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Our goal was to obtain age- and stage-specific Catalan breast cancer survival functions.
Therefore, the methods used seem to provide a robust estimate of the breast cancer survival functions in Catalonia.
On the other hand, we obtained detailed breast cancer survival functions that will be used for modeling the effect of screening and adjuvant treatments in Catalonia.
This work presents the methodology used to estimate age- and stage-specific Catalan breast cancer survival functions from scarce Catalan survival data by adapting the age- and stage-specific US functions.
From the historical summary stage information and additional data on the extent of disease, the CISNET inferred breast cancer survival functions for the US population in 5 age groups (30 39, 40 49, 50 59, 60 69 and 70 84 years) and for 5 disease stages at diagnosis (American Joint Committee on Cancer (AJCC) staging system: I, II-node negative (II-), II-node positive (II+), III, IV) [ 6].
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One of the CISNET groups, lead by Marvin Zelen and Sandra Lee at the Dana-Farber Cancer Institute in Boston [ 3], developed a stochastic model that requires specific breast cancer survival probability density functions (pdf s) by age and disease stage at diagnosis.
As for PAI-1, Kaplan-Meier survival curves representing probability of breast cancer survival as a function of PAI-1 and uPA status are represented in Figures 5 and 6 respectively.
As these account for more than two-thirds of newly diagnosed breast cancer patients, it is likely that the increasing use of adjuvant therapy has had an important function in breast cancer survival improvement.
A similar conceptual criticism can be addressed to the work by Yakovlev and colleagues [ 12], who found a two-component structure of the hazard function in breast cancer survival and suggested possible explanations based on the heterogeneity of "malignant potential" remaining in treated tumours.
In the 1980s, breast cancer survival rates for the two were nearly identical.
Gu-Trantien, C. et al. CD4 follicular helper T cell infiltration predicts breast cancer survival.
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