Published online Feb 27, 2024. doi: 10.4240/wjgs.v16.i2.357
Peer-review started: November 3, 2023
First decision: December 6, 2023
Revised: December 16, 2023
Accepted: January 19, 2024
Article in press: January 19, 2024
Published online: February 27, 2024
Processing time: 113 Days and 21.9 Hours
Gastric cancer (GC) is a globally prevalent malignancy known for its aggressive behaviour and poor survival outcomes, especially when metastasis occurs. Recent research has focused on identifying more precise prognostic factors to tailor individual treatment strategies. By developing a nomogram using e Surveillance, epidemiology, and end results program (SEER) database, this study addresses a critical gap in understanding GC lung metastasis (GCLM). This approach goes beyond traditional American Joint Committee on Cancer staging, thus offering a more accurate predictive model for overall survival (OS) and risk categorization in GCLM patients. This contribution is significant because it can inform better clinical decision-making and potentially improve outcomes in this patient population.
This study was motivated by the need to improve prognostic predictions for GCLM, which is a condition associated with notably poor survival outcomes. The aim was to address key problems in current prognostic models, such as their limited ability to accurately predict OS and cumulative incidence prediction (CIP) in GCLM patients. The significance of solving these problems lies in providing clinicians with a more effective tool for risk stratification, which can guide personalized treatment plans and potentially improve patient outcomes. By developing a more accurate and comprehensive nomogram using data from the SEER database, this research contributes to the advancement of precision medicine in GC care, particularly for those with lung metastases.
The primary objective of this study was to develop an accurate prognostic nomogram for patients with GCLM by using data from the SEER database. This nomogram aims to predict OS and CIP more effectively than existing models. The study successfully identified significant prognostic factors related to GCLM, thus integrating these factors into a model that offers more precise survival predictions and risk stratification. These objectives are significant for future research, as they will enhance the understanding of GCLM and aid in the advancement of personalized treatment strategies, thus potentially improving patient outcomes in this challenging cancer subtype.
This research utilized a retrospective analysis of data from the SEER database comprising patients with GCLM from January 2000 to December 2020. The methods included univariate and multivariate Cox regression analyses to identify independent prognostic factors, and a nomogram was developed for predicting OS. This nomogram was validated by using the time-dependent area under the curve and calibration curves. Additionally, decision curve analysis was used to assess the clinical usefulness of the model. The novelty of this research lies in the comprehensive approach combining various clinical and demographic variables that have not been previously integrated in traditional models, thereby enhancing the prognostic accuracy for GCLM patients.
This research established a novel prognostic nomogram for predicting OS in patients with GCLM that included factors such as age, sex, race, tumour size, and treatment modality. This model demonstrated superior predictive accuracy compared to traditional staging systems, thereby significantly contributing to personalized treatment planning and risk assessment in GCLM patients. However, challenges remain in validating the nomogram across diverse patient po
The research concluded with the successful development of a prognostic nomogram for predicting the OS of patients with lung metastases from GC based on an extensive and precise collection of clinicopathological variables. Moreover, this approach helps to appropriately classify patients into high-risk and low-risk groups, thereby guiding treatment. This model, which was validated for its reliability and clinical application, represents a significant innovation in personalized treatment and prognosis strategies for GC.
Future efforts will focus on additional external validation and prospective evaluations to further establish the model's efficacy and applicability in clinical settings.