BE, using findings from endoscopy as the reference standard.

RESULTS

BE was detected in 81 of 1152 patients during their first EGD (7.0%). GERD symptoms alone identified patients with BE with an area under the receiver operating characteristic curve (AuROC) of 0.579. All of the tools were more accurate in identifying patients with BE than the frequency and duration of GERD (AuROC for GERD, 0.579 vs range for other tools, 0.660-0.695), and predicted risk correlated well with observed risk (calibration). The AUROCs of the HUNT tool (0.796), the M-BERET (0.773), and the Kunzmann tool (0.763) were comparable in discriminating between patients with early neoplasia (n = 94) vs no BE. Each tool was more accurate in discriminating BE with early neoplasia than GERD frequency and duration alone (AuROC, 0.667; P < .01).

CONCLUSIONS

The HUNT, M-BERET, and Kunzmann tools identify patients with BE with AuROC values ranging from 0.665 to 0.695, and discriminate patients with early neoplasia from patients without BE with AuROC values ranging from 0.763 to 0.796. These tools are more accurate than frequency and duration of GERD in identifying individuals at risk for neoplastic BE.

Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach

Background

Screening for Barrett's Oesophagus (BE) relies on endoscopy which is invasive and has a low yield. This study aimed to develop and externally validate a simple symptom and risk-factor questionnaire to screen for patients with BE.

Methods

Questionnaires from 1299 patients in the BEST2 case-controlled study were analysed: 880 had BE including 40 with invasive oesophageal adenocarcinoma (OAC) and 419 were controls. This was randomly split into a training cohort of 776 patients and an internal validation cohort of 523 patients. External validation included 398 patients from the BOOST case-controlled study: 198 with BE (23 with OAC) and 200 controls. Identification of independently important diagnostic features was undertaken using machine learning techniques information gain (IG) and correlation based feature selection (CFS). Multiple classification tools were assessed to create a multi-variable risk prediction model. Internal validation was followed by external validation in the independent dataset.

Findings

The BEST2 study included 40 features. Of these, 24 added IG but following CFS, only 8 demonstrated independent diagnostic value including age, gender, smoking, waist circumference, frequency of stomach pain, duration of heartburn and acid taste and taking of acid suppression medicines. Logistic regression offered the highest prediction quality with AUC (area under the receiver operator curve) of 0.87. In the internal validation set, AUC was 0.86. In the BOOST external validation set, AUC was 0.81.

Interpretation

The diagnostic model offers valid predictions of diagnosis of BE in patients with symptomatic gastroesophageal reflux, assisting in identifying who should go forward to invasive testing. Overweight men who have been taking stomach medicines for a long time may merit particular consideration for further testing. The risk prediction tool is quick and simple to administer but will need further calibration and validation in a prospective study in primary care.

Funding

Charles Wolfson Trust and Guts UK.

Determinants of participation and detection rate of upper gastrointestinal cancer from population-based screening program in China

Upper gastrointestinal cancer (UGC) screening has been widely implemented in many Asian countries. However, there is little evidence of participation and diagnostic yields in population-based UGC screening in China. The participation rate and detection of upper gastrointestinal lesions in this program were reported and related factors were explored. The analysis was conducted in the context of the Cancer Screening Program in Urban China, which recruited 179 002 eligible participants aged 40-74 years from three cities in Henan province from 2013 to 2017. A total of 43 423 participants were evaluated to be high risk for esophageal cancer or gastric cancer by an established risk score system and were subsequently recommended for endoscopy. Of 43 423 with high risk for UGC, 7996 subjects undertook endoscopy (participation rate of 18.4%). We found that male sex, high level of education, marriage, smoking, current alcohol drinking, lack of physical activity, history of upper gastrointestinal system disease, and family history of UGC were associated with increased participation of endoscopy screening. Overall, 15 UGC (0.19%), 275 squamous epithelial dysplasia (3.44%), and 33 intraepithelial neoplasm (0.41%) cases were detected. Several factors including age, sex, smoking, current alcohol drinking, lack of physical activity, and dietary intake of processed meat were identified to be associated with the presence of upper gastrointestinal lesions. Health promotion campaigns targeting the specific group of individuals identified in our study will be helpful for improvement of the adherence of UGC screening in population-based cancer screening programs. Participant rate and yield of UGC screening will provide important references for evaluating the effectiveness and cost-effectiveness of cancer screening in China.

Interactive decision support for esophageal adenocarcinoma screening and surveillance

BACKGROUND

A key barrier to controlling esophageal adenocarcinoma (EAC) is identifying those most likely to benefit from screening and surveillance. We aimed to develop an online educational tool, termed IC-RISC™, for providers and patients to estimate more precisely their absolute risk of developing EAC, interpret this estimate in the context of risk of dying from other causes, and aid in decision-making.

RESULTS

U.S. incidence and mortality data and published relative risk estimates from observational studies and clinical trials were used to calculate absolute risk of EAC over 10 years adjusting for competing risks. These input parameters varied depending on presence of the key precursor, Barrett's esophagus. The open source application works across common devices to gather risk factor data and graphically illustrate estimated risk on a single page. Changes to input data are immediately reflected in the colored graphs. We used the calculator to compare the risk distribution between EAC cases and controls from six population-based studies to gain insight into the discrimination metrics of current practice guidelines for screening, observing that current guidelines sacrifice a significant amount of specificity to identify 78-86% of eventual cases in the US population.

CONCLUSIONS

This educational tool provides a simple and rapid means to graphically communicate risk of EAC in the context of other health risks, facilitates "what-if" scenarios regarding potential preventative actions, and can inform discussions regarding screening, surveillance and treatment options. Its generic architecture lends itself to being easily extended to other cancers with distinct pathways and/or intermediate stages, such as hepatocellular cancer. IC-RISC™ extends current qualitative clinical practice guidelines into a quantitative assessment, which brings the possibility of preventative actions being offered to persons not currently targeted for screening and, conversely, reducing unnecessary procedures in those at low risk. Prospective validation and application to existing well-characterized cohort studies are needed.

External validation of a model to determine risk of progression of Barrett's oesophagus to neoplasia

BACKGROUND

A risk prediction model containing sex, smoking history, Barrett's oesophagus length and presence of low-grade dysplasia was found to identify individuals at a higher risk of progression to oesophageal adenocarcinoma or high-grade dysplasia.

AIM

To externally validate the model predicting risk of progression from Barrett's oesophagus to neoplasia and assess the predictive utility of additional factors.

METHODS

We conducted a retrospective cohort study among individuals from the population-based Northern Ireland Barrett's register with a histologically confirmed diagnosis of Barrett's oesophagus (with intestinal metaplasia) between 1993 and 2005. The association between a points based model and risk of progression to high-grade dysplasia or oesophageal adenocarcinoma until 2010 was assessed using Cox Proportional Hazards model. Model performance was assessed using area under the receiver operating characteristics curves (AUROC), sensitivity and specificity.

RESULTS

We identified 1198 individuals with Barrett's oesophagus of whom 54 progressed. The model discriminated reasonably well between progressors and nonprogressors, with an AUROC of 0.70 (95% CI 0.63-0.78). When categorised into low, intermediate and high risk groups, the AUROC was 0.68 (95% CI 0.61-0.74). Compared to using data on dysplasia and segment length for risk stratification, the model resulted in a net reclassification improvement of 20.9%.

CONCLUSIONS

This external validation provides further evidence that a model based on sex, smoking, Barrett's segment length and baseline low-grade dysplasia may help to risk stratify patients after an initial diagnosis of Barrett's oesophagus. The model also performed better than the use of low-grade dysplasia status alone for risk-stratification.

Barrett's esophagus: novel strategies for screening and surveillance

Barrett’s esophagus is the precursor lesion for esophageal adenocarcinoma. Screening and surveillance of Barrett’s esophagus are undertaken with the goal of earlier detection and lowering the mortality from esophageal adenocarcinoma. The widely used technique is standard esophagogastroduodenoscopy with biopsies per the Seattle protocol for screening and surveillance of Barrett’s esophagus. Surveillance intervals vary depending on the degree of dysplasia with endoscopic eradication therapy confined to patients with Barrett’s esophagus and confirmed dysplasia. In this review, we present various novel techniques for screening of Barrett’s esophagus such as unsedated transnasal endoscopy, cytosponge with trefoil factor-3, balloon cytology, esophageal capsule endoscopy, liquid biopsy, electronic nose, and oral microbiome. In addition, advanced imaging techniques such as narrow band imaging, dye-based chromoendoscopy, confocal laser endomicroscopy, volumetric laser endomicroscopy, and wide-area transepithelial sampling with computer-assisted three-dimensional analysis developed for better detection of dysplasia are also reviewed.

Information on Genetic Variants Does Not Increase Identification of Individuals at Risk of Esophageal Adenocarcinoma Compared to Clinical Risk Factors

We previously developed a tool that identified individuals who later developed esophageal adenocarcinoma (based on age, sex, body mass index, smoking status, and prior esophageal conditions) with an area under the curve of 0.80. In this study, we collected data from 329,463 individuals in the UK Biobank cohort who were tested for genetic susceptibility to esophageal adenocarcinoma (a polygenic risk score based on 18 recognized genetic variants). We found that after inclusion of this genetic information, the area under the curve for identification of individuals who developed esophageal adenocarcinoma remained at 0.80. Testing for genetic variants associated with esophageal adenocarcinoma therefore seems unlikely to improve identification of individuals at risk of esophageal adenocarcinoma.

Risk factors for oesophageal cancer

The two main histological subtypes of oesophageal cancer, squamous cell carcinoma and adenocarcinoma, have distinct risk factor profiles. For oesophageal squamous cell carcinoma, tobacco smoking and excess alcohol use are the main risk factors. For adenocarcinoma, gastro-oesophageal reflux disease and obesity are main risk factors, whereas tobacco smoking is a moderately strong risk factor and infection with Helicobacter pylori decreases the risk. Dietary factors may influence the risk of both types of oesophageal cancer. Genetic factors are involved in the aetiology, but their influence is generally low. The striking male predominance in oesophageal adenocarcinoma is unexplained, although sex hormones may play a role. Risk prediction models combining information on multiple risk factors have shown promising potential in identifying high-risk individuals for targeted prevention and early detection, which should prompt further studies. More high-quality research efforts are warranted for better understanding of the aetiology of oesophageal cancer, particularly in developing countries.