Development of novel prediction models for nodal and distant metastasis in G1 and G2 colorectal neuroendocrine tumors.

Purpose: We aimed to construct novel nomograms for nodal and distant metastasis for patients with G1 and G2 colorectal neuroendocrine tumors (NETs).
Methods: A training set and validation set were obtained by including G1 and G2 colorectal NET patients from the Surveillance, Epidemiology, and End Results database and the Cancer Hospital Chinese Academy of Medical Science, respectively. The area under curve (AUC) values under receiver operating characteristic (ROC) curves, calibration plots, and the Hosmer‒Lemeshow tests were used to evaluate the discriminability and calibration of nomograms.
Results: In total, 3690 and 172 patients were included in the training set and validation set, respectively. Tumor size, location, and T stage were included in the nomogram predicting nodal metastasis. The AUC values of the nomogram were 0.972 (95% confidence interval (CI): 0.964-0.980) and 0.897 (95% CI: 0.846-0.948) in the training set and validation set, respectively. The calibration plots and Hosmer‒Lemeshow test for the training set (P = 0.999) and validation set (P = 0.537) showed good model calibration. Tumor size, T stage, and N stage were incorporated into the nomogram predicting distant metastasis. The ROC curves demonstrated desirable discrimination both in the training set (AUC = 0.938 (95% CI: 0.921-0.954)) and validation set (AUC = 0.938 (95% CI: 0.890-0.988)). The calibration curves and Hosmer‒Lemeshow test indicated acceptable model calibration both in the training set (P = 0.908) and validation set (P = 0.722).
Conclusion: The proposed nomograms may be used as a reliable tool to predict the nodal and distant metastasis in G1 and G2 colorectal NETs.
(© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)