A predictive nomogram for lymph node metastasis of incidental gallbladder cancer: a SEER population-based study
Yingnan Yang1, Zhuolong Tu1, Huajie Cai1
1Department of Hepatobiliary Surgery, First Affiliated Hospital of Wenzhou Medical University, Nanbaixiang street, Ouhai district, Wenzhou, Zhejiang Province, China.
Background:
Existing imaging techniques have a low ability to detect lymph node metastasis (LNM) of gallbladder cancer (GBC). Gallbladder removal by laparoscopic cholecystectomy can provide pathological information regarding the tumor itself for incidental gallbladder cancer (IGBC). The purpose of this study was to identify the risk factors associated with LNM of IGBC and to establish a nomogram to improve the ability to predict the risk of LNM for IGBC.
Methods:
A total of 796 patients diagnosed with stage T1/2 GBC between 2004 and 2015 who underwent surgery and lymph node evaluation were enrolled in this study. We randomly divided the dataset into a training set (70%) and a validation set (30%). A logistic regression model was used to construct the nomogram in the training set and then was verified in the validation set. Nomogram performance was quantified with respect to discrimination and calibration.
Results:
The rates of LNM in T1a, T1b and T2 patients were 7, 11.1 and 44.3%, respectively. Tumor diameter, T stage, and tumor differentiation were independent factors affecting LNM. The C-index and AUC of the training set were 0.718 (95% CI, 0.676-0.760) and 0.702 (95% CI, 0.659-0.702), respectively, demonstrating good prediction performance. The calibration curves showed perfect agreement between the nomogram predictions and actual observations. Decision curve analysis showed that the LNM nomogram was clinically useful when the risk was decided at a possibility threshold of 2-63%. The C-index and AUC of the validation set were 0.73 (95% CI: 0.665-0.795) and 0.692 (95% CI: 0.625-0.759), respectively.
Conclusion:
The nomogram established in this study has good prediction ability. For patients with IGBC requiring re-resection, the model can effectively predict the risk of LNM and make up for the inaccuracy of imaging.
