Improved survival time: What can survival cure models tell us about population-based survival improvements in late-stage colorectal, ovarian, and testicular cancer?
✍ Scribed by Lan Huang; Kathleen A Cronin; Karen A. Johnson; Angela B. Mariotto; Eric J. Feuer
- Publisher
- John Wiley and Sons
- Year
- 2008
- Tongue
- English
- Weight
- 899 KB
- Volume
- 112
- Category
- Article
- ISSN
- 0008-543X
No coin nor oath required. For personal study only.
✦ Synopsis
Abstract
BACKGROUND
The objective of the current study was to investigate the long‐term impact of treatment advances on the survival of patients with late‐stage ovarian, colorectal (American Joint Committee on Cancer stage III, men), and testicular cancers by estimating the increase in the percentage cured from their disease and the change in survival time of uncured patients.
METHODS
Cause‐specific survival data from 1973 to 2000 were obtained from the Surveillance, Epidemiology, and End Results Program. Survival cure models were fit and were used to estimate the gain in life expectancy (GLE) attributed to an increase in the fraction of cured patients and to prolonged survival among noncured patients.
RESULTS
Treatment improvement for ovarian cancer resulted in a total GLE of 2 years, and 80% of that GLE was because of an extension of survival time in uncured patients (from 0.9 years to 2.1 years) rather than an increased cure fraction (from 12% to 14%). In contrast, the cure rate rose from 29% to 47% for colorectal cancer, representing 82% of a 2.8‐year GLE, and from 23% to 81% for testicular cancer, representing 100% of a 24‐year GLE.
CONCLUSIONS
The current results suggested that treatment benefits for testicular and colorectal cancer in men with late‐stage disease primarily are the result of increases in cure fraction, whereas survival gains for ovarian cancer occur despite persisting disease. Cure models, in combination with population‐level data, provide insight into how treatment advances are changing survival and ultimately impacting mortality. Survival patterns reflect the underlying biology of response to cancer treatment and suggest promising directions for future research. Cancer 2008. Published 2008 by the American Cancer Society.