Limits...
How does age affect baseline screening mammography performance measures? A decision model.

Keen JD, Keen JE - BMC Med Inform Decis Mak (2008)

Bottom Line: We compared the single baseline screening mammogram decision with the no screening alternative.The model predicts the total intervention rate = 0.013*AGE2 - 0.67*AGE + 40, or 34/1000 at age 40 to 47/1000 at age 60.Therefore, the positive biopsy (intervention) fraction varies from 6% at age 40 to 32% at age 60.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Radiology, John H. Stroger Jr. Hospital of Cook County, 1901 West Harrison Street, Chicago, IL 60612-9985, USA. jkeen@ccbhs.org

ABSTRACT

Background: In order to promote consumer-oriented informed medical decision-making regarding screening mammography, we created a decision model to predict the age dependence of the cancer detection rate, the recall rate and the secondary performance measures (positive predictive values, total intervention rate, and positive biopsy fraction) for a baseline mammogram.

Methods: We constructed a decision tree to model the possible outcomes of a baseline screening mammogram in women ages 35 to 65. We compared the single baseline screening mammogram decision with the no screening alternative. We used the Surveillance Epidemiology and End Results national cancer database as the primary input to estimate cancer prevalence. For other probabilities, the model used population-based estimates for screening mammography accuracy and diagnostic mammography outcomes specific to baseline exams. We varied radiologist performance for screening accuracy.

Results: The cancer detection rate increases from 1.9/1000 at age 40 to 7.2/1000 at age 50 to 15.1/1000 at age 60. The recall rate remains relatively stable at 142-157/1000, which varies from 73-236/1000 at age 50 depending on radiologist performance. The positive predictive value of a screening mammogram increases from 1.3% at age 40 to 9.8% at age 60, while the positive predictive value of a diagnostic mammogram varies from 2.9% at age 40 to 19.2% at age 60. The model predicts the total intervention rate = 0.013*AGE2 - 0.67*AGE + 40, or 34/1000 at age 40 to 47/1000 at age 60. Therefore, the positive biopsy (intervention) fraction varies from 6% at age 40 to 32% at age 60.

Conclusion: Breast cancer prevalence, the cancer detection rate, and all secondary screening mammography performance measures increase substantially with age.

Show MeSH

Related in: MedlinePlus

Decision model for baseline screening mammography used to predict primary and secondary performance measures. An asymptomatic woman between ages 35 and 65 getting a baseline mammogram faces four possible outcomes. These outcomes depend on her health status (cancer or healthy) and the initial radiologist reading (positive or negative). The corresponding inputs for the circular probability nodes are population-based values of age-dependent cancer prevalence and radiologist accuracy. There is a wide range of radiologist performance for sensitivity and specificity. The probability of a true-positive outcome is equivalent to the cancer detection rate, a screening benefit. The false-positive outcome involves healthy women and is a screening harm. Besides additional diagnostic imaging, this outcome may result in further diagnostic evaluation and intervention. The probability values for the third and fourth branch points (BiR12initial, etc.) use population-based data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2563001&req=5

Figure 1: Decision model for baseline screening mammography used to predict primary and secondary performance measures. An asymptomatic woman between ages 35 and 65 getting a baseline mammogram faces four possible outcomes. These outcomes depend on her health status (cancer or healthy) and the initial radiologist reading (positive or negative). The corresponding inputs for the circular probability nodes are population-based values of age-dependent cancer prevalence and radiologist accuracy. There is a wide range of radiologist performance for sensitivity and specificity. The probability of a true-positive outcome is equivalent to the cancer detection rate, a screening benefit. The false-positive outcome involves healthy women and is a screening harm. Besides additional diagnostic imaging, this outcome may result in further diagnostic evaluation and intervention. The probability values for the third and fourth branch points (BiR12initial, etc.) use population-based data.

Mentions: Decision analysis applies quantitative methods to help optimize decision making under uncertainty, with applications in diagnostic radiology [18]. We created a decision tree that models a simplified screening scenario starting with the choice that a woman faces starting in her late thirties: either to start screening for breast cancer and begin with a baseline screening mammogram, or postpone screening by doing nothing, as shown by the decision node square in Figure 1. The first branch point (probability node circles) of each decision splits into women with and without cancer, while the second branch point for those getting screening splits into a positive or negative mammogram, with terminal outcome nodes (triangles) for those with cancer. The third branch point includes diagnostic and subsequent imaging, consultations, and interventions for healthy women. We did not include likely stages of diagnosis or the effects of overdiagnosis since they do not affect the calculation of the performance measures. To keep the model simple and easier to comprehend, we did not simulate the effects of repeat screening. We used DATA™ 3.5 software (TreeAge, Williamstown, MA, 1999) and Excel (Microsoft, Redmond WA, 2003) to construct the computer model.


How does age affect baseline screening mammography performance measures? A decision model.

Keen JD, Keen JE - BMC Med Inform Decis Mak (2008)

Decision model for baseline screening mammography used to predict primary and secondary performance measures. An asymptomatic woman between ages 35 and 65 getting a baseline mammogram faces four possible outcomes. These outcomes depend on her health status (cancer or healthy) and the initial radiologist reading (positive or negative). The corresponding inputs for the circular probability nodes are population-based values of age-dependent cancer prevalence and radiologist accuracy. There is a wide range of radiologist performance for sensitivity and specificity. The probability of a true-positive outcome is equivalent to the cancer detection rate, a screening benefit. The false-positive outcome involves healthy women and is a screening harm. Besides additional diagnostic imaging, this outcome may result in further diagnostic evaluation and intervention. The probability values for the third and fourth branch points (BiR12initial, etc.) use population-based data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2563001&req=5

Figure 1: Decision model for baseline screening mammography used to predict primary and secondary performance measures. An asymptomatic woman between ages 35 and 65 getting a baseline mammogram faces four possible outcomes. These outcomes depend on her health status (cancer or healthy) and the initial radiologist reading (positive or negative). The corresponding inputs for the circular probability nodes are population-based values of age-dependent cancer prevalence and radiologist accuracy. There is a wide range of radiologist performance for sensitivity and specificity. The probability of a true-positive outcome is equivalent to the cancer detection rate, a screening benefit. The false-positive outcome involves healthy women and is a screening harm. Besides additional diagnostic imaging, this outcome may result in further diagnostic evaluation and intervention. The probability values for the third and fourth branch points (BiR12initial, etc.) use population-based data.
Mentions: Decision analysis applies quantitative methods to help optimize decision making under uncertainty, with applications in diagnostic radiology [18]. We created a decision tree that models a simplified screening scenario starting with the choice that a woman faces starting in her late thirties: either to start screening for breast cancer and begin with a baseline screening mammogram, or postpone screening by doing nothing, as shown by the decision node square in Figure 1. The first branch point (probability node circles) of each decision splits into women with and without cancer, while the second branch point for those getting screening splits into a positive or negative mammogram, with terminal outcome nodes (triangles) for those with cancer. The third branch point includes diagnostic and subsequent imaging, consultations, and interventions for healthy women. We did not include likely stages of diagnosis or the effects of overdiagnosis since they do not affect the calculation of the performance measures. To keep the model simple and easier to comprehend, we did not simulate the effects of repeat screening. We used DATA™ 3.5 software (TreeAge, Williamstown, MA, 1999) and Excel (Microsoft, Redmond WA, 2003) to construct the computer model.

Bottom Line: We compared the single baseline screening mammogram decision with the no screening alternative.The model predicts the total intervention rate = 0.013*AGE2 - 0.67*AGE + 40, or 34/1000 at age 40 to 47/1000 at age 60.Therefore, the positive biopsy (intervention) fraction varies from 6% at age 40 to 32% at age 60.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Radiology, John H. Stroger Jr. Hospital of Cook County, 1901 West Harrison Street, Chicago, IL 60612-9985, USA. jkeen@ccbhs.org

ABSTRACT

Background: In order to promote consumer-oriented informed medical decision-making regarding screening mammography, we created a decision model to predict the age dependence of the cancer detection rate, the recall rate and the secondary performance measures (positive predictive values, total intervention rate, and positive biopsy fraction) for a baseline mammogram.

Methods: We constructed a decision tree to model the possible outcomes of a baseline screening mammogram in women ages 35 to 65. We compared the single baseline screening mammogram decision with the no screening alternative. We used the Surveillance Epidemiology and End Results national cancer database as the primary input to estimate cancer prevalence. For other probabilities, the model used population-based estimates for screening mammography accuracy and diagnostic mammography outcomes specific to baseline exams. We varied radiologist performance for screening accuracy.

Results: The cancer detection rate increases from 1.9/1000 at age 40 to 7.2/1000 at age 50 to 15.1/1000 at age 60. The recall rate remains relatively stable at 142-157/1000, which varies from 73-236/1000 at age 50 depending on radiologist performance. The positive predictive value of a screening mammogram increases from 1.3% at age 40 to 9.8% at age 60, while the positive predictive value of a diagnostic mammogram varies from 2.9% at age 40 to 19.2% at age 60. The model predicts the total intervention rate = 0.013*AGE2 - 0.67*AGE + 40, or 34/1000 at age 40 to 47/1000 at age 60. Therefore, the positive biopsy (intervention) fraction varies from 6% at age 40 to 32% at age 60.

Conclusion: Breast cancer prevalence, the cancer detection rate, and all secondary screening mammography performance measures increase substantially with age.

Show MeSH
Related in: MedlinePlus