RESUMEN
There is no strong evidence indicating the optimal treatment for breast cancer (BC) and no specific prognostic model. The aim of this study was to establish nomograms to predict the overall survival (OS) of BC patients receiving chemoradiotherapy and surgery, thereby quantifying survival benefits and improving patient management. A total of 1877 patients with primary nonmetastatic BC who received chemoradiotherapy and surgery from 2010 to 2019 were identified from the Surveillance, Epidemiology and End Results (SEER) database as the training cohort, 804 as the internal validation cohort, and 796 patients from the First Affiliated Hospital of Zhengzhou University (n=324) and Jiaxing Maternal and Child Health Hospital (n=472) as the external validation cohort. Least absolute shrinkage and selection operator (LASSO), univariate, and multivariate Cox regression analyses were performed in the training cohort to determine independent prognostic factors for BC, and a nomogram was constructed to predict 3-year, 5-year, and 8-year OS. The final model incorporated 7 factors that significantly affect OS: race, location, positive regional nodes, T stage, N stage, subtype, and grade. The calibration curves showed good consistency between the predicted survival and actual outcomes. Time-dependent receiver operating characteristic (ROC) curves and the time-dependent area under the curve (AUC) confirmed that the accuracy and clinical usefulness of the constructed nomograms were favorable. Decision curve analysis (DCA) and net reclassification improvement (NRI) also demonstrated that this nomogram was more suitable for clinical use than the 7th American Joint Committee on Cancer (AJCC) tumor node metastasis (TNM) staging system and the previous prediction model. In the training cohort and the internal validation cohort, the concordance indices (C-index) of the nomogram for predicting OS (0.723 and 0.649, respectively) were greater than those of the 7th AJCC TNM staging system and the previous prediction model. In addition, based on Kaplan-Meier (K-M) survival curves, the survival differences among different risk stratifications were statistically significant, indicating that our risk model was accurate. In this study, we determined independent prognostic factors for OS in patients with primary nonmetastatic BC treated with chemoradiotherapy and surgery. A new and accurate nomogram for predicting 3-, 5-, and 8-year OS in this patient population was developed and validated for potential clinical applicability.
RESUMEN
BACKGROUND: Multiple perioperative inflammatory markers are considered important factors affecting the long-term survival of esophageal cancer (EC) patients. Hematological parameters, whether single or combined, have high predictive value. AIM: To investigate the inflammatory status of patients with preoperative EC using blood inflammatory markers, and to establish and validate competing risk nomogram prediction models for overall survival (OS) and progression-free survival (PFS) in EC patients. METHODS: A total of 508 EC patients who received radical surgery (RS) treatment in The First Affiliated Hospital of Zhengzhou University from August 5, 2013, to May 1, 2019, were enrolled and randomly divided into a training cohort (356 cases) and a validation cohort (152 cases). We performed least absolute shrinkage and selection operator (LASSO)-univariate Cox- multivariate Cox regression analyses to establish nomogram models. The index of concordance (C-index), time-dependent receiver operating characteristic (ROC) curves, time-dependent area under curve (AUC) and calibration curves were used to evaluate the discrimination and calibration of the nomograms, and decision curve analysis (DCA) was used to evaluate the net benefit of the nomograms. The relative integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to evaluate the improvement in predictive accuracy of our new model compared with the AJCC staging system and another traditional model. Finally, the relationship between systemic inflammatory response markers and prognostic survival was explored according to risk plot, time-dependent AUC, Kaplan-Meier and restricted cubic spline (RCS). RESULTS: Based on the multivariate analysis for overall survival (OS) in the training cohort, nomograms with 10 variables, including the aggregate index of systemic inflammation (AISI) and lymphocyte-to-monocyte ratio (LMR), were established. Time-dependent ROC, time-dependent AUC, calibration curves, and DCA showed that the 1-, 3-, and 5 year OS and PFS probabilities predicted by the nomograms were consistent with the actual observations. The C-index, NRI, and IDI of the nomograms showed better performance than the AJCC staging system and another prediction model. Moreover, risk plot, time-dependent AUC, and Kaplan-Meier showed that higher AISI scores and lower LMR were associated with poorer prognosis, and there was a nonlinear relationship between them and survival risk. CONCLUSION: AISI and LMR are easy to obtain, reproducible and minimally invasive prognostic tools that can be used as markers to guide the clinical treatment and prognosis of patients with EC.