Update and validation of a diagnostic model to identify prevalent malignant lesions in esophagus in general population

Construction and validation of serum Metabolic Risk Score for early warning of malignancy in esophagus

Using noninvasive biomarkers to identify high-risk individuals prior to endoscopic examination is crucial for optimization of screening strategies for esophageal squamous cell carcinoma (ESCC). We conducted a nested case-control study based on two community-based screening cohorts to evaluate the warning value of serum metabolites for esophageal malignancy. The serum samples were collected at enrollment when the cases had not been diagnosed. We identified 74 differential metabolites and two prominent perturbed metabolic pathways, and constructed Metabolic Risk Score (MRS) based on 22 selected metabolic predictors. The MRS generated an area under the receiver operating characteristics curve (AUC) of 0.815. The model performed well for the within-1-year interval (AUC: 0.868) and 1-to-5-year interval (AUC: 0.845) from blood draw to diagnosis, but showed limited ability in predicting long-term cases (>5 years). In summary, the MRS could serve as a potential early warning and risk stratification tool for establishing a precision strategy of ESCC screening.

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.

Challenge and future of cancer screening in China: Insights from esophageal cancer screening practice

Cancer stands as a prominent public health concern in China, with elusive intervention targets due to unidentified high-risk causal factors for most cancers. Consequently, emphasis has shifted towards screening, diagnosing, and treating early cancer cases within the general population. However, China faces considerable obstacles in its cancer prevention and control efforts, attributing to the complexity and heterogeneity of the occurrence, progression, and prognosis of malignant tumors across populations, time, and regions. Taking esophageal cancer screening practices as an example, this review outlines the importance and assessment of cancer screening, delineating major challenges in China's cancer prevention and control: 1) limited comprehension of cancer's natural history; 2) lack of "China Evidence" supporting screening effectiveness and value; 3) compromised efficiency and accuracy in current screening modality; and 4) insufficient sustainability of the current screening practices and translation of relevant scientific research achievements. To address these challenges, we propose potential coping strategies: 1) establishing tailored technologies and pathways for cancer prevention and control based on population-based and clinical epidemiological studies using high-quality designs; 2) breaking conventional constraints to establish a novel cancer screening strategy aligned with real-world needs; and 3) establishing enhanced communication platforms among scientific research teams, policymakers, and industrial institutions to foster collaboration and innovation.

The landscape of cancer research and cancer care in China

The rising cancer incidence rate in China poses a substantial public health concern, although there have been remarkable improvements in the country's cancer mortality and survival rates. In this Review, we outline the current landscape and future directions of cancer care and research in China. We discuss national screening programs and strategies for cancer detection and delve into the evolving landscape of cancer care, emphasizing the adoption of multidisciplinary, comprehensive treatment and precision oncology. Additionally, we examine changes in drug research and development policies that have enabled approval of new drugs. Finally, we look to the future, highlighting key priorities and identifying gaps. Effectively addressing challenges and seizing opportunities associated with cancer research in China will enable the development of targeted approaches to alleviate the global burden of cancer.

Esophageal cancer in China: Practice and research in the new era

China, as the one of the largest developing countries in the world and with about one-fifth of the global population, is bearing an increasing burden on health from cancer. In the area of esophageal cancer (EC), China accounts for more than 50% of the global cases, with this disease being a particularly worse for those in disadvantaged populations. Along with China's socioeconomic condition, the epidemiology, diagnosis, therapeutics and research of EC have developed throughout the 21st century. In the current review, existing control measures for EC in China are outlined, including the incidence, mortality, screening, clinical diagnosis, multidisciplinary treatment and research landscape. EC in China are very different from those in some other parts of the world, especially in Western countries. Core measures that could contribute to the prevention of EC and improve clinical outcomes in patients of less developed countries and beyond are recommended. International cooperation among academia, government and industry is especially warranted in global EC control.

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.