Survival Outcome of Gastric Signet Ring Cell Carcinoma Based on the Optimal Number of Examined Lymph Nodes: A Nomogram- and Machine-Learning-Based Approach.
Yongkang LaiJunfeng XieXiaojing YinWeiguo LaiJianhua TangYi-Qi DuZhaoshen LiPublished in: Journal of clinical medicine (2023)
The optimal number of examined lymph nodes (ELNs) for gastric signet ring cell carcinoma recommended by National Comprehensive Cancer Network guidelines remains unclear. This study aimed to determine the optimal number of ELNs and investigate its prognostic significance. In this study, we included 1723 patients diagnosed with gastric signet ring cell carcinoma in the Surveillance, Epidemiology, and End Results database. X-tile software was used to calculate the cutoff value of ELNs, and the optimal number of ELNs was found to be 32 for adequate nodal staging. In addition, we performed propensity score matching (PSM) analysis to compare the 1-, 3-, and 5-year survival rates; 1-, 3-, and 5-year survival rates for total examined lymph nodes (ELNs < 32 vs. ELNs ≥ 32) were 71.7% vs. 80.1% ( p = 0.008), 41.8% vs. 51.2% ( p = 0.009), and 27% vs. 30.2% ( p = 0.032), respectively. Furthermore, a predictive model based on 32 ELNs was developed and displayed as a nomogram. The model showed good predictive ability performance, and machine learning validated the importance of the optimal number of ELNs in predicting prognosis.
Keyphrases
- lymph node
- machine learning
- neoadjuvant chemotherapy
- end stage renal disease
- free survival
- public health
- ejection fraction
- newly diagnosed
- chronic kidney disease
- big data
- squamous cell carcinoma
- early stage
- prognostic factors
- patient reported outcomes
- risk factors
- quality improvement
- radiation therapy
- deep learning
- squamous cell
- locally advanced
- childhood cancer
- drug induced