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Objective <p>We aimed to evaluate the efficacy of the log odds of positive lymph nodes (LODDS) in survival prediction of elderly patients with gastric adenocarcinoma (GAC) after gastrectomy, and to construct a relevant survival prediction model.</p> Methods <p>In this study, patient data was collected from both the Surveillance, Epidemiology, and End Results (SEER) database and a medical records database at a hospital in China. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were used to identify independent risk factors for cancer-specific survival (CSS) and a nomogram was constructed based on the results of multivariate Cox regression. Using consistency index (C-index), calibration curve, time-dependent receiver operating characteristic curve (tdROC) and decision curve analysis (DCA) to evaluate the predictive performance of nomogram. Generating Kaplan-Meier survival curves to show the difference in CSS between different groups.</p> Results <p>Multivariate Cox analysis indicated that race, site, T stage, size, and LODDS were independently associated with the CSS. The C-index and AUC of the nomogram both exceed 0.71, while the calibration curve suggests that the nomogram accurately predicts CSS. Additionally, DCA curve results demonstrate superior clinical net benefits of the nomogram over TNM staging. High-risk patients identified by the predictive model exhibit inferior survival outcomes compared to low-risk patients. In addition, group comparison showed that only high-risk patients or high-LODDS group could benefit from chemotherapy and radiotherapy.</p> Conclusions <p>The LODDS is an independent prognostic factor for elderly GAC patients after gastrectomy. The nomogram based on LODDS has better predictive ability than the traditional TNM staging system, assisting clinical doctors in evaluating patient prognosis and guiding treatment.</p>

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Construction and validation of a novel nomogram based on the log odds of positive lymph nodes to predict cancer-specific survival in elderly patients with gastric adenocarcinoma after radical surgery

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月2日

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  • Lei Wang,
  • Jingjing Ge,
  • Yihua Fang,
  • Huiqiong Han,
  • Yanru Qin

摘要

Objective

We aimed to evaluate the efficacy of the log odds of positive lymph nodes (LODDS) in survival prediction of elderly patients with gastric adenocarcinoma (GAC) after gastrectomy, and to construct a relevant survival prediction model.

Methods

In this study, patient data was collected from both the Surveillance, Epidemiology, and End Results (SEER) database and a medical records database at a hospital in China. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were used to identify independent risk factors for cancer-specific survival (CSS) and a nomogram was constructed based on the results of multivariate Cox regression. Using consistency index (C-index), calibration curve, time-dependent receiver operating characteristic curve (tdROC) and decision curve analysis (DCA) to evaluate the predictive performance of nomogram. Generating Kaplan-Meier survival curves to show the difference in CSS between different groups.

Results

Multivariate Cox analysis indicated that race, site, T stage, size, and LODDS were independently associated with the CSS. The C-index and AUC of the nomogram both exceed 0.71, while the calibration curve suggests that the nomogram accurately predicts CSS. Additionally, DCA curve results demonstrate superior clinical net benefits of the nomogram over TNM staging. High-risk patients identified by the predictive model exhibit inferior survival outcomes compared to low-risk patients. In addition, group comparison showed that only high-risk patients or high-LODDS group could benefit from chemotherapy and radiotherapy.

Conclusions

The LODDS is an independent prognostic factor for elderly GAC patients after gastrectomy. The nomogram based on LODDS has better predictive ability than the traditional TNM staging system, assisting clinical doctors in evaluating patient prognosis and guiding treatment.