Cancer Patient Survival - Patterns, Comparisons, Trends: A Population-based Cancer Registry Study in Finland
- 1 January 1999
- journal article
- Published by Medical Journals Sweden AB in Acta Oncologica
- Vol. 38 (3) , 283-294
- https://doi.org/10.1080/028418699431348
Abstract
The effects of primary site, sex, age, stage and histological type on cancer patient survival were analysed on the basis of the population-based material of the Finnish Cancer Registry from 1985 to 1994. In addition, trends in survival were constructed for the period 1955-1994. Detailed site-specific data are published as Supplement 12 to Vol. 38 of Acta Oncologica. Within a given site, the survival differences by gender were not large. However, because of different site distributions, the average prognosis for female patients, all sites taken together, was superior to that of males: the 5-year relative survival rates (RSR) were 58% and 43%, respectively. In general, older patients had a poorer outcome compared with younger patients (partly because of different stage and histology distributions). Stage was a strong determinant of patient survival. In some cancers with a poor average prognosis the 5-year RSR for localized tumours was reasonable, e.g. 61% for stomach cancer, males, 34% for gallbladder cancer, females, and 29% for lung cancer, males. Most of the survival rates clearly increased over time. In addition to improvements in cancer treatment, changes over time in several other factors affect the trends, such as changes in the stage distribution (early diagnosis as a result of health education, improved diagnostic methods, screening, etc.) and in the composition of the patient material because of changing definitions of cancer (e.g. papilloma versus papillary carcinoma of the bladder, occult carcinoma of the thyroid, and early prostate cancer). The large Cancer Registry material (466,000 patients) enabled accurate estimates of the survival rates of cancer patients in Finland. These rates reflect the effectiveness of the healthcare system as a whole and are useful for planning and evaluation purposes. However, the estimated survival rates are based on grouped data, and cannot be directly applied for predicting the prognoses of individual patients, although they can be used as guidelines.Keywords
This publication has 7 references indexed in Scilit:
- Comparison of a genetic algorithm neural network with logistic regression for predicting outcome after surgery for patients with nonsmall cell lung carcinomaCancer, 1997
- Artificial neural networks improve the accuracy of cancer survival predictionCancer, 1997
- Interpreting Survival Differences and TrendsTumori Journal, 1997
- Social class as a prognostic factor in breast cancer survivalCancer, 1990
- A computer program package for relative survival analysisComputer Programs in Biomedicine, 1985
- Cancer Survival Corrected for Heterogeneity in Patient WithdrawalBiometrics, 1982