scholarly journals Incorporating the Number of PLN into the AJCC Stage Could Better Predict the Survival for Patients with NSCLC: A Large Population-Based Study

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xiaoling Shang ◽  
Zhenxiang Li ◽  
Jiamao Lin ◽  
Haining Yu ◽  
Chenglong Zhao ◽  
...  
Keyword(s):  
Cox Regression ◽  
Large Population ◽  
Staging System ◽  
Tnm Staging ◽  
Significant Prognostic Factor ◽  
Survival Curves ◽  
Cutoff Values ◽  
Tnm Staging System ◽  
Nsclc Patients ◽  
Tile Model

Purpose. This study aimed to investigate the application of the number of positive lymph nodes (PLNs) in tumor, node, metastasis (TNM) staging system of non-small cell lung cancer (NSCLC) patients. Patients and Methods. We screened a total of 15820 patients with resected NSCLC between 2004 and 2015 from SEER database. The X-tile model was used to determine the cutoff values of the number of PLNs. Overall survival (OS) curves were plotted using the Kaplan–Meier method, and the differences among the individual groups were defined using the log-rank test. Cox regression model was used to perform univariate and multivariate analyses and to assess the association between the number of PLNs and OS. Results. In this study, using the X-tile model, we screened three different cutoff values, including nN0, nN1–3, and nN4-. Survival curves demonstrated that our defined nN stage had a significant predictive value for OS ( P < 0.001 ). In the univariate and multivariate Cox analyses, the result showed that nN stage was a significant prognostic factor of OS for NSCLC patients ( P < 0.001 ). Subsequently, we classified the patients into five subgroups based on the combination of pN and nN stages, including pN0 + nN0, pN1 + nN1-3, pN2 + nN1-3, pN1 + nN4-, and pN2 + nN4-. Moreover, survival curves revealed significant differences among these five groups ( P < 0.001 ). Conclusion. A combination of pathological LNs (pN) and the number of LN (nN) involvement in NSCLC patients had a better prognostic value than the current TNM staging system based on only pN stage.

Multiple immune features based signature for predicting recurrence and survival of inoperable la-NSCLC patients.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e21066-e21066
Author(s):  
Meiying Guo ◽  
Wanlong Li ◽  
Bingjie Fan ◽  
Bing Zou ◽  
Xindong Sun ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Immune Status ◽  
Cox Regression ◽  
Locally Advanced ◽  
Staging System ◽  
Tnm Staging ◽  
Immune Signature ◽  
Complementary Role ◽  
Tnm Staging System ◽  
Nsclc Patients

e21066 Background: The immune status of tumor microenvironment is extremely complex. One single immune feature cannot reflect the integral immune status and its prognostic value was limited. We postulated that the immune signature based on multiple immuno-features could markedly improve the prediction of post-chemoradiotherapeutic survival in inoperable locally advanced non-small-cell lung cancer (LA-NSCLC) patients. Methods: In this study, 100 patients who were diagnosed as inoperable LA-NSCLC between January 2005 and January 2016 were analyzed. A 5-immune feature-based signature was then constructed using the nested repeat 10-fold cross validation with LASSO Cox regression model. Nomograms were then established for predicting prognosis. Results: Immune signature combining 5 immuno-features were significantly associated with OS and PFS (P = 0.002 and P = 0.014, respectively) in patients with inoperable LA-NSCLC, and at a cutoff of -0.198 stratified patients into two groups with 5-year OS rates of 39.8% and 8.8%, and 2-year PFS rates of 22.2% and 5.5% for the high- and low-immune signature groups, respectively. Using immune signature, we proposed immune signature nomograms, which were better than the traditional TNM staging system in terms of discriminating ability (OS: 0.692 vs. 0.588; PFS: 0.672 vs. 0.586, respectively) or net weight classification (OS: 32.96%; PFS: 9.22%), suggesting that immune signature plays a complementary role in the prognosis prediction of patients with inoperable LA-NSCLC. Conclusions: Multiple immune features based immune signature could effectively predict recurrence and survival of inoperable LA-NSCLC patients, and complemented the prognostic value of the TNM staging system.

scholarly journals A nomogram based on Aspartate Aminotransferase/Alanine Aminotransferase (AST/ALT) ratio after surgery to predict the prognostic value in patients with gastric cancer

2020 ◽  
Author(s):  
Linfang Li ◽  
Shan Xing ◽  
Ning Xue ◽  
Miantao Wu ◽  
Yaqing Liang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Aspartate Aminotransferase ◽  
Alanine Aminotransferase ◽  
Cox Regression ◽  
Staging System ◽  
Tnm Staging ◽  
Calibration Plot ◽  
Study Cohort ◽  
Tnm Staging System ◽  
Resectable Gastric Cancer

Abstract Background This study aimed to develop an effective nomogram for predicting overall survival (OS) in surgically treated gastric cancer. Methods We retrospectively evaluated 190 gastric cancer in this study. Cox regression analyses were performed to identify significant prognostic factors for OS in patients with resectable gastric cancer. The predictive accuracy of nomogram was assessed by calibration plot, concordance index (C-index) and decision curve, and then were compared with the traditional TNM staging system. Based on the total points (TPS) by nomogram, we further divided patients into different risk groups. Results On multivariate analysis of the 190 cohort, independent factors for survival were age, clinical stage and Aspartate Aminotransferase/Alanine Aminotransferase (SLR), which were entered into the nomogram. The calibration curve for the probability of OS showed that the nomogram-based predictions were in good agreement with actual observations. And the C-index of the established nomogram for predicting OS had a superior discrimination power compared with the TNM staging system [0.768 (95% CI: 0.725-0.810) vs 0.730 (95% CI: 0.688-0.772), p < 0.05]. Decision curve also demonstrated that the nomogram was better than TNM staging system. Based on the TPS of the nomogram, we further subdivided the study cohort into 3 groups: low risk (TPS ≤ 158), middle risk (158 < TPS ≤ 188), high risk (TPS > 188), the differences of OS rate were significant in the groups. Conclusions The established nomogram resulted in more accurate prognostic prediction for individual patient with resectable gastric cancer.

scholarly journals Development and validation of a prognostic nomogram for predicting overall survival in patients with primary bladder sarcoma: a SEER-based retrospective study

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shijie Li ◽  
Xuefeng Liu ◽  
Xiaonan Chen
Keyword(s):  
Overall Survival ◽  
Risk Stratification ◽  
Cox Regression ◽  
Staging System ◽  
Tnm Staging ◽  
Calibration Curves ◽  
Tnm Staging System ◽  
Primary Bladder ◽  
Stratification System ◽  
Risk Stratification System

Abstract Background Primary bladder sarcoma (PBS) is a rare malignant tumor of the bladder with a poor prognosis, and its disease course is inadequately understood. Therefore, our study aimed to establish a prognostic model to determine individualized prognosis of patients with PBS. Patients and Methods Data of 866 patients with PBS, registered from 1973 to 2015, were extracted from the surveillance, epidemiology, and end result (SEER) database. The patients included were randomly split into a training (n = 608) and a validation set (n = 258). Univariate and multivariate Cox regression analyses were employed to identify the important independent prognostic factors. A nomogram was then established to predict overall survival (OS). Using calibration curves, receiver operating characteristic curves, concordance index (C-index), decision curve analysis (DCA), net reclassification improvement (NRI) and integrated discrimination improvement (IDI), the performance of the nomogram was internally validated. We compared the nomogram with the TNM staging system. The application of the risk stratification system was tested using Kaplan–Meier survival analysis. Results Age at diagnosis, T-stage, N-stage, M-stage, and tumor size were identified as independent predictors of OS. C-index of the training cohort were 0.675, 0.670, 0.671 for 1-, 3- and 5-year OS, respectively. And that in the validation cohort were 0.701, 0.684, 0.679, respectively. Calibration curves also showed great prediction accuracy. In comparison with TNM staging system, improved net benefits in DCA, evaluated NRI and IDI were obtained. The risk stratification system can significantly distinguish the patients with different survival risk. Conclusion A prognostic nomogram was developed and validated in the present study to predict the prognosis of the PBS patients. It may assist clinicians in evaluating the risk factors of patients and formulating an optimal individualized treatment strategy.

scholarly journals Identification of a four long non-coding RNA (lncRNA) Signature for Predicting Prognosis of Patients with Non-Small Cell Lung Cancer: a Multicenter Study in China

2020 ◽  
Author(s):  
Rui-Qi Wang ◽  
Xiao-Ran Long ◽  
Chun-Lei Ge ◽  
Mei-Yin Zhang ◽  
Long Huang ◽  
...  
Keyword(s):  
Staging System ◽  
Tnm Staging ◽  
Small Cell ◽  
Small Cell Lung ◽  
Discovery Cohort ◽  
Non Coding Rna ◽  
Tnm Staging System ◽  
Nsclc Patients ◽  
Long Non Coding Rna ◽  
Independent Cohort

Abstract Background:This study aims to identify a long non-coding RNA (lncRNA) signature for predicting survival in non-small-cell lung carcinoma (NSCLC) patients and providing additional prognostic information to the tumor node metastasis (TNM) staging system. Methods: NSCLC cases from a hospital were divided into a discovery cohort (n=194) and validation cohort (n=172) and analyzed using a custom lncRNA microarray. Another 73 cases obtained from another hospital were assayed using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). The differentially expressed lncRNAs were detected by significance analysis of microarrays (SAM) program and used for identifying those associated with survival in the discovery cohort, which were then employed to construct a prognostic lncRNA signature using a risk-score method. The signature was then confirmed in the validation and independent cohort as well. Results: The discovery cohort was found to comprise of 305 lncRNAs, which showed differential expression between the NSCLC and the corresponding normal lung tissues, a 4-lncRNA signature was identified that was found to significantly correlate with the survival of the NSCLC patients. This signature was further validated in the validation and independent cohort. Moreover, multivariate Cox analysis demonstrates that the 4-lncRNA signature is independent of the TNM staging system.as a risk-score model. The receiver operating characteristic (ROC) curve indicates that the prognostic value of the combined model is significantly higher than that of TNM staging alone in all the cohorts. Conclusions:This study identified a 4-lncRNA signature, which is a powerful prognostic biomarker which related to patient survival in addition to the traditional TNM staging system.

scholarly journals Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Jianguo Lai ◽  
Bo Chen ◽  
Guochun Zhang ◽  
Xuerui Li ◽  
Hsiaopei Mok ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Stratification ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Treatment Strategies ◽  
Staging System ◽  
Tnm Staging ◽  
The Cancer Genome Atlas ◽  
Tnm Staging System

Abstract Background Accumulating evidence has demonstrated that immune-related lncRNAs (IRLs) are commonly aberrantly expressed in breast cancer (BC). Thus, we aimed to establish an IRL-based tool to improve prognosis prediction in BC patients. Methods We obtained IRL expression profiles in large BC cohorts (N = 911) from The Cancer Genome Atlas (TCGA) database. Then, in light of the correlation between each IRL and recurrence-free survival (RFS), we screened prognostic IRL signatures to construct a novel RFS nomogram via a Cox regression model. Subsequently, the performance of the IRL-based model was evaluated through discrimination, calibration ability, risk stratification ability and decision curve analysis (DCA). Results A total of 52 IRLs were obtained from TCGA. Based on multivariate Cox regression analyses, four IRLs (A1BG-AS1, AC004477.3, AC004585.1 and AC004854.2) and two risk parameters (tumor subtype and TNM stage) were utilized as independent indicators to develop a novel prognostic model. In terms of predictive accuracy, the IRL-based model was distinctly superior to the TNM staging system (AUC: 0.728 VS 0.673, P = 0.010). DCA indicated that our nomogram had favorable clinical practicability. In addition, risk stratification analysis showed that the IRL-based tool efficiently divided BC patients into high- and low-risk groups (P < 0.001). Conclusions A novel IRL-based model was constructed to predict the risk of 5-year RFS in BC. Our model can improve the predictive power of the TNM staging system and identify high-risk patients with tumor recurrence to implement more appropriate treatment strategies.

scholarly journals A novel model with nutrition-related parameters for predicting overall survival of cancer patients

2021 ◽  
Author(s):  
Qi Zhang ◽  
Kangping Zhang ◽  
Xiangrui Li ◽  
Xi Zhang ◽  
Mengmeng Song ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
Nutritional Status ◽  
Cancer Patients ◽  
Cox Regression ◽  
Validation Cohort ◽  
Staging System ◽  
Tnm Staging ◽  
Cox Regression Analysis ◽  
Tnm Staging System

Abstract Background Increasing evidence indicates that nutritional status could influence the survival of cancer patients. This study aims to develop and validate a nomogram with nutrition-related parameters for predicting the overall survival of cancer patients.Patients and Methods 8,749 patients from the multicentre cohort study in China were included as the primary cohort to develop the nomogram, and 696 of these patients were recruited as a validation cohort. Patients nutritional status were assessed using the PG-SGA. LASSO regression models and Cox regression analysis were used for factor selection and nomogram development. The nomogram was then evaluated for its effectiveness in discrimination, calibration, and clinical usefulness by the C-index, calibration curves, and Decision Curve Analysis. Kaplan-Meier survival curves were used to compare the survival rate.Results Seven independent prognostic factors were identified and integrated into the nomogram. The C-index was 0.73 (95% CI, 0.72 to 0.74) and 0.77 (95% CI, 0.74 to 0.81) for the primary cohort and validation cohort, which were both higher than 0.59 (95% CI, 0.58 to 0.61) of the TNM staging system. DCA demonstrated that the nomogram was higher than the TNM staging system and the TNM staging system combined with PG-SGA. Significantly median overall survival differences were found by stratifying patients into different risk groups (score <18.5 and ≥18.5) for each TNM category (all Ps < 0.001).Conclusion Our study screened out seven independent prognostic factors and successfully generated an easy-to-use nomogram, validated and shown a better predictive validity for the overall survival of cancer patients.

Construction and validation of a simple-to-use nomogram incorporating clinicopathological parameters into the TNM staging system to predict prognosis for stage II colorectal cancer.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 31-31
Author(s):  
Shaobo Mo ◽  
Yaqi Li ◽  
Junjie Peng ◽  
SanJun Cai
Keyword(s):  
Colorectal Cancer ◽  
Large Population ◽  
Disease Free Survival ◽  
Staging System ◽  
Tnm Staging ◽  
Stage Ii ◽  
Clinicopathological Parameters ◽  
Tnm Staging System ◽  
Stage Ii Colorectal Cancer ◽  
Validation Set

31 Background: Survival outcomes are significant different in stage II colorectal cancer (CRC) patients with diverse clinicopathological features. Objective of this study is to establish a credible prognostic nomogram incorporating easily obtained parameters for stage II CRC patients. Methods: A total of 1708 stage II CRC patients at Fudan University Shanghai Cancer Center (FUSCC) during 2008 to 2013 were retrospectively analyzed in this study. Cases were randomly separated into training set (n = 1084) and validation set (n = 624). Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors which were subsequently incorporated into a nomogram. The performance of the nomogram was evaluated by C-index and ROC curve to calculate the area under the curve (AUC). The clinical utility of the nomogram was evaluated using decision curve analysis (DCA). Results: In univariate and multivariate analyses, eight parameters were correlated with disease free survival (DFS), which were subsequently selected to draw prognostic nomogram based on DFS. For DFS predictions, the predicted concordance index (C-index) of the nomogram was 0.842 (95% confidence interval (CI), 0.710-0.980), and 0.701 (95% CI, 0.610-0.770) for training and validation set, respectively. The AUC values of ROC predicted 1, 3 and 5-year survival of nomogram in the training and validation groups were 0.869, 0.858, 0.777 and 0.673, 0.714, 0.706, respectively. The recurrence probability calibration curve showed good consistency between actual observations and nomogram-based predictions. DCA showed better clinical application value for the nomogram compared with TNM staging system. Conclusions: A novel nomogram based on a large population study was established and validated, which is a simple-to-use tool for physicians to facilitate the postoperative personalized prognostic evaluation and determine therapeutic strategies for stage II CRC patients.

scholarly journals A Nomogram Based on Aspartate Aminotransferase/Alanine Aminotransferase (AST/ALT) Ratio to Predict Prognosis After Surgery in Gastric Cancer Patients

2020 ◽  
Vol 27 (1) ◽  
pp. 107327482095445
Author(s):  
Linfang Li ◽  
Qiuyao Zeng ◽  
Ning Xue ◽  
Miantao Wu ◽  
Yaqing Liang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Aspartate Aminotransferase ◽  
Alanine Aminotransferase ◽  
Cox Regression ◽  
Staging System ◽  
Tnm Staging ◽  
Study Cohort ◽  
Cox Regression Analysis ◽  
Tnm Staging System ◽  
Resectable Gastric Cancer

Introduction: Using the TMN classification alone to predict survival in patients with gastric cancer has certain limitations, we conducted this study was to develop an effective nomogram based on aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio to predict overall survival (OS) in surgically treated gastric cancer. Methods: we retrospectively analyzed 190 cases of gastric cancer and used Cox regression analysis to identify the significant prognostic factors for OS in patients with resectable gastric cancer. The predictive accuracy of nomogram was assessed using a calibration plot, concordance index (C-index) and decision curve. This was then compared with a traditional TNM staging system. Based on the total points (TPS) by nomogram, we further divided patients into different risk groups. Results: multivariate analysis of the entire cohort revealed that independent risk factors for survival were age, clinical stage and AST/ALT ratio, which were entered then into the nomogram. The calibration curve for the probability of OS showed that the nomogram-based predictions were in good agreement with actual observations. Additionally, the C-index of the established nomogram for predicting OS had a superior discrimination power compared to the TNM staging system [0.794 (95% CI: 0.749-0.839) vs 0.730 (95% CI: 0.688-0.772), p < 0.05]. Decision curve also demonstrated that the nomogram was better than the TNM staging system. Based on TPS of the nomogram, we further subdivided the study cohort into 3 groups including low risk (TPS ≤ 158), middle risk (158 < TPS ≤ 188) and high risk (TPS > 188) categories. The differences in OS rate were significant among the groups. Conclusion: the established nomogram is associated with a more accurate prognostic prediction for individual patients with resectable gastric cancer.

Prognostic nomogram to predict overall survival for patients with perihilar cholangiocarcinoma: A population-based study of SEER database (Preprint)

2020 ◽  
Author(s):  
Chendong Wang
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
Staging System ◽  
Tnm Staging ◽  
Survival Outcome ◽  
Perihilar Cholangiocarcinoma ◽  
Cox Regression Analysis ◽  
Calibration Curves ◽  
Tnm Staging System ◽  
Net Benefits

BACKGROUND Perihilar cholangiocarcinoma (pCCA) is a highly aggressive malignancy with poor prognosis. Accurate prediction is of great significance for patients’ survival outcome. OBJECTIVE The present study aimed to propose a prognostic nomogram for predicting the overall survival (OS) for patients with pCCA. METHODS We conducted a retrospective analysis in a total of 940 patients enrolled from the Surveillance, Epidemiology, and End Results (SEER) program and developed a nomogram based on the prognostic factors identified from the cox regression analysis. Concordance index (C-index), risk group stratification and calibration curves were adopted to test the discrimination and calibration ability of the nomogram with bootstrap method. Decision curves were also plotted to evaluate net benefits in clinical use against TNM staging system. RESULTS On the basis of multivariate analysis, five independent prognostic factors including age, summary stage, surgery, chemotherapy, together with radiation were selected and entered into the nomogram model. The C-index of the model was significantly higher than TNM system in the training set (0.703 vs 0.572, P<0.001), which was also proved in the validation set (0.718 vs 0.588, P<0.001). The calibration curves for 1-, 2-, and 3-year OS probabilities exhibited good agreements between the nomogram-predicted and the actual observation. Decision curves displayed that the nomogram obtained more net benefits than TNM staging system in clinical context. The OS curves of two distinct risk groups stratified by nomogram-predicted survival outcome illustrated statistical difference. CONCLUSIONS We established and validated an easy-to-use prognostic nomogram, which can provide more accurate individualized prediction and assistance in decision making for pCCA patients.

Sign in / Sign up

Export Citation Format

Share Document