Lymph node involvement in gastric adenocarcinoma

Clinicopathologic factors correlated with lymph node metastasis in gastric cancer: a retrospective cohort study involving 5606 patients

BACKGROUND

The identification of risk factors associated with lymph node metastasis (LNM) in gastric cancer will establish a crucial foundation for the implementation of endoscopic operation and multidisciplinary treatment programs.

METHODS

A total of 5606 patients with gastric cancer with comprehensive clinicopathologic data were enrolled through systematic searching and rigorous screening. Of the 5606 patients, 1438 were diagnosed with early gastric cancer (EGC), which would be used for further analysis. Subsequently, univariate and multivariate logistic regression analyses were performed to identify the risk factors.

RESULTS

The rates of LNM in T1a, T1b, T2, T3, T4a, and T4b stage gastric cancer were 7.0%, 19.4%, 48.4%, 77.1%, 83.8%, and 89.6%, respectively. Female (odds ratio [OR], 1.559; P = .032), lower tumor location (OR, 1.773; P = .023), tumor size of >2 cm (OR, 2.007; P < .001), mixed (OR, 2.371; P = .001) and undifferentiated histologic types (OR, 2.952; P < .001), T1b stage (OR, 2.041; P < .001), presence of ulceration (OR, 1.758; P = .027), and lymphovascular invasion (OR, 5.722; P < .001) were identified as independent risk factors for LNM in EGC. A nomogram was constructed using appropriate predictors to preoperatively predict the risk of LNM in patients with EGC.

CONCLUSION

This study identified the clinicopathologic factors associated with LNM in patients with EGC and developed a prediction model, thereby facilitating the integration of diverse treatment modalities in managing patients with EGC.

Predicting lymph node metastasis in colorectal cancer patients: development and validation of a column chart model

Lymph node metastasis (LNM) is one of the crucial factors in determining the optimal treatment approach for colorectal cancer. The objective of this study was to establish and validate a column chart for predicting LNM in colon cancer patients. We extracted a total of 83,430 cases of colon cancer from the Surveillance, Epidemiology, and End Results (SEER) database, spanning the years 2010-2017. These cases were divided into a training group and a testing group in a 7:3 ratio. An additional 8545 patients from the years 2018-2019 were used for external validation. Univariate and multivariate logistic regression models were employed in the training set to identify predictive factors. Models were developed using logistic regression, LASSO regression, ridge regression, and elastic net regression algorithms. Model performance was quantified by calculating the area under the ROC curve (AUC) and its corresponding 95% confidence interval. The results demonstrated that tumor location, grade, age, tumor size, T stage, race, and CEA were independent predictors of LNM in CRC patients. The logistic regression model yielded an AUC of 0.708 (0.7038-0.7122), outperforming ridge regression and achieving similar AUC values as LASSO regression and elastic net regression. Based on the logistic regression algorithm, we constructed a column chart for predicting LNM in CRC patients. Further subgroup analysis based on gender, age, and grade indicated that the logistic prediction model exhibited good adaptability across all subgroups. Our column chart displayed excellent predictive capability and serves as a useful tool for clinicians in predicting LNM in colorectal cancer patients.

Gastric submucosal mass lesions

PURPOSE OF REVIEW

Gastric submucosal mass lesions are a collection of tumours that arise in the stomach and are deep to the mucosal layer. Distinct from gastric epithelial carcinomas, these tumours are generally more indolent. They are often found incidentally on upper endoscopy. Most often they present as smooth protuberant masses covered by normal intact gastric mucosa. The majority are asymptomatic. Endoscopic ultrasound (EUS) is frequently employed to further characterize the lesions. EUS can be diagnostic of some lesions, such as lipomas, and can be used to guide fine needle aspiration to diagnose others. Adding to the traditional management approaches of observation and surgical resection, numerous new and emerging endoscopic therapies are now being used to resect these gastric tumours.

RECENT FINDINGS

This review focuses on evolving strategies in the diagnosis and management of submucosal mass lesions. Although surgical intervention was once the lone option for intervention, there are an increasing number of endoscopic therapies. There have also been advancements in neoadjuvant therapies and in distinguishing the malignant potential of submucosal mass lesions.

SUMMARY

Gastric submucosal lesions are common. EUS is frequently indicated in the evaluation and diagnosis. For tumours for which observation is not recommended, novel endoscopic therapies may offer less invasive management options.