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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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Estimates of tumor growth rate variation and screening test sensitivity for all age groups combined. Estimates for all age groups combined, with correction of background incidence (+21%) due to increased hormone therapy use. (a) Estimated variation of tumor growth rates, illustrated by growth curves for the 5th, 25th, 50th, 75th and 95th percentiles. (b) Estimated screening test sensitivity with 95% pointwise confidence intervals.
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Figure 4: Estimates of tumor growth rate variation and screening test sensitivity for all age groups combined. Estimates for all age groups combined, with correction of background incidence (+21%) due to increased hormone therapy use. (a) Estimated variation of tumor growth rates, illustrated by growth curves for the 5th, 25th, 50th, 75th and 95th percentiles. (b) Estimated screening test sensitivity with 95% pointwise confidence intervals.

Mentions: The estimated tumor growth implies that tumors in women 50 to 59 years of age take a mean 1.4 years to grow from 10 mm to 20 mm in diameter, while tumors in women 60 to 69 years of age take a mean time of 2.1 years (Table 1). Overall, the mean time taken to grow from 10 mm to 20 mm was estimated as 1.7 years, but there were large individual variations with an estimated standard deviation of 2.2 years. If we removed the correction for a probable higher background incidence due to increased HRT use, growth rates were somewhat lower (Table 1). There were generally large variations in tumor growth (Figure 4a), and tumor-doubling times at 15 mm varied from 41 days for the first quartile to 234 for the last quartile (Table 1). Comparing the new estimates with earlier estimates based on overlooked cancers found in Spratt and colleagues [9] we found generally good concordance, with only slightly more very fast-growing tumors (Table 2).


Breast cancer tumor growth estimated through mammography screening data.

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

Estimates of tumor growth rate variation and screening test sensitivity for all age groups combined. Estimates for all age groups combined, with correction of background incidence (+21%) due to increased hormone therapy use. (a) Estimated variation of tumor growth rates, illustrated by growth curves for the 5th, 25th, 50th, 75th and 95th percentiles. (b) Estimated screening test sensitivity with 95% pointwise confidence intervals.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Estimates of tumor growth rate variation and screening test sensitivity for all age groups combined. Estimates for all age groups combined, with correction of background incidence (+21%) due to increased hormone therapy use. (a) Estimated variation of tumor growth rates, illustrated by growth curves for the 5th, 25th, 50th, 75th and 95th percentiles. (b) Estimated screening test sensitivity with 95% pointwise confidence intervals.
Mentions: The estimated tumor growth implies that tumors in women 50 to 59 years of age take a mean 1.4 years to grow from 10 mm to 20 mm in diameter, while tumors in women 60 to 69 years of age take a mean time of 2.1 years (Table 1). Overall, the mean time taken to grow from 10 mm to 20 mm was estimated as 1.7 years, but there were large individual variations with an estimated standard deviation of 2.2 years. If we removed the correction for a probable higher background incidence due to increased HRT use, growth rates were somewhat lower (Table 1). There were generally large variations in tumor growth (Figure 4a), and tumor-doubling times at 15 mm varied from 41 days for the first quartile to 234 for the last quartile (Table 1). Comparing the new estimates with earlier estimates based on overlooked cancers found in Spratt and colleagues [9] we found generally good concordance, with only slightly more very fast-growing tumors (Table 2).

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