Using radiation risk models in cancer screening simulations: important assumptions and effects on outcome projections

Abstract

Purpose: To evaluate the effect of incorporating radiation risk into microsimulation (first-order Monte Carlo) models for breast and lung cancer screening to illustrate effects of including radiation risk on patient outcome projections.

Materials and methods: All data used in this study were derived from publicly available or deidentified human subject data. Institutional review board approval was not required. The challenges of incorporating radiation risk into simulation models are illustrated with two cancer screening models (Breast Cancer Model and Lung Cancer Policy Model) adapted to include radiation exposure effects from mammography and chest computed tomography (CT), respectively. The primary outcome projected by the breast model was life expectancy (LE) for BRCA1 mutation carriers. Digital mammographic screening beginning at ages 25, 30, 35, and 40 years was evaluated in the context of screenings with false-positive results and radiation exposure effects. The primary outcome of the lung model was lung cancer-specific mortality reduction due to annual screening, comparing two diagnostic CT protocols for lung nodule evaluation. The Metropolis-Hastings algorithm was used to estimate the mean values of the results with 95% uncertainty intervals (UIs).

Results: Without radiation exposure effects, the breast model indicated that annual digital mammography starting at age 25 years maximized LE (72.03 years; 95% UI: 72.01 years, 72.05 years) and had the highest number of screenings with false-positive results (2.0 per woman). When radiation effects were included, annual digital mammography beginning at age 30 years maximized LE (71.90 years; 95% UI: 71.87 years, 71.94 years) with a lower number of screenings with false-positive results (1.4 per woman). For annual chest CT screening of 50-year-old females with no follow-up for nodules smaller than 4 mm in diameter, the lung model predicted lung cancer-specific mortality reduction of 21.50% (95% UI: 20.90%, 22.10%) without radiation risk and 17.75% (95% UI: 16.97%, 18.41%) with radiation risk.

Conclusion: Because including radiation exposure risk can influence long-term projections from simulation models, it is important to include these risks when conducting modeling-based assessments of diagnostic imaging.

Figure 1:

ERR of radiation exposures from annual digital mammography. ERR estimates are based on absorbed dose to the breast of 4.15 mGy for annual digital screening mammography starting at age 25 years. The radiation risks at specific ages were calculated by summing the cumulative absorbed doses to the breast from annual screening by using (Equations 2) and (3) for ERR_AA and ERR_AE, respectively.

Figure 2:

Age-specific breast cancer incidence from annual digital screening mammography beginning at age 25 years without and with radiation exposure risk. The ERR_AA and ERR_AE models produced results that were almost indistinguishable.

Figure 3a:

Radiation exposure risk influences optimal age to begin mammographic screening. (a) Efficiency frontier plot shows the benefit of annual screening (Annu.) with digital mammography (DM) starting at different ages in the context of false-positive screening test results (FPs), without added radiation exposure risk. (b) Plot shows comparison of same strategies as in a, with radiation exposure risk included. Screening beginning at age 25 years provides decreased LE and greater false-positive results than does screening beginning at age 30 years and is no longer considered an “efficient” screening strategy.

Figure 3b:

Radiation exposure risk influences optimal age to begin mammographic screening. (a) Efficiency frontier plot shows the benefit of annual screening (Annu.) with digital mammography (DM) starting at different ages in the context of false-positive screening test results (FPs), without added radiation exposure risk. (b) Plot shows comparison of same strategies as in a, with radiation exposure risk included. Screening beginning at age 25 years provides decreased LE and greater false-positive results than does screening beginning at age 30 years and is no longer considered an “efficient” screening strategy.

Figure 4a:

(a, b) Mortality reduction from screening as a function of time since entry into a hypothetical annual screening program (to age 74 years) for cohorts of (a) 60-year-old male patients and (b) 60-year-old female patients. Solid horizontal line = 20% mortality reduction (for reference). The upper two curves represent a screening strategy where follow-up (FU) in patients with suspicious lung nodules less than 4 mm in diameter is performed with CT examinations (FU < 4 mm), with incorporation of radiation risk (rad) and without the incorporation of radiation risk (no rad). The bottom two curves represent the results from a screening strategy with no follow up of lung nodules less than 4 mm in diameter (no FU < 4 mm).

Figure 4b:

(a, b) Mortality reduction from screening as a function of time since entry into a hypothetical annual screening program (to age 74 years) for cohorts of (a) 60-year-old male patients and (b) 60-year-old female patients. Solid horizontal line = 20% mortality reduction (for reference). The upper two curves represent a screening strategy where follow-up (FU) in patients with suspicious lung nodules less than 4 mm in diameter is performed with CT examinations (FU < 4 mm), with incorporation of radiation risk (rad) and without the incorporation of radiation risk (no rad). The bottom two curves represent the results from a screening strategy with no follow up of lung nodules less than 4 mm in diameter (no FU < 4 mm).