Survival in differentiated thyroid carcinoma: A comparison between the 7th and 8th editions of the AJCC / UICC TNM staging system and the ATA initial risk stratification system

Head & Neck ◽  
2021 ◽  
Author(s):  
Beatriz Godoi Cavalheiro ◽  
Leandro Luongo Matos ◽  
Ana Kober Nogueira Leite ◽  
Marco Aurelio Vamondes Kulcsar ◽  
Claudio Roberto Cernea ◽  
...  
Keyword(s):  
Thyroid Carcinoma ◽  
Risk Stratification ◽  
Staging System ◽  
Differentiated Thyroid Carcinoma ◽  
Tnm Staging ◽  
Tnm Staging System ◽  
Stratification System ◽  
Risk Stratification System

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 A Nomogram for Predicting Survival in Patients With Breast Cancer Liver Metastasis: A Population-Based Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Yu Xiong ◽  
Xia Shi ◽  
Qi Hu ◽  
Xingwei Wu ◽  
Enwu Long ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Liver Metastasis ◽  
Risk Stratification ◽  
Staging System ◽  
Tnm Staging ◽  
Tnm Staging System ◽  
Stratification System ◽  
Breast Cancer Liver Metastasis ◽  
Risk Stratification System

ObjectiveThe prognosis of patients with breast cancer liver metastasis (BCLM) was poor. We aimed at constructing a nomogram to predict overall survival (OS) for BCLM patients using the SEER (Surveillance Epidemiology and End Results) database, thus choosing an optimized therapeutic regimen to treat.MethodsWe identified 1173 patients with BCLM from the SEER database and randomly divided them into training (n=824) and testing (n=349) cohorts. The Cox proportional hazards model was applied to identify independent prognostic factors for BCLM, based on which a nomogram was constructed to predict 1-, 2-, and 3-year OS. Its discrimination and calibration were evaluated by the Concordance index (C-index) and calibration plots, while the accuracy and benefits were assessed by comparing it to AJCC-TNM staging system using the decision curve analysis (DCA). Kaplan-Meier survival analyses were applied to test the clinical utility of the risk stratification system.ResultsGrade, marital status, surgery, radiation therapy, chemotherapy, CS tumor size, tumor subtypes, bone metastatic, brain metastatic, and lung metastatic were identified to be independent prognostic factors of OS. In comparison with the AJCC-TNM staging system, an improved C-index was obtained (training group: 0.701 vs. 0.557, validation group: 0.634 vs. 0.557). The calibration curves were consistent between nomogram-predicted survival probability and actual survival probability. Additionally, the DCA curves yielded larger net benefits than the AJCC-TNM staging system. Finally, the risk stratification system can significantly distinguish the ones with different survival risk based on the different molecular subtypes.ConclusionWe have successfully built an effective nomogram and risk stratification system to predict OS in BCLM patients, which can assist clinicians in choosing the appropriate treatment strategies for individual BCLM patients.

scholarly journals Clinical Value of Lymph Node Ratio Integration with the 8th Edition of the UICC TNM Classification and 2015 ATA Risk Stratification Systems for Recurrence Prediction in Papillary Thyroid Cancer

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jandee Lee ◽  
Seul Gi Lee ◽  
Kwangsoon Kim ◽  
Seung Hyuk Yim ◽  
Haengrang Ryu ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Lymph Node Ratio ◽  
Staging System ◽  
Tnm Staging ◽  
Papillary Thyroid ◽  
Clinical Value ◽  
Tnm Staging System ◽  
Node Ratio ◽  
Stratification System ◽  
Risk Stratification System

Abstract Recently, the 2015 American Thyroid Association (ATA) risk stratification and the 8th edition of the American Joint Committee on Cancer/Union for International Cancer Control (AJCC/UICC) TNM staging system were released. This study was conducted to assess the clinical value of the lymph node ratio (LNR) as a predictor of recurrence when integrated with these newly released stratification systems, and to compare the predictive accuracy of the modified systems with that of the newly released systems. The optimal LNR threshold value for predicting papillary thyroid cancer (PTC) recurrence was 0.17857 using the Contal and O’Quigley method. The 8th edition of the AJCC/UICC TNM staging system with the LNR and the 2015 ATA risk stratification system with the LNR were significant predictors of recurrence. Furthermore, calculation of the proportion of variance explained (PVE), the Akaike information criterion (AIC), Harrell’s c index, and the incremental area under the curve (iAUC) revealed that the 8th edition of the TNM staging system with the LNR, and the 2015 ATA risk stratification system with the LNR, showed the best predictive performance. Integration of the LNR with the TNM staging and the ATA risk stratification systems should improve prediction of recurrence in patients with PTC.

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.

The multi-institutional myeloma group clinico-genomic risk stratification system: A blinded validation

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 8521-8521
Author(s):  
S. A. Tuchman ◽  
W. Chng ◽  
A. Anguiano ◽  
W. T. Barry ◽  
F. Zhan ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Validation Cohort ◽  
Staging System ◽  
Discovery Cohort ◽  
Oncogenic Pathway ◽  
Pathway Activation ◽  
Stratification System ◽  
Genomic Risk ◽  
Risk Stratification System

8521 Background: Several clinical and molecular prognostic factors (e.g, International Staging System [ISS] stage, plasma cell labeling index, genomic models) exist for multiple myeloma (MM). We hypothesized that exploiting gene signatures representative of oncogenic pathway deregulation (i.e., Ras, Myc, etc.), would improve MM prognostication and also aid with the identification of novel therapeutic targets. Methods: Using a discovery cohort (n=47) of patients with MM and corresponding gene expression data, we built upon current molecular risk-stratification and devised a Bayesian genomic (“metagene”) model for prognosis. We validated that model in an independent patient cohort (n=207). Finally, we incorporated ISS staging and clinical variables to construct a combined Clinico-Genomic Risk Stratification System. We further validated the combined model in a separate cohort (n=72), in a blinded manner. Results: Using gene signatures predictive of oncogenic pathway activation in the discovery cohort, we identified specific patterns (metagenes) of signaling pathway activation with prognostic relevance. In an independent validation cohort, this metagene-based model accurately predicted event free survival (EFS) independently of ISS (multivariate hazard ratio [HR] 3.4 for ISS stage, and 5.4 for the metagene model, p=0.002). Using multivariate risk modeling, we incorporated ISS staging and the metagene model into a Clinico-Genomic System and successfully stratified the validation cohort into three groups (low, intermediate, and high risk) with markedly different EFS (HR 4.2 for intermediate risk and 14.0 for high risk vs. the low risk cohort, p<0.0001). In an additional blinded validation, the Clinico-Genomic System again accurately predicted median overall survival (68.7 [low risk] vs 24.7 [intermediate risk] vs 18.7 months [high risk], p<0.0001); more accurately than either ISS or other reported genomic models. Conclusions: A combined Clinico-Genomic Risk Stratification System, building on patterns of oncogenic pathway activation and ISS staging system, improves upon current prognostic models in MM and identifies novel pathway targets for future therapeutic consideration. No significant financial relationships to disclose.

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