Endoscopic causes and characteristics of missed gastric cancers after endoscopic submucosal dissection

Risk factors of missed early gastric cancer in endoscopic resected population: a retrospective, case-control study

BACKGROUND

Missed early gastric cancer (MEGC) is prevalent during esophagogastroduodenoscopy (EGD), which is the first-line recommended strategy for detecting early gastric cancer (EGC). Hence, we explored the risk factors for MEGC and different types of MEGC, based on the endoscopic resected population.

METHODS

This retrospective, case-control study was conducted at Nanjing Drum Tower Hospital (NJDTH). We included patients who were diagnosed with EGC during screening EGD, underwent endoscopic resection, and were confirmed by postoperative pathology at the NJDTH from January 2014 to December 2021, and classified them into different types according to the different root causes of misses. Univariable, multivariable, subgroup and propensity score analyses were used to explore the risk factors for MEGC and different types of MEGC.

RESULTS

A total of 447 patients, comprising 345 with initially detected early gastric cancer (IDEGC) and 102 with MEGC, were included in this study. Larger size (≥ 1 cm) (OR 0.45, 95% CI 0.27-0.74, P = 0.002) and invasion depth of submucosa (OR 0.26, 95% CI 0.10-0.69, P = 0.007) were negatively associated with MEGC. Use of sedation (OR 0.32, 95% CI 0.20-0.52, P < 0.001) and longer observation time (OR 0.60, 95% CI 0.37-0.96, P = 0.034) exhibited protective effect on MEGC.

CONCLUSIONS

Smaller and more superficial EGC lesions are more susceptible to misdiagnosis. The use of sedation and prolonged observation time during EGD could help reduce the occurrence of MEGC.

Predicting early gastric cancer risk using machine learning: A population-based retrospective study

Background

Early detection and treatment are crucial for reducing gastrointestinal tumour-related mortality. The diagnostic efficiency of the most commonly used diagnostic markers for gastric cancer (GC) is not very high. A single laboratory test cannot meet the requirements of early screening, and machine learning methods are needed to aid the early diagnosis of GC by combining multiple indicators.

Methods

Based on the XGBoost algorithm, a new model was developed to distinguish between GC and precancerous lesions in newly admitted patients between 2018 and 2023 using multiple laboratory tests. We evaluated the ability of the prediction score derived from this model to predict early GC. In addition, we investigated the efficacy of the model in correctly screening for GC given negative protein tumour marker results.

Results

The XHGC20 model constructed using the XGBoost algorithm could distinguish GC from precancerous disease well (area under the receiver operating characteristic curve [AUC] = 0.901), with a sensitivity, specificity and cut-off value of 0.830, 0.806 and 0.265, respectively. The prediction score was very effective in the diagnosis of early GC. When the cut-off value was 0.27, and the AUC was 0.888, the sensitivity and specificity were 0.797 and 0.807, respectively. The model was also effective at evaluating GC given negative conventional markers (AUC = 0.970), with the sensitivity and specificity of 0.941 and 0.906, respectively, which helped to reduce the rate of missed diagnoses.

Conclusions

The XHGC20 model established by the XGBoost algorithm integrates information from 20 clinical laboratory tests and can aid in the early screening of GC, providing a useful new method for auxiliary laboratory diagnosis.