UNASSIGNED: Little attention has been paid in the prognosis of colorectal signet ring cell carcinoma (SRCC). This study aims to explore the predictive capacity of log odds of positive lymph nodes (LODDS), lymph node ratio (LNR), and pN stage in the prognosis of patients with colorectal SRCC.
UNASSIGNED: A retrospective cohort study was designed, and data were extracted from the Surveillance, Epidemiology and End Results (SEER) database. Data on demographic characteristics, clinicopathological features, and treatment were extracted. Outcomes were overall survival (OS) and cancer-specific survival (CSS). Association of LODDS, LNR, and pN stage with OS and CSS were explored using Cox proportional hazard model and Cox competing risk model, respectively, with results showing as hazard ratio and 95% confidence interval (CI). Predictive performance of LODDS, LNR, and pN stage in OS and CSS was assessed by calculating C-index.
UNASSIGNED: A total of 2,198 patients were included in this study. LODDS, LNR, and pN stage were associated with the OS and CSS of colorectal SRCC patients (all P < 0.05). LODDS showed a good performance in the OS (C-index: 0.704, 95% CI: 0.690-0.718), which was superior to LNR (C-index: 0.657, 95% CI: 0.643-0.671) and pN stage (C-index: 0.643, 95% CI: 0.629-0.657). The C-index of LODDS, LNR, and pN stage for CSS was 0.733 (95% CI: 0.719-0.747), 0.713 (95% CI: 0.697-0.729), and 0.667 (95% CI: 0.651-0.683), respectively.
UNASSIGNED: LODDS displayed a better predictive capacity in the OS and CSS than LNR and pN stage, indicating that LODDS may be effective to predict the prognosis of colorectal SRCC in the clinic.

Aim: Increasing studies have demonstrated lymph node ratio (LNR) to be an accurate prognostic indicator in breast cancer and an alternative to pN staging; however, the AJCC-TNM staging system classified apical or infraclavicular/ipsilateral supraclavicular lymph node-positive (APN(+)) patients with a worse prognosis as the pN3 stage. Until now, different reports on LNR in breast cancer have ignored this possibility. Consequently, it is necessary to discuss the role of APN(+) patients in the LNR system to obtain a precise LNR that predicts the prognosis accurately. Materials and Methods: We collected data on 10,120 breast cancer patients, including 3,936 lymph node-positive patients (3,283 APN(-) and 653 APN(+) patients), who visited our hospital from 2007 to 2012. Then we applied X-tile analysis to calculate cut-off values and conduct survival analysis and multivariate analysis to evaluate patients\' prognosis. Results: We confirmed that some APN(+) patients were mis-subgrouped according to previously reported LNR, indicating that APN(+) patients should be excluded in the application of LNR to predict prognosis. Then we applied X-tile analysis to calculate two cut-off values (0.15 and 0.34) for LNR-APN(-) patients and conducted survival analysis and found that LNR-APN(-) staging was superior to pN staging in predicting the prognosis of APN(-) breast cancer patients. Conclusion: From this study, we conclude that excluding APN(+) patients is the most necessary condition for effective implementation of the LNR system. LNR-APN(-) staging could be a more comprehensive approach in predicting prognosis and guiding clinicians to provide accurate and appropriate treatment.

BACKGROUND: pN stage and breast cancer subtypes (BCS) are both well-recognized prognostic indicators. Our previous work has highlighted that patients even with the same pN stage exhibited a significant survival difference in different BCS. Given this achievement, we hypothesized that a statistical interaction might exist between pN stage and BCS. The aim of this retrospective cohort study was to compare the prognostic value of the combined pN stage and BCS (pNnew stage) with either pN stage or BCS alone, and to determine if this combined new stage could serve as an alternative discriminator of outcome.
METHODS: We combined pN stage and BCS to create a new variable named pNnew stage and then divided it into four groups: pN0new, pN1new, pN2new, and pN3new. Survival analysis was performed with the use of the Kaplan-Meier method and the log-rank test was used for univariate analysis. For multivariate analysis, cox proportional hazard models were applied, allowing for the estimation of disease-free survival (DFS). To assess discriminatory accuracy of the models, we compared the area under the receiver-operating characteristic curve (AUROC), the Akaike information criterion (AIC), and the Bayesian information criterion (BIC) values. Then, we used this pNnew stage to generate a TNnewM staging system according to the 7th AJCC staging system.
RESULTS: A statistical interaction between pN stage and BCS was found. In multivariate survival analysis, the pNnew stage has been confirmed as an independent prognostic variable of 5-year DFS. The pNnew stage, with a smaller AIC or BIC value and larger AUROC, was a more powerful predictor of DFS than either pN stage or BCS alone. Results were validated in a separate cohort of patients. The TNnewM stage proposed in our present study was found comparable to the new 8th AJCC edition which includes anatomic T, N, and M plus tumor grade and the status of the biomarkers Her-2, ER, and PR with respect to prognostic value for breast cancer patients.
CONCLUSIONS: The pNnew stage (combined pN stage and BCS) appears to be a more powerful predictor and discriminator for the outcome of breast cancer, as compared to pN stage or BCS alone, and the TNnewM stage may serve as a simple, easy-to-use alternative to the 8th AJCC edition staging manual.

OBJECTIVE: To examine the prognostic value of lymph node ratio (LNR) for patients with node-positive breast cancer with varying numbers of minimum nodes removed (>5, > 10 and > 15 total node count).
METHODS: This study examined the original histopathological reports of 332 node-positive patients treated in the state of New South Wales (NSW), Australia between 1 April 1995 and 30 September 1995. The LNR was defined as the number of positive lymph nodes (LNs) over the total number of LNs removed. The LNR cutoffs were defined as low-risk, 0.01-0.20; intermediate-risk, 0.21- 0.65; and high-risk, LNR >0.65.
RESULTS: The median follow-up was 10.3 years. In multivariate analysis, LNR was an independent predictor of 10-year breast cancer specific survival when > 5 nodes were removed. However, LNR was not an independent predictor when > 15 nodes were removed. In a multivariate analysis the relative risk of death (RR) decreased from 2.20 to 1.05 for intermediate-risk LNR and from 3.07 to 2.64 for high-risk while P values increased from 0.027 to 0.957 for intermediate-risk LNR and 0.018 to 0.322 for high-risk with the number of nodes removed increasing from > 5 to > 15.
CONCLUSIONS: Although LNR is important for patients with low node denominators, for patients with macroscopic nodal metastases in several nodes following an axillary dissection who have more than 15 nodes dissected, the oncologist can be satisfied that prognosis, selection of adjuvant chemotherapy and radiotherapy fields can be based on the numerator of the positive nodes.

OBJECTIVE: To evaluate the association between lymph node ratio (LNR) and cancer-specific survival (CSS) in a population of patients with penile cancer and lymph node metastases (LNM).
METHODS: We evaluated 81 patients with pathologically determined LNM who were surgically treated at our institution between 2000 and 2012. We considered LNR both as a continuously coded and as a categorically coded variable. The minimum-P-value approach was used to determine the most significant LNR threshold. The Kaplan-Meier method was used to determine CSS rates, and univariable and multivariable Cox regression models were fitted to test the predictors of CSS.
RESULTS: The median (interquartile range [IQR]) numbers of positive and removed lymph nodes were 2 (1-4) and 22 (13-30), respectively. The median (IQR) LNR was 10.3 (6.3-16.6)% and the most significant LNR threshold was 22%. The median (IQR) follow-up was 26 (16-62) months. Overall, the 5-year CSS rate was 50.5%. After stratification according to LNR, 5-year CSS rates were 65.2% vs 9.6% in patients with LNR < 22% vs LNR ≥ 22%, respectively (P < 0.001). In multivariable Cox regression models, after adjusting for several established prognostic factors, LNR was as independent predictor of CSS (P≤0.012). Finally, LNR significantly improved the accuracy of multivariable Cox regression models by 4.9-10.5%.
CONCLUSIONS: Although further investigations are needed to evaluate the relationship between tumour burden and treatment intensity, LNR may represent a powerful predictor of CSS in patients with penile cancer and pathologically determined LNM.