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Comparison of three lymph node staging schemes for predicting the outcome in patients with small bowel adenocarcinoma: A population-based cohort and international multicentre cohort study.

ABSTRACT: BACKGROUND:The prognostic roles of three common lymph node staging schemes, number of positive lymph nodes (pN), lymph node ratio (LNR) and log odds of positive lymph nodes (LODDS) in small bowel adenocarcinoma (SBA) are unclear. We assessed their prognostic ability in SBA. METHODS:A total of 2128 patients diagnosed with SBA between 1988 and 2010 from the Surveillance, Epidemiology, and End Results (SEER) database and 186 patients from 15 hospitals in France and China were identified. We evaluated the prognostic ability of the schemes in both continuous and stratified patterns using R2, Harrell's C, and time-dependent receiver operating characteristic curve analyses. FINDINGS:For continuous pattern, the LODDS had a better capacity of discrimination and higher accuracy of prognosis than pN and LNR. Similarly, the stratified LODDS classification had a better performance of discrimination and higher accuracy of prognosis than the pN and LNR classification. The multivariable model using the LODDS classification also showed superiorly predictive accuracy and discriminatory capacity to those of the 7th and, 8th TNM node and LNR classification. These results were fully validated in an independent international multicentre cohort. INTERPRETATION:The LODDS scheme showed a better prognostic performance than the LNR or pN schemes in patients with SBA regardless of continuous or stratified pattern. The LODDS scheme could serve as an auxiliary to lymph node staging systems in future revisions of the American Joint Committee on Cancer (AJCC) manual. FUND: This work was funded by the Zhejiang Province Natural Science Fund of China.

SUBMITTER: Zhou YY

PROVIDER: S-EPMC6443578 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Comparison of three lymph node staging schemes for predicting the outcome in patients with small bowel adenocarcinoma: A population-based cohort and international multicentre cohort study.

Zhou Yang-Yang YY Du Xiao-Jing XJ Zhang Chi-Hao CH Aparicio Thomas T Zaanan Aziz A Afchain Pauline P Chen Li-Ping LP Hu Sun-Kuan SK Zhang Pei-Chen PC Wu Ming M Zhang Qing-Wei QW Wang Hong H

EBioMedicine 20190226

<h4>Background</h4>The prognostic roles of three common lymph node staging schemes, number of positive lymph nodes (pN), lymph node ratio (LNR) and log odds of positive lymph nodes (LODDS) in small bowel adenocarcinoma (SBA) are unclear. We assessed their prognostic ability in SBA.<h4>Methods</h4>A total of 2128 patients diagnosed with SBA between 1988 and 2010 from the Surveillance, Epidemiology, and End Results (SEER) database and 186 patients from 15 hospitals in France and China were identif ...[more]

PMID: 30824384

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Project description:Objective: To compare the prognostic predictive performance of six lymph node (LN) staging schemes: American Joint Committee on Cancer (AJCC) N stage, number of retrieved lymph nodes (NRLN), number of positive lymph nodes (NPLN), number of negative lymph nodes (NNLN), lymph node ratio (LNR), and log odds of positive lymph nodes (LODDS) among node-positive endometrioid endometrial cancer (EEC) patients. Methods: A total of 3,533 patients diagnosed with node-positive EEC between 2010 and 2016 from the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively analyzed. We applied X-tile software to identify the optimal cutoff value for different staging schemes. Univariate and multivariate Cox regression models were used to assess the relationships between different LN schemes and survival outcomes [disease-specific survival (DSS) and overall survival (OS)]. Moreover, Akaike information criterion (AIC) and Harrell concordance index (C-index) were used to evaluate the predictive performance of each scheme in both continuous and categorical patterns. Results: N stage (N1/N2) was not an independent prognostic factor for node-positive EEC patients based on multivariate analysis (DSS: p = 0.235; OS: p = 0.145). Multivariate model incorporating LNR demonstrated the most superior goodness of fit regardless of continuous or categorical pattern. Regarding discrimination power of the models, LNR outperformed other models in categorical pattern (OS: C-index = 0.735; DSS: C-index = 0.737); however, LODDS obtained the highest C-index in continuous pattern (OS: 0.736; DSS: 0.739). Conclusions: N stage (N1/N2) was unable to differentiate the prognosis for node-positive EEC patients in our study. However, LNR and LODDS schemes seemed to have a better predictive performance for these patients than other number-based LN schemes whether in DSS or OS, which revealed that LNR and LODDS should be more helpful in prognosis assessment for node-positive EEC patients than AJCC N stage.

Project description:Background: The prognostic significance of the lymph node (LN) classification for small bowel neuroendocrine tumors (SBNETs) remains unknown. The aim of the present study was to evaluate and compare the prognostic assessment of different LN staging systems. Methods: Patients with SBNETs were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The X-tile program was used to determine the cutoff value of the resected lymph nodes (RLNs), negative lymph nodes (NLNs), lymph node ratio (LNR), and the log odds of positive lymph nodes (LODDS). Survival analyses were performed using Kaplan-Meier curves with log-rank test. Logistic regression analysis was used to evaluate the differences between different periods. Univariate and multivariate Cox proportional hazards models were used to assess the prognostic value of different LN staging systems on cause-specific survival (CSS). The relative discriminative abilities of the different LN staging systems were assessed using the Akaike information criterion (AIC) and the Harrell consistency index (HCI). Result: A total of 3,680 patients were diagnosed with SBNETs between 1988 and 2014 from the SEER database. A significant difference over time (1988-1999 vs. 2000-2014) was seen in age (P <0.001), tumor differentiation (P <0.001), T stage (P <0.001), and RLN (P <0.001) subgroups. Multivariate Cox survival analysis identified that LN status stratified by the number of RLNs, NLNs, LNR, and LODDS all predicted CSS in patients with SBNETs (all P <0.05), whereas the number of positive lymph nodes (PLNs) failed (P = 0.452). When assessed using categorical variables, LODDS staging systems showed the best prognostic performance (HCI: 0.766, AIC: 7,575.154) in the whole population. Further analysis based on different RLNs after eliminating the missing data showed that when the RLNs are <12, the LODDS (HCI: 0.769, AIC: 1,088.731) maintained the best prognostic performance as well when the RLNs are ?12 (HCI: 0.835, AIC: 825.692). Among patients with LNR scores of 0 or 1, there was a residual heterogeneity of outcomes that were better stratified and characterized by the LODDS. Conclusion: LODDS was a better predicator of survival when LN status was stratified as a categorical variable and should be considered when assessing the prognosis of patients with SBNETs to allow a more reliable means to stratify patient survival.

Project description:BackgroundIdentifying lymph node metastasis areas during surgery for early invasive lung adenocarcinoma remains challenging. The aim of this study was to develop a nomogram mathematical model before the end of surgery for predicting lymph node metastasis in patients with early invasive lung adenocarcinoma.MethodsIn this study, we included patients with invasive lung adenocarcinoma measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to January 2022. Preoperative biomarker results, clinical features, and computed tomography characteristics were collected. The enrolled patients were randomized into a training cohort and a validation cohort in a 7:3 ratio. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. Recipient operating characteristic (ROC) curves were used to assess the discrimination ability of the model. Calibration capability was assessed using the Hosmer-Lemeshow test and calibration curves. The clinical utility of the nomogram was assessed using decision curve analysis (DCA).ResultsThe overall incidence of lymph node metastasis was 13.23% (61/461). Six indicators were finally determined to be independently associated with lymph node metastasis. These six indicators were: age (P < 0.001), serum amyloid (SA) (P = 0.008); carcinoma antigen 125 (CA125) (P = 0. 042); mucus composition (P = 0.003); novel aspartic proteinase of the pepsin family A (Napsin A) (P = 0.007); and cytokeratin 5/6 (CK5/6) (P = 0.042). The area under the ROC curve (AUC) was 0.843 (95% CI: 0.779-0.908) in the training cohort and 0.838 (95% CI: 0.748-0.927) in the validation cohort. the P-value of the Hosmer-Lemeshow test was 0.0613 in the training cohort and 0.8628 in the validation cohort. the bias of the training cohort corrected C-index was 0.8444 and the bias-corrected C-index for the validation cohort was 0.8375. demonstrating that the prediction model has good discriminative power and good calibration.ConclusionsThe column line graphs created showed excellent discrimination and calibration to predict lymph node status in patients with ≤ 2 cm invasive lung adenocarcinoma. In addition, the predictive model has predictive potential before the end of surgery and can inform clinical decision making.

Dataset Information

Comparison of three lymph node staging schemes for predicting the outcome in patients with small bowel adenocarcinoma: A population-based cohort and international multicentre cohort study.

Publications

Comparison of three lymph node staging schemes for predicting the outcome in patients with small bowel adenocarcinoma: A population-based cohort and international multicentre cohort study.

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