Using nomogram, decision tree, and deep learning models to predict lymph node metastasis in patients with early gastric cancer: a multi-cohort study

Lulu Zhao; Weili Han; Penghui Niu; Yuanyuan Lu; Fan Zhang; Fuzhi Jiao; Xiadong Zhou; Wanqing Wang; Xiaoyi Luan; Mingyan He; Quanlin Guan; Yumin Li; Yongzhan Nie; Kaichun Wu; Dongbing Zhao; Yingtai Chen

Using nomogram, decision tree, and deep learning models to predict lymph node metastasis in patients with early gastric cancer: a multi-cohort study

Am J Cancer Res. 2023 Jan 15;13(1):204-215. eCollection 2023.

Authors

Lulu Zhao¹, Weili Han², Penghui Niu¹, Yuanyuan Lu², Fan Zhang³, Fuzhi Jiao⁴, Xiadong Zhou⁵, Wanqing Wang¹, Xiaoyi Luan¹, Mingyan He⁵, Quanlin Guan⁴, Yumin Li³, Yongzhan Nie², Kaichun Wu², Dongbing Zhao¹, Yingtai Chen¹

Affiliations

¹ National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College Beijing, China.
² State Key Laboratory of Cancer Biology and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University Xi'an, Shaanxi, China.
³ Lanzhou University Second Hospital Lanzhou, Gansu, China.
⁴ The First Hospital of Lanzhou University Lanzhou, Gansu, China.
⁵ Gansu Provincial Cancer Hospital Lanzhou, Gansu, China.

PMID: 36777507
PMCID: PMC9906085

Abstract

The accurate assessment of lymph node metastasis (LNM) in patients with early gastric cancer is critical to the selection of the most appropriate surgical treatment. This study aims to develop an optimal LNM prediction model using different methods, including nomogram, Decision Tree, Naive Bayes, and deep learning methods. In this study, we included two independent datasets: the gastrectomy set (n=3158) and the endoscopic submucosal dissection (ESD) set (n=323). The nomogram, Decision Tree, Naive Bayes, and fully convolutional neural networks (FCNN) models were established based on logistic regression analysis of the development set. The predictive power of the LNM prediction models was revealed by time-dependent receiver operating characteristic (ROC) curves and calibration plots. We then used the ESD set as an external cohort to evaluate the models' performance. In the gastrectomy set, multivariate analysis showed that gender (P=0.008), year when diagnosed (2006-2010 year, P=0.265; 2011-2015 year, P=0.001; and 2016-2020 year, P<0.001, respectively), tumor size (2-4 cm, P=0.001; and ≥4 cm, P<0.001, respectively), tumor grade (poorly-moderately, P=0.016; moderately, P<0.001; well-moderately, P<0.001; and well, P<0.001, respectively), vascular invasion (P<0.001), and pT stage (P<0.001) were independent risk factors for LNM in early gastric cancer. The area under the curve (AUC) for the validation set using the nomogram, Decision Tree, Naive Bayes, and FCNN models were 0.78, 0.76, 0.77, and 0.79, respectively. In conclusion, our multi-cohort study systematically investigated different LNM prediction methods for patients with early gastric cancer. These models were validated and shown to be reliable with AUC>0.76 for all. Specifically, the FCNN model showed the most accurate prediction of LNM risks in early gastric cancer patients with AUC=0.79. Based on the FCNN model, patients with LNM rates of >4.77% are strong candidates for gastrectomy rather than ESD surgery.

Keywords: early gastric cancer; lymph node metastasis.