Patterns of Lymph Node Metastasis in Patients With T1/T2 Gastroduodenal Neuroendocrine Neoplasms: Implications for Endoscopic Treatment

Risk factors, prognostic factors, and nomograms for distant metastasis in patients with diagnosed duodenal cancer: A population-based study

BACKGROUND

Duodenal cancer is one of the most common subtypes of small intestinal cancer, and distant metastasis (DM) in this type of cancer still leads to poor prognosis. Although nomograms have recently been used in tumor areas, no studies have focused on the diagnostic and prognostic evaluation of DM in patients with primary duodenal cancer.

AIM

To develop and evaluate nomograms for predicting the risk of DM and personalized prognosis in patients with duodenal cancer.

METHODS

Data on duodenal cancer patients diagnosed between 2010 and 2019 were extracted from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for DM in patients with duodenal cancer, and univariate and multivariate Cox proportional hazards regression analyses were used to determine independent prognostic factors in duodenal cancer patients with DM. Two novel nomograms were established, and the results were evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

A total of 2603 patients with duodenal cancer were included, of whom 457 cases (17.56%) had DM at the time of diagnosis. Logistic analysis revealed independent risk factors for DM in duodenal cancer patients, including gender, grade, tumor size, T stage, and N stage (P < 0.05). Univariate and multivariate COX analyses further identified independent prognostic factors for duodenal cancer patients with DM, including age, histological type, T stage, tumor grade, tumor size, bone metastasis, chemotherapy, and surgery (P < 0.05). The accuracy of the nomograms was validated in the training set, validation set, and expanded testing set using ROC curves, calibration curves, and DCA curves. The results of Kaplan-Meier survival curves (P < 0.001) indicated that both nomograms accurately predicted the occurrence and prognosis of DM in patients with duodenal cancer.

CONCLUSION

The two nomograms are expected as effective tools for predicting DM risk in duodenal cancer patients and offering personalized prognosis predictions for those with DM, potentially enhancing clinical decision-making.

Application of machine learning algorithms to predict lymph node metastasis in gastric neuroendocrine neoplasms

Background

Neuroendocrine neoplasms (NENs) are tumors that originate from secretory cells of the diffuse endocrine system and typically produce bioactive amines or peptide hormones. This paper describes the development and validation of a predictive model of the risk of lymph node metastasis among gastric NEN patients based on machine learning platform.

Methods

In this investigation, data from 1256 patients were used, of whom 119 patients from the First Affiliated Hospital of Soochow University in China and 1137 cases from the surveillance epidemiology and end results (SEER) database were combined. Six machine learning algorithms, including the logistic regression model (LR), random forest (RF), decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), were used to build the predictive model. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Results

Among the 1256 patients with gastric NENs, 276 patients (21.97 %) developed lymph node metastasis. T stage, tumor size, degree of differentiation, and sex were predictive factors of lymph node metastasis. The RF model achieved the best predictive performance among the six machine learning models, with an AUC, accuracy, sensitivity, and specificity of 0.81, 0.78, 0.76, and 0.82, respectively.

Conclusion

The RF model provided the best prediction and can help physicians determine the lymph node metastasis risk of gastric NEN patients to formulate individualized medical strategies.

Patterns of Lymph Node Metastasis and Optimal Surgical Strategy in Small (≤20 mm) Gastroenteropancreatic Neuroendocrine Tumors

BACKGROUND

Regional lymph node metastasis (LNM) is crucial for planning additional lymphadenectomy, and is directly correlated with poor prognosis in gastroenteropancreatic neuroendocrine tumors (GEP-NETs). However, the patterns of LNM for small (≤20 mm) GEP-NETs remain unclear. This population-based study aimed at evaluating LNM patterns and identifying optimal surgical strategies from the standpoint of lymph node dissemination.

METHODS

This retrospective cohort study retrieved data from the Surveillance, Epidemiology, and End Results (SEER) 18 registries database for 17,308 patients diagnosed as having localized well-differentiated GEP-NETs ≤ 20 mm between January 1, 2004, and December 31, 2017. The patterns of LNM were characterized in 6,622 patients who underwent extended resection for adequate lymph node harvest.

RESULTS

Of 6,622 patients with localized small GEP-NETs in the current study, 2,380 (36%) presented with LNM after regional lymphadenectomy. Nodal involvement was observed in approximately 7.4%, 49.1%, 13.6%, 53.7%, 13.8%, 7.8%, and 15.4% of gastric (g-), small intestinal (si-), appendiceal (a-), colonic (c-), rectal (r-), non-functional pancreatic (nfp-), and functional pancreatic (fp-) NETs ≤ 20 mm. Patients with younger age, larger tumor size, and muscularis invasion were more likely to present with LNM. Additional lymphadenectomy conferred a significant survival advantage in NETs (≤10 mm: HR, 0.47; 95% CI, 0.33-0.66; p < 0.001; 11-20 mm: HR, 0.54; 95% CI, 0.34-0.85; p = 0.008) and fp-NETs ≤ 20 mm (HR, 0.08; 95% CI, 0.02-0.36; p = 0.001), as well as g-NETs (HR, 0.39; 95% CI, 0.16-0.96; p = 0.041) and c-NETs of 11-20 mm (HR, 0.07; 95% CI, 0.01-0.48; p = 0.007). Survival benefits of additional lymphadenectomy were not found in a-NETs, r-NETs, and nfp-NETs with a small size.

CONCLUSIONS

Given the increased risk for nodal metastasis, primary tumor resection with regional lymphadenectomy is a potential optimal surgical strategy for si-NETs and fp-NETs ≤ 20 mm, as well as g-NETs and c-NETs of 11-20 mm. Local resection is an appropriate and reliable surgical approach for a-NETs, r-NETs, and nfp-NETs ≤ 20 mm.