scholarly journals Role of postoperative radiotherapy in pT3N0 rectal cancer: A risk‐stratification system based on population analyses

2019 ◽  
Vol 8 (3) ◽  
pp. 1024-1033 ◽  
Author(s):  
Yun‐xia Huang ◽  
Yan‐zong Lin ◽  
Jin‐luan Li ◽  
Xue‐qing Zhang ◽  
Li‐rui Tang ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Risk Stratification ◽  
Postoperative Radiotherapy ◽  
Population Analyses ◽  
Stratification System ◽  
Risk Stratification System

666 A NOVEL RISK STRATIFICATION SYSTEM FOR CHEMOTHERAPY FAILURE IN TESTICULAR GERM CELL TUMOR

2012 ◽  
Vol 187 (4S) ◽  
Author(s):  
Jessica Lubahn ◽  
Nicholas Cost ◽  
Mehrad Adibi ◽  
Adam Romman ◽  
Ganesh Raj ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Germ Cell ◽  
Germ Cell Tumor ◽  
Testicular Germ Cell Tumor ◽  
Cell Tumor ◽  
Testicular Germ Cell ◽  
Stratification System ◽  
Risk Stratification System

The Influence of Age on Disease Outcome in 2015 ATA High Risk Differentiated Thyroid Cancer Patients

2021 ◽  
Author(s):  
Evert F.s. van Velsen ◽  
Robin P. Peeters ◽  
Merel T. Stegenga ◽  
F.j. van Kemenade ◽  
Tessa M. van Ginhoven ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
High Risk ◽  
Risk Stratification ◽  
Differentiated Thyroid Cancer ◽  
Response To Therapy ◽  
Disease Outcome ◽  
High Risk Patients ◽  
Risk Patients ◽  
Stratification System ◽  
Risk Stratification System

Objective Recent research suggests that the addition of age improves the 2015 American Thyroid Association (ATA) Risk Stratification System for differentiated thyroid cancer (DTC). The aim of our study was to investigate the influence of age on disease outcome in ATA High Risk patients with a focus on differences between patients with papillary (PTC) and follicular thyroid cancer (FTC). Methods We retrospectively studied adult patients with High Risk DTC from a Dutch university hospital. Logistic regression and Cox proportional hazards models were used to estimate the effects of age (at diagnosis) and several age cutoffs (per five years increment between 20 and 80 years) on (i) response to therapy, (ii) developing no evidence of disease (NED), (iii) recurrence, and (iv) disease specific mortality (DSM). Results We included 236 ATA High Risk patients (32% FTC) with a median follow-up of 6 years. Age, either continuously or dichotomously, had a significant influence on having an excellent response after initial therapy, developing NED, recurrence, and DSM for PTC and FTC. For FTC, an age cutoff of 65 or 70 years showed the best statistical model performance, while this was 50 or 60 years for PTC. Conclusions In a population of patients with High Risk DTC, older age has a significant negative influence on disease outcomes. Slightly different optimal age cutoffs were identified for the different outcomes, and these cutoffs differed between PTC and FTC. Therefore, the ATA Risk Stratification System may further improve should age be incorporated as an additional risk factor.

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