End Results of Cancer Patients: From Population-Based Cancer Registry Data

1987 ◽  
pp. 81-87
Author(s):  
Akira Takano ◽  
Yoshi Okuno
Keyword(s):  
Cancer Patients ◽  
Cancer Registry ◽  
Population Based ◽  
Registry Data ◽  
Cancer Registry Data ◽  
End Results

scholarly journals Long‐term survival and conditional survival of cancer patients in Japan using population‐based cancer registry data

2014 ◽  
Vol 105 (11) ◽  
pp. 1480-1486 ◽  
Author(s):  
Yuri Ito ◽  
Isao Miyashiro ◽  
Hidemi Ito ◽  
Satoyo Hosono ◽  
Dai Chihara ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Cancer Registry ◽  
Population Based ◽  
Registry Data ◽  
Conditional Survival ◽  
Term Survival ◽  
Long Term Survival ◽  
Cancer Registry Data

Population-based incidence estimates of uveal melanoma in Germany. Supplementing cancer registry data by case–control data

2006 ◽  
Vol 15 (2) ◽  
pp. 165-170 ◽  
Author(s):  
Andreas Stang ◽  
Andrea Schmidt-Pokrzywniak ◽  
Martin Lehnert ◽  
Donald M. Parkin ◽  
Jaques Ferlay ◽  
...  
Keyword(s):  
Uveal Melanoma ◽  
Cancer Registry ◽  
Population Based ◽  
Case Control ◽  
Registry Data ◽  
Control Data ◽  
Cancer Registry Data

scholarly journals 365MO Exploring changes in glioblastoma treatment patterns in Europe and the USA with population-based cancer registry data

2020 ◽  
Vol 31 ◽  
pp. S398
Author(s):  
F. Giusti ◽  
M.D.C. Martos ◽  
S. Scoccianti ◽  
L. Neamtiu ◽  
G. Randi ◽  
...  
Keyword(s):  
Cancer Registry ◽  
Population Based ◽  
Treatment Patterns ◽  
Registry Data ◽  
Cancer Registry Data ◽  
The Usa

scholarly journals Differences in cancer survival by area-level socio-economic disadvantage: A population-based study using cancer registry data

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0228551 ◽  
Author(s):  
Nina Afshar ◽  
Dallas R. English ◽  
Tony Blakely ◽  
Vicky Thursfield ◽  
Helen Farrugia ◽  
...  
Keyword(s):  
Cancer Registry ◽  
Cancer Survival ◽  
Population Based ◽  
Registry Data ◽  
Economic Disadvantage ◽  
Population Based Study ◽  
Cancer Registry Data

Predicting the absolute risk of dying from colorectal cancer and from other causes using population-based cancer registry data

2011 ◽  
Vol 31 (5) ◽  
pp. 489-500 ◽  
Author(s):  
Minjung Lee ◽  
Kathleen A. Cronin ◽  
Mitchell H. Gail ◽  
Eric J. Feuer
Keyword(s):  
Colorectal Cancer ◽  
Cancer Registry ◽  
Absolute Risk ◽  
Population Based ◽  
Registry Data ◽  
Cancer Registry Data ◽  
The Absolute

Incidence patterns and survival of gynecological sarcoma in Germany: Analysis of population-based cancer registry data on 2106 women, 2009-2013.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e13058-e13058
Author(s):  
Klaus Pietzner ◽  
Nina Buttmann ◽  
Jalid Sehouli ◽  
Klaus Kraywinkel
Keyword(s):  
Cancer Registry ◽  
Survival Rates ◽  
Female Genital Tract ◽  
Relative Survival ◽  
Endometrial Stromal Sarcoma ◽  
Population Based ◽  
Registry Data ◽  
Uterine Leiomyosarcoma ◽  
Cancer Registry Data ◽  
Stromal Sarcoma

e13058 Background: Sarcoma of the female genital tract are rare tumors. They are described to be associated with a poor prognosis, when compared to gynecogical carcinoma. Aim of this study was to report incidence patterns and survival rates for gynecological sarcoma in Germany. Methods: Clinical data and survival rates for patients with gynecological sarcoma diagnosed in Germany between 2009 and 2013 were extracted from the German national centre for population-based cancer registry data. Incidence patterns and 5-year-relative survival rates were calculated. Results: A total of 2,106 gynecological sarcoma (GS) were eligible for analysis. The uterus was the most common site with 87.2% of all cases. The annual age-standardized incidence rate was 7.7 per 1 million women for all gynecological sarcoma. The median age at diagnosis was 59 years. The prognosis ranged according to site and subtype from a poor 5-year-relative survival of 47.6% (uterine leiomyosarcoma) to a very good 5-year-relative survival of 97.2% (endometrial stromal sarcoma). Conclusions: Despite the rareness of gynecological sarcoma, the size of the dataset allows a differentiation of subtypes according to morphology and site of origin. Clinically relevant differences in incidence and prognosis between subgroups were observed.

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