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Breast cancer tumor growth estimated through mammography screening data.

Weedon-Fekjaer H, Lindqvist BH, Vatten LJ, Aalen OO, Tretli S - Breast Cancer Res. (2008)

Bottom Line: The mean time a tumor needed to grow from 10 mm to 20 mm in diameter was estimated as 1.7 years, increasing with age.Compared with previously used Markov models for tumor progression, the applied model gave considerably higher model fit (85% increased predictive power) and provided estimates directly linked to tumor size.There is a large variation in breast cancer tumor growth, with faster growth among younger women.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Etiological Research, Cancer Registry of Norway, Institute of Population-based Cancer Research, Montebello, N-0310 Oslo, Norway. harald.weedon-fekjaer@kreftregisteret.no

ABSTRACT

Introduction: Knowledge of tumor growth is important in the planning and evaluation of screening programs, clinical trials, and epidemiological studies. Studies of tumor growth rates in humans are usually based on small and selected samples. In the present study based on the Norwegian Breast Cancer Screening Program, tumor growth was estimated from a large population using a new estimating procedure/model.

Methods: A likelihood-based estimating procedure was used, where both tumor growth and the screen test sensitivity were modeled as continuously increasing functions of tumor size. The method was applied to cancer incidence and tumor measurement data from 395,188 women aged 50 to 69 years.

Results: Tumor growth varied considerably between subjects, with 5% of tumors taking less than 1.2 months to grow from 10 mm to 20 mm in diameter, and another 5% taking more than 6.3 years. The mean time a tumor needed to grow from 10 mm to 20 mm in diameter was estimated as 1.7 years, increasing with age. The screen test sensitivity was estimated to increase sharply with tumor size, rising from 26% at 5 mm to 91% at 10 mm. Compared with previously used Markov models for tumor progression, the applied model gave considerably higher model fit (85% increased predictive power) and provided estimates directly linked to tumor size.

Conclusion: Screening data with tumor measurements can provide population-based estimates of tumor growth and screen test sensitivity directly linked to tumor size. There is a large variation in breast cancer tumor growth, with faster growth among younger women.

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Overview of the new cancer growth model. New cancer growth model: assumptions, model parameters, and likelihood function.
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Figure 3: Overview of the new cancer growth model. New cancer growth model: assumptions, model parameters, and likelihood function.

Mentions: Although the growth rates vary throughout the lifespan of each tumor, a smoothly increasing function is likely to serve as a good model for growth rates at the population level, as departures from one individual to the next probably are smoothed out at the population level. For small tumors, growth is mostly governed by the cell reproduction rate of the given tumor cells. This constantly higher growth rate leads to an exponential growth curve with constant doubling times. When tumors grow larger, growth velocity is likely to decrease with the increasing burden on the host, as the tumor receives more limited nutrition. One family of curves starting with near-exponential growth, before gradually leveling off below a given maximum level, is the general logistic function (see examples in Figure 3).


Breast cancer tumor growth estimated through mammography screening data.

Weedon-Fekjaer H, Lindqvist BH, Vatten LJ, Aalen OO, Tretli S - Breast Cancer Res. (2008)

Overview of the new cancer growth model. New cancer growth model: assumptions, model parameters, and likelihood function.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2481488&req=5

Figure 3: Overview of the new cancer growth model. New cancer growth model: assumptions, model parameters, and likelihood function.
Mentions: Although the growth rates vary throughout the lifespan of each tumor, a smoothly increasing function is likely to serve as a good model for growth rates at the population level, as departures from one individual to the next probably are smoothed out at the population level. For small tumors, growth is mostly governed by the cell reproduction rate of the given tumor cells. This constantly higher growth rate leads to an exponential growth curve with constant doubling times. When tumors grow larger, growth velocity is likely to decrease with the increasing burden on the host, as the tumor receives more limited nutrition. One family of curves starting with near-exponential growth, before gradually leveling off below a given maximum level, is the general logistic function (see examples in Figure 3).

Bottom Line: The mean time a tumor needed to grow from 10 mm to 20 mm in diameter was estimated as 1.7 years, increasing with age.Compared with previously used Markov models for tumor progression, the applied model gave considerably higher model fit (85% increased predictive power) and provided estimates directly linked to tumor size.There is a large variation in breast cancer tumor growth, with faster growth among younger women.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Etiological Research, Cancer Registry of Norway, Institute of Population-based Cancer Research, Montebello, N-0310 Oslo, Norway. harald.weedon-fekjaer@kreftregisteret.no

ABSTRACT

Introduction: Knowledge of tumor growth is important in the planning and evaluation of screening programs, clinical trials, and epidemiological studies. Studies of tumor growth rates in humans are usually based on small and selected samples. In the present study based on the Norwegian Breast Cancer Screening Program, tumor growth was estimated from a large population using a new estimating procedure/model.

Methods: A likelihood-based estimating procedure was used, where both tumor growth and the screen test sensitivity were modeled as continuously increasing functions of tumor size. The method was applied to cancer incidence and tumor measurement data from 395,188 women aged 50 to 69 years.

Results: Tumor growth varied considerably between subjects, with 5% of tumors taking less than 1.2 months to grow from 10 mm to 20 mm in diameter, and another 5% taking more than 6.3 years. The mean time a tumor needed to grow from 10 mm to 20 mm in diameter was estimated as 1.7 years, increasing with age. The screen test sensitivity was estimated to increase sharply with tumor size, rising from 26% at 5 mm to 91% at 10 mm. Compared with previously used Markov models for tumor progression, the applied model gave considerably higher model fit (85% increased predictive power) and provided estimates directly linked to tumor size.

Conclusion: Screening data with tumor measurements can provide population-based estimates of tumor growth and screen test sensitivity directly linked to tumor size. There is a large variation in breast cancer tumor growth, with faster growth among younger women.

Show MeSH
Related in: MedlinePlus