Comparison of LNR- and LODDS-based predictive models for prognosis in non-elderly patients with locally advanced
Wei Tao1, Yuxi Cheng2, Peng Wang1
1Department of General Surgery, Xinqiao Hospital, Army Medical University, No. 183 Xinqiao Road, Chongqing, 400037, China.
Purpose:
The purpose of this study was to comprehensively assess the efficacy of lymph node ratio (LNR) and log odds of positive lymph nodes (LODDS) in predicting survival outcomes in non-elderly locally advanced rectal cancer (LARC) patients treated with neoadjuvant chemoradiotherapy (NCRT).
Methods:
The 1643 non-elderly LARC patients undergoing NCRT between 2010 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and were randomly assigned at a ratio of 7:3. We used Cox regression models to identify independent prognostic factors, then constructed nomogram models to predict cancer-specific survival (CSS) and overall survival (OS). The relative weight in nomogram models, receiver operating characteristic (ROC), area under the curve (AUC), concordance index (C-index), calibration curve, and decision curve analysis (DCA) were performed to evaluate and compare the predictive performance between LNR and LODDS.
Results:
The distribution of LNR and LODDS showed that LODDS exhibited a more detailed stratification when LNR was equal to the extreme value. In predicting CSS, the LNR (hazard ratio (HR), 1.987; 95% confidence interval (CI), 1.375-2.872) and LODDS (HR, 1.568; 95% CI, 1.154-2.131) were independent risk factors in corresponding nomogram models. Regarding OS, the LODDS (HR, 1.387; 95% CI, 1.060-1.816) showed an independent predictive value. All evaluation methods confirmed the reliability of both models. Although the LNR-based model showed better performance for short-term CSS and the LODDS-based model demonstrated slightly better prediction for long-term CSS and OS, these two models showed largely comparable predictive ability.
Conclusions:
The LNR and LODDS can provide complementary prognostic value in survival prediction, offering clinicians a comprehensive basis for clinical decision-making.
