Log odds of positive lymph nodes as a novel prognostic predictor for gastric cancer: a systematic review and meta-analysis.

BACKGROUND: We conducted a systematic review and meta-analysis to summarize the predictive and prognostic ability of the log odds of positive lymph nodes (LODDS) staging system and compare it with pathological N (pN) classification and the ratio-based lymph node system (rN) for the overall survival (OS) of gastric cancer (GC). METHODS: Through a systematic review till March 7, 2022, we identified population-based studies that reported the prognostic effects of LODDS in patients with GC. We compare the predictive effectiveness of the LODDS staging system with that of the rN and pN classification systems for the OS of GC. RESULTS: Twelve studies comprising 20,312 patients were included in this systematic review and meta-analysis. The results showed that LODDS1, LODDS2, LODDS3, and LODDS4 in GC patients were correlated with poor OS compared with LODDS0 (LODDS1 vs. LODDS0: HR = 1.62, 95% CI (1.42, 1.85); LODDS2 vs. LODDS0: HR = 2.47, 95% CI (2.02, 3.03); LODDS3 vs. LODDS0: HR = 3.15, 95% CI (2.50, 3.97); LODDS4 vs. LODDS0: HR = 4.55, 95% CI (3.29, 6.29)). Additionally, significant differences in survival were observed among patients with different LODDS classifications (all P-values were < 0.001) with the same rN and pN classifications. Meanwhile, for patients with different pN or rN classifications with the same LODDS classification, prognosis was highly similar. CONCLUSION: The findings show that LODDS is correlated with the prognosis of GC patients and is superior to the pN and rN classifications for prognostic assessment.

Development and Validation of a Prognostic Model to Predict the Prognosis of Patients With Retroperitoneal Liposarcoma: A Large International Population-Based Cohort Study.

Background: Retroperitoneal liposarcomas (RPLs), sarcoma of mesenchymal origin, are the most common soft tissue sarcomas (STS) of the retroperitoneum. Given the rarity of RPLs, the prognostic values of clinicopathological features in the patients remain unclear. The nomogram can provide a visual interface to aid in calculating the predicted probability that a patient will achieve a particular clinical endpoint and communication with patients. Methods: We included a total of 1,392 RPLs patients diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. For nomogram construction and validation, patients in the SEER database were divided randomly into the training cohort and internal validation cohort at a ratio of 7:3, while 65 patients with RPLs from our center between 2010 and 2016 served as the external validation cohort. The OS curves were drawn using the Kaplan-Meier method and assessed using the log-rank test. Moreover, Fine and Gray's competing-risk regression models were conducted to assess CSS. Univariate and multivariate analyses were performed to select the prognostic factors for survival time. We constructed a predictive nomogram based on the results of the multivariate analyses. Results: Through univariate and multivariate analyses, it is found that age, histological grade, classification, SEER stage, surgery constitute significant risk factors for OS, and age, classification, SEER stage, AJCC M stage, surgery, and tumor size constitute risk factors for CSS. We found that the nomogram provided a good assessment of OS and CSS at 1, 3, and 5 years in patients with RPLs (1-year OS: (training cohort: AUC = 0.755 (95% CI, 0.714, 0.796); internal validation cohort: AUC = 0.754 (95% CI, 0.681, 0.827); external validation cohort: AUC = 0.793 (95% CI, 0.651, 0.935)); 3-year OS: (training cohort: AUC = 0.782 (95% CI, 0.752, 0.811); internal validation cohort: AUC = 0.788 (95% CI, 0.736, 0.841); external validation cohort: AUC = 0.863 (95% CI, 0.773, 0.954)); 5-year OS: (training cohort: AUC = 0.780 (95% CI, 0.752, 0.808); internal validation cohort: AUC = 0.783 (95% CI, 0.732, 0.834); external validation cohort: AUC = 0.854 (95% CI, 0.762, 0.945)); 1-year CSS: (training cohort: AUC = 0.769 (95% CI, 0.717, 0.821); internal validation cohort: AUC = 0.753 (95% CI, 0.668, 0.838); external validation cohort: AUC = 0.799 (95% CI, 0.616, 0.981)); 3-year CSS: (training cohort: AUC = 0.777 (95% CI, 0.742, 0.811); internal validation cohort: AUC = 0.787 (95% CI, 0.726, 0.849); external validation cohort: AUC = 0.808 (95% CI, 0.673, 0.943)); 5-year CSS: (training cohort: AUC = 0.773 (95% CI, 0.741, 0.805); internal validation cohort: AUC = 0.768 (95% CI, 0.709, 0.827); external validation cohort: AUC = 0.829 (95% CI, 0.712, 0.945))). The calibration plots for the training, internal validation, and external validation cohorts at 1-, 3-, and 5-year OS and CSS indicated that the predicted survival rates closely correspond to the actual survival rates. Conclusion: We constructed and externally validated an unprecedented nomogram prognostic model for patients with RPLs. The nomogram can be used as a potential, objective, and supplementary tool for clinicians to predict the prognosis of RPLs patients around the world.