
LONDON (Reuters), Mar 24 - More Europeans are beating cancer, perhaps due to more widespread screening and earlier diagnosis, according to a study published on Tuesday.
While there were differences among countries and different groups of patients, new data taken from cancer registries of 23 European nations showed the proportion of people cured of the most common cancers has risen.
"The good news is that for most cancers, survival has increased during the 1980s and 1990s," said Alexander Eggermont, president of the European Cancer Organization, who did not take part in the long-running Eurocare study.
The study compared two periods -- 1988 to 1990 and 1997 to 1999 -- and found that the proportion of men and women cured of lung cancer rose from 6% to 8%, stomach cancer cures went from 15% to 18%, and colorectal cancer cure rates went from 42% to 49%.
Where people lived played a role in the cancer cure rates, according to the Eurocare study comprising data covering 23 countries and more than 151 million people.
For all cancers combined, most men, about 47%, were cured in Iceland and most women, 59%, were cured in France and Finland. In Poland the fewest men, 21%, and women, 38%, were cured.
At 5%, Denmark, the Czech Republic, and Poland had the lowest proportion of cured lung cancer patients while France and Spain at more than 10% had the highest.
"Without this information, it would be impossible to assess whether improvements in cancer diagnosis, treatment, and care are actually having an effect on the outcome for patients," Eggermont said.
The data also highlighted gaps between countries for breast cancer, suggesting that national screening programs adopted in some Western countries are helping fight the disease.
The difference between Poland, the Czech Republic, and Slovenia compared to more western European countries was now about 10%, the researchers said in the European Journal of Cancer.
"The data also tells us what cancers and which areas of Europe need to be targeted for further research and investment," Eggermont added in a statement.
Last Updated: 2009-03-24 10:32:50 -0400 (Reuters Health)
Related Reading
EU health chief says cancer screenings must double, January 23, 2009
Copyright © 2009 Reuters Limited. All rights reserved. Republication or redistribution of Reuters content, including by framing or similar means, is expressly prohibited without the prior written consent of Reuters. Reuters shall not be liable for any errors or delays in the content, or for any actions taken in reliance thereon. Reuters and the Reuters sphere logo are registered trademarks and trademarks of the Reuters group of companies around the world.


![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)





![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)








