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BMC Cancer
Published in: BMC Cancer 1/2024

Open Access 01-12-2024 | Gastric Cancer | Research

Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study

Authors: Zhouwei Zhan, Bijuan Chen, Hui Cheng, Shaohua Xu, Chunping Huang, Sijing Zhou, Haiting Chen, Xuanping Lin, Ruyu Lin, Wanting Huang, Xiaohuan Ma, Yu Fu, Zhipeng Chen, Hanchen Zheng, Songchang Shi, Zengqing Guo, Lihui Zhang

Published in: BMC Cancer | Issue 1/2024

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Abstract

Objective

The purpose of this study was to develop an individual survival prediction model based on multiple machine learning (ML) algorithms to predict survival probability for remnant gastric cancer (RGC).

Methods

Clinicopathologic data of 286 patients with RGC undergoing operation (radical resection and palliative resection) from a multi-institution database were enrolled and analyzed retrospectively. These individuals were split into training (80%) and test cohort (20%) by using random allocation. Nine commonly used ML methods were employed to construct survival prediction models. Algorithm performance was estimated by analyzing accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC), confusion matrices, five-fold cross-validation, decision curve analysis (DCA), and calibration curve. The best model was selected through appropriate verification and validation and was suitably explained by the SHapley Additive exPlanations (SHAP) approach.

Results

Compared with the traditional methods, the RGC survival prediction models employing ML exhibited good performance. Except for the decision tree model, all other models performed well, with a mean ROC AUC above 0.7. The DCA findings suggest that the developed models have the potential to enhance clinical decision-making processes, thereby improving patient outcomes. The calibration curve reveals that all models except the decision tree model displayed commendable predictive performance. Through CatBoost-based modeling and SHAP analysis, the five-year survival probability is significantly influenced by several factors: the lymph node ratio (LNR), T stage, tumor size, resection margins, perineural invasion, and distant metastasis.

Conclusions

This study established predictive models for survival probability at five years in RGC patients based on ML algorithms which showed high accuracy and applicative value.
Appendix
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Metadata
Title
Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study
Authors
Zhouwei Zhan
Bijuan Chen
Hui Cheng
Shaohua Xu
Chunping Huang
Sijing Zhou
Haiting Chen
Xuanping Lin
Ruyu Lin
Wanting Huang
Xiaohuan Ma
Yu Fu
Zhipeng Chen
Hanchen Zheng
Songchang Shi
Zengqing Guo
Lihui Zhang
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2024
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-024-12303-9

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