Development and Validation of a Risk Prediction Model for Esophageal Squamous Cell Carcinoma Using Cohort Studies

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.

Assessing chronic gestational exposure to environmental chemicals in pregnant women: Advancing the co-PBK model

Vulnerable populations, such as pregnant women and their fetuses, confront potential health risks due to exposure to environmental toxic compounds. Computational methods have been popular in assessing chemical exposure to populations, contrasting with traditional cohort studies for human biomonitoring. This study proposes a screening-level approach based on physiologically based kinetic (PBK) modeling to evaluate the steady-state exposure of pregnant women to environmental chemicals throughout pregnancy. To exemplify the modeling application, naphthalene was chosen. Simulation results indicated that maternal fat exhibited significant bioaccumulation potential, with the log-transformed BTF of naphthalene at 0.51 mg kg-1 per mg d-1 in the steady state. The placenta was primarily exposed to 0.83 mg/d naphthalene for a 75.2 kg pregnant woman, considering all exposure routes. In the fetal structure, single-organ fetal PBK modeling estimated a naphthalene exposure of 123.64 mg/d to the entire fetus, while multiple-organ fetal PBK modeling further revealed the bioaccumulation highest in fat tissue. The liver identified as the vital organ for metabolism, kBioT,LiverM was demonstrated with the highest sensitivity among rate constants in the maternal body. Furthermore, the first-order kinetic rate constants related to the placenta and blood were found to impact the distribution process of naphthalene in the fetus, influencing gestational exposure. In conclusion, urgent attention is needed to develop a computational biomonitoring tool for assessing toxic chemical exposure in vulnerable populations.

Laboratory blood parameters and machine learning for the prognosis of esophageal squamous cell carcinoma

Background

In contemporary study, the death of esophageal squamous cell carcinoma (ESCC) patients need precise and expedient prognostic methodologies.

Objective

To develop and validate a prognostic model tailored to ESCC patients, leveraging the power of machine learning (ML) techniques and drawing insights from comprehensive datasets of laboratory-derived blood parameters.

Methods

Three ML approaches, including Gradient Boosting Machine (GBM), Random Survival Forest (RSF), and the classical Cox method, were employed to develop models on a dataset of 2521 ESCC patients with 27 features. The models were evaluated by concordance index (C-index) and time receiver operating characteristics (Time ROC) curves. We used the optimal model to evaluate the correlation between features and prognosis and divide patients into low- and high-risk groups by risk stratification. Its performance was analyzed by Kaplan-Meier curve and the comparison with AJCC8 stage. We further evaluate the comprehensive effectiveness of the model in ESCC subgroup by risk score and KDE (kernel density estimation) plotting.

Results

RSF's C-index (0.746) and AUC (three-year AUC 0.761, five-year AUC 0.771) had slight advantage over GBM and the classical Cox method. Subsequently, 14 features such as N stage, T stage, surgical margin, tumor length, age, Dissected LN number, MCH, Na, FIB, DBIL, CL, treatment, vascular invasion, and tumor grade were selected to build the model. Based on these, we found significant difference for survival rate between low-(3-year OS 81.8%, 5-year OS 69.8%) and high-risk (3-year OS 25.1%, 5-year OS 11.5%) patients in training set, which was also verified in test set (all P < 0.0001). Compared with the AJCC8th stage system, it showed a greater discriminative ability which is also in good agreement with its staging ability.

Conclusion

We developed an ESCC prognostic model with good performance by clinical features and laboratory blood parameters.

Development of a TLR-Based Model That Can Predict Prognosis, Tumor Microenvironment, and Drug Response for Esophageal Squamous Cell Carcinoma

The toll-like receptor (TLR) family is an important class of proteins involved in the immune response. However, little is known about the association between TLRs and Esophageal squamous cell cancer (ESCC). We explored differentially expressed genes (DEGs) between ESCC and esophagus tissues in TCGA and GTEx database. By taking the intersection with TLR gene set and using univariate Cox analysis and multivariate Cox regression analysis to discriminate the hub genes, we created a TLR-prognostic model. Our model separated patients with ESCC into high- and low-risk score (RS) groups. Prognostic analysis was performed with Kaplan-Meier curves. The two groups were also compared regarding tumor immune microenvironment and drug sensitivity. Six hub genes (including CD36, LGR4, MAP2K3, NINJ1, PIK3R1, and TRAF3) were screened to construct a TLR-prognostic model. High-RS group had a worse survival (p < 0.01), lower immune checkpoint expression (p < 0.05), immune cell abundance (p < 0.05) and decreased sensitivity to Epirubicin (p < 0.001), 5-fluorouracil (p < 0.0001), Sorafenib (p < 0.01) and Oxaliplatin (p < 0.05). We constructed a TLR-based model, which could be used to assess the prognosis of patients with ESCC, provide new insights into drug treatment for ESCC patients and investigate the TME and drug response.

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).

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.

Machine learning-based automated sponge cytology for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction: a nationwide, multicohort, prospective study

BACKGROUND

Oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction have a dismal prognosis, and early detection is key to reduce mortality. However, early detection depends on upper gastrointestinal endoscopy, which is not feasible to implement at a population level. We aimed to develop and validate a fully automated machine learning-based prediction tool integrating a minimally invasive sponge cytology test and epidemiological risk factors for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction before endoscopy.

METHODS

For this multicohort prospective study, we enrolled participants aged 40-75 years undergoing upper gastrointestinal endoscopy screening at 39 tertiary or secondary hospitals in China for model training and testing, and included community-based screening participants for further validation. All participants underwent questionnaire surveys, sponge cytology testing, and endoscopy in a sequential manner. We trained machine learning models to predict a composite outcome of high-grade lesions, defined as histology-confirmed high-grade intraepithelial neoplasia and carcinoma of the oesophagus and oesophagogastric junction. The predictive features included 105 cytological and 15 epidemiological features. Model performance was primarily measured with the area under the receiver operating characteristic curve (AUROC) and average precision. The performance measures for cytologists with AI assistance was also assessed.

FINDINGS

Between Jan 1, 2021, and June 30, 2022, 17 498 eligible participants were involved in model training and validation. In the testing set, the AUROC of the final model was 0·960 (95% CI 0·937 to 0·977) and the average precision was 0·482 (0·470 to 0·494). The model achieved similar performance to consensus of cytologists with AI assistance (AUROC 0·955 [95% CI 0·933 to 0·975]; p=0·749; difference 0·005, 95% CI, -0·011 to 0·020). If the model-defined moderate-risk and high-risk groups were referred for endoscopy, the sensitivity was 94·5% (95% CI 88·8 to 97·5), specificity was 91·9% (91·2 to 92·5), and the predictive positive value was 18·4% (15·6 to 21·6), and 90·3% of endoscopies could be avoided. Further validation in community-based screening showed that the AUROC of the model was 0·964 (95% CI 0·920 to 0·990), and 92·8% of endoscopies could be avoided after risk stratification.

INTERPRETATION

We developed a prediction tool with favourable performance for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction. This approach could prevent the need for endoscopy screening in many low-risk individuals and ensure resource optimisation by prioritising high-risk individuals.

FUNDING

Science and Technology Commission of Shanghai Municipality.

Cost-effectiveness of toripalimab plus chemotherapy for advanced esophageal squamous cell carcinoma

BACKGROUND

Toripalimab is an immune checkpoint inhibitor (ICI) against programmed death ligand 1 (PD-L1). It has been approved for advanced esophageal squamous cell carcinoma (ESCC) as the first-line treatment due to significantly improved progression-free survival (PFS) and overall survival (OS) in the JUPITER-06 trial.

AIM

This study aimed to compare the cost-effectiveness between toripalimab plus chemotherapy and placebo plus chemotherapy from the perspective of the Chinese health system.

METHOD

The study developed a 3-year partitioned survival model to assess costs and outcomes in two treatment groups with or without toripalimab. The critical indicator was the incremental cost-effectiveness ratio (ICER). Scenario and sensitivity analyses were performed to evaluate the robustness of the findings and identify the parameters with the greatest impact on cost-effectiveness.

RESULTS

In the base case analysis, the incremental effectiveness and cost of toripalimab plus chemotherapy versus placebo plus chemotherapy were 0.26 quality-adjusted life year (QALYs) and $11,254.84, respectively, resulting in an ICER of $43,405.09/QALY, higher than the 2021 willingness-to-pay threshold in China ($37,658.70/QALY). The results were sensitive to the utility of PFS, the incidence of neutropenia in the toripalimab group, and the cost of toripalimab. The toripalimab plus chemotherapy group was cost-effective only if the price of toripalimab decreased by more than 40%.

CONCLUSION

Adding toripalimab to chemotherapy was not cost-effective in patients with advanced ESCC in China.

Clinical Outcome-Related Cancer Pathways and Mutational Signatures in Patients With Unresectable Esophageal Squamous Cell Carcinoma Treated With Chemoradiotherapy

PURPOSE

Definitive chemoradiotherapy (dCRT) is a standard-of-care for locally advanced unresectable esophageal squamous cell carcinoma (ESCC). However, even in individuals treated with the same dCRT regimen, differences in the local control rate and radiation-induced thoracic toxicity exist (radiation-induced esophagitis [RIE]).

METHODS AND MATERIALS

Here, we describe a comprehensive genomic evaluation of pretreatment tumor tissue samples from 183 patients with ESCC using targeted sequencing of 474 cancer-related genes. The association between endpoints (progression-free survival [PFS], overall survival, locoregional relapse-free survival, distant metastasis-free survival), toxicity (RIE) and genomic features, including altered pathways and the mutational signature, was analyzed. An independent cohort of 84 stage II-III patients with ESCC was used for validation.

RESULTS

Gene alterations in the cell cycle pathway were identified in 87% of cases. Other frequently altered pathways included PI3K-AKT (45.9%), NOTCH (38.3%), NRF2 (36.6%), RKT-RAS (28.4%), and homologous recombination repair (HRR; 20.2%). HRR pathway alterations correlated with shortened PFS (mutation vs wild-type: 9.00 vs 14.40 months, hazard ratio, 2.10; 95% confidence interval, 1.29-3.44), while altered RTK-RAS pathways were correlated with worse overall survival in patients with ESCC treated with chemoradiotherapy (mutation vs wild-type: 23.70 vs 33.50 months; hazard ratio, 1.65; 95% confidence interval, 1.01-2.69). Furthermore, enrichment of apolipoprotein B mRNA editing enzyme, catalytic polypeptide (APOBEC) signatures (signatures 2 and 13) was identified in ESCC tumors with altered HRR pathways. High APOBEC signatures and an altered HRR pathway were correlated with poor prognoses in dCRT-treated ESCC. Moreover, the APOBEC signature and/or the presence of HRR pathway alterations were associated with poor PFS and overall survival, which was validated in an independent whole exome sequence cohort. Notably, the altered HRR pathway was also associated with high-grade RIE toxicity in patients with ESCC.

CONCLUSIONS

Collectively, our results support the use of comprehensive genomic profiling to guide treatment and minimize RIE in patients with ESCC.

Tumor organoid biobank-new platform for medical research

Organoids are a new type of 3D model for tumor research, which makes up for the shortcomings of cell lines and xenograft models, and promotes the development of personalized precision medicine. Long-term culture, expansion and storage of organoids provide the necessary conditions for the establishment of biobanks. Biobanks standardize the collection and preservation of normal or pathological specimens, as well as related clinical information. The tumor organoid biobank has a good quality control system, which is conducive to the clinical transformation and large-scale application of tumor organoids, such as disease modeling, new drug development and high-throughput drug screening. This article summarized the common tumor types of patient-derived organoid (PDO) biobanks and the necessary information for biobank construction, such as the number of organoids, morphology, success rate of culture and resuscitation, pathological types. In our results, we found that patient-derived tumor organoid (PDTO) biobanks were being established more and more, with the Netherlands, the United States, and China establishing the most. Biobanks of colorectal, pancreas, breast, glioma, and bladder cancers were established more, which reflected the relative maturity of culture techniques for these tumors. In addition, we provided insights on the precautions and future development direction of PDTO biobank building.

Development and Validation of Esophageal Squamous Cell Carcinoma Risk Prediction Models Based on an Endoscopic Screening Program

IMPORTANCE

Assessment tools are lacking for screening of esophageal squamous cell cancer (ESCC) in China, especially for the follow-up stage. Risk prediction to optimize the screening procedure is urgently needed.

OBJECTIVE

To develop and validate ESCC prediction models for identifying people at high risk for follow-up decision-making.

DESIGN, SETTING, AND PARTICIPANTS

This open, prospective multicenter diagnostic study has been performed since September 1, 2006, in Shandong Province, China. This study used baseline and follow-up data until December 31, 2021. The data were analyzed between April 6 and May 31, 2022. Eligibility criteria consisted of rural residents aged 40 to 69 years who had no contraindications for endoscopy. Among 161 212 eligible participants, those diagnosed with cancer or who had cancer at baseline, did not complete the questionnaire, were younger than 40 years or older than 69 years, or were detected with severe dysplasia or worse lesions were eliminated from the analysis.

EXPOSURES

Risk factors obtained by questionnaire and endoscopy.

MAIN OUTCOMES AND MEASURES

Pathological diagnosis of ESCC and confirmation by cancer registry data.

RESULTS

In this diagnostic study of 104 129 participants (56.39% women; mean [SD] age, 54.31 [7.64] years), 59 481 (mean [SD] age, 53.83 [7.64] years; 58.55% women) formed the derivation set while 44 648 (mean [SD] age, 54.95 [7.60] years; 53.51% women) formed the validation set. A total of 252 new cases of ESCC were diagnosed during 424 903.50 person-years of follow-up in the derivation cohort and 61 new cases from 177 094.10 person-years follow-up in the validation cohort. Model A included the covariates age, sex, and number of lesions; model B included age, sex, smoking status, alcohol use status, body mass index, annual household income, history of gastrointestinal tract diseases, consumption of pickled food, number of lesions, distinct lesions, and mild or moderate dysplasia. The Harrell C statistic of model A was 0.80 (95% CI, 0.77-0.83) in the derivation set and 0.90 (95% CI, 0.87-0.93) in the validation set; the Harrell C statistic of model B was 0.83 (95% CI, 0.81-0.86) and 0.91 (95% CI, 0.88-0.95), respectively. The models also had good calibration performance and clinical usefulness.

CONCLUSIONS AND RELEVANCE

The findings of this diagnostic study suggest that the models developed are suitable for selecting high-risk populations for follow-up decision-making and optimizing the cancer screening process.

Subjective factors affecting prognosis of 469 patients with esophageal squamous cell carcinoma: a retrospective cohort study of endoscopic screening

Background

To date, no in-depth studies have focused on the impact of various clinical characteristics of esophageal squamous cell carcinoma (ESCC), including its association with subjective symptoms, on patient prognosis. We aimed to investigate the clinical factors that affect the prognosis of patients with ESCC and to clarify how subjective symptoms are related to prognosis.

Methods

We retrospectively evaluated the clinical records of 503 consecutive patients with ESCC from April 2011 to December 2019. Six established prognostic factors for ESCC (body mass index, alcohol drinking, cigarette smoking, sex, clinical stage, and age) and subjective symptoms were used to subgroup patients and analyze survival differences. Next, the patients were divided into two groups: a symptomatic group and an asymptomatic group. In the symptomatic group, differences in the incidence of subjective symptoms according to tumor size, tumor location, macroscopic tumor type, and clinical stage were examined. Finally, subjective symptoms were divided into swallowing-related symptoms and other symptoms, and their prognosis was compared.

Results

Multivariate Cox regression analysis identified sex [hazard ratio (HR) 1.778; 95% CI 1.004–3.149; p = 0.049], TNM classification (HR 6.591; 95% CI 3.438–12.63; p < 0.001), and subjective symptoms (HR 1.986; 95% CI 1.037–3.803; p = 0.0386) as independent risk factors for overall survival. In the symptomatic group, the mean time from symptom onset to diagnosis was 2.4 ± 4.3 months. The incidence of subjective symptoms differed by clinical stage, and the prognosis of patients with swallowing-related symptoms was significantly worse than that of patients with other symptoms.

Conclusion

The results of this study suggest that screening by upper gastrointestinal endoscopy, independent of subjective symptoms (especially swallowing-related symptoms), may play an important role in the early detection and improvement of prognosis of ESCC, although further validation in a large prospective study is needed.

Difference between “Lung Age” and Real Age as a Novel Predictor of Postoperative Complications, Long-Term Survival for Patients with Esophageal Cancer after Minimally Invasive Esophagectomy

Background

This study aimed to investigate whether the difference between “lung age” and real age (L–R) could be useful for the prediction of postoperative complications and long-term survival in patients with esophageal cancer followed by minimally invasive esophagectomy (MIE).

Methods

This retrospective cohort study included 625 consecutive patients who had undergone MIE. “Lung age” was determined by the calculation method proposed by the Japanese Respiratory Society. According to L–R, patients were classified into three groups: group A: L–R ≦ 0 (n = 104), group B: 15 > L–R > 0 (n = 199), group C: L–R ≥ 15 (n = 322). Clinicopathological factors, postoperative complications evaluated by comprehensive complications index (CCI), and overall survival were compared between the groups. A CCI value >30 indicated a severe postoperative complication.

Results

Male, smoking status, smoking index, chronic obstructive pulmonary disease, American Society of Anesthesiologists status, lung age, and forced expiratory volume in 1 s were associated with group classification. CCI values, postoperative hospital stays, and hospital costs were significantly different among groups. Multivariate analysis indicated that L–R, coronary heart disease, and 3-field lymphadenectomy were significant factors for predicting CCI value >30. Regarding the prediction of CCI value >30, area under the curve value was 0.61(95%: 0.56–0.67), 0.46 (95% CI, 0.40–0.54), and 0.46 (95% CI, 0.40–0.54) for L–R, Fev1, and Fev1%, respectively. Regarding overall survival, there was a significant difference between group A and group B + C (log-rank test: p = 0.03).

Conclusions

Esophageal cancer patients with impaired pulmonary function had a higher risk of severe postoperative complications and poorer prognosis than those with normal pulmonary function. The difference between “lung age” and “real age” seems to be a novel and potential predictor of severe postoperative complications and long-term survival.

Development and validation of a risk prediction model for incident liver cancer

Objective

We aimed to develop and validate a risk prediction model for liver cancer based on routinely available risk factors using the data from UK Biobank prospective cohort study.

Methods

This analysis included 359,489 participants (2,894,807 person-years) without a previous diagnosis of cancer. We used the Fine-Gray regression model to predict the incident risk of liver cancer, accounting for the competing risk of all-cause death. Model discrimination and calibration were validated internally. Decision curve analysis was conducted to quantify the clinical utility of the model. Nomogram was built based on regression coefficients.

Results

Good discrimination performance of the model was observed in both development and validation datasets, with an area under the curve (95% confidence interval) for 5-year risk of 0.782 (0.748-0.816) and 0.771 (0.702-0.840) respectively. The calibration showed fine agreement between observed and predicted risks. The model yielded higher positive net benefits in the decision curve analysis than considering either all participants as being at high or low risk, which indicated good clinical utility.

Conclusion

A new risk prediction model for liver cancer composed of routinely available risk factors was developed. The model had good discrimination, calibration and clinical utility, which may help with the screening and management of liver cancer for general population in the public health field.

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.