AUTHOR=Yang Tao , Martinez-Useros Javier , Liu JingWen , Alarcón Isaias , Li Chao , Li WeiYao , Xiao Yuanxun , Ji Xiang , Zhao YanDong , Wang Lei , Morales-Conde Salvador , Yang Zuli TITLE=A retrospective analysis based on multiple machine learning models to predict lymph node metastasis in early gastric cancer JOURNAL=Frontiers in Oncology VOLUME=12 YEAR=2022 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.1023110 DOI=10.3389/fonc.2022.1023110 ISSN=2234-943X ABSTRACT=Background

Endoscopic submucosal dissection has become the primary option of treatment for early gastric cancer. However, lymph node metastasis may lead to poor prognosis. We analyzed factors related to lymph node metastasis in EGC patients, and we developed a construction prediction model with machine learning using data from a retrospective series.

Methods

Two independent cohorts’ series were evaluated including 305 patients with EGC from China as cohort I and 35 patients from Spain as cohort II. Five classifiers obtained from machine learning were selected to establish a robust prediction model for lymph node metastasis in EGC.

Results

The clinical variables such as invasion depth, histologic type, ulceration, tumor location, tumor size, Lauren classification, and age were selected to establish the five prediction models: linear support vector classifier (Linear SVC), logistic regression model, extreme gradient boosting model (XGBoost), light gradient boosting machine model (LightGBM), and Gaussian process classification model. Interestingly, all prediction models of cohort I showed accuracy between 70 and 81%. Furthermore, the prediction models of the cohort II exhibited accuracy between 48 and 82%. The areas under curve (AUC) of the five models between cohort I and cohort II were between 0.736 and 0.830.

Conclusions

Our results support that the machine learning method could be used to predict lymph node metastasis in early gastric cancer and perhaps provide another evaluation method to choose the suited treatment for patients.