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

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

Bottom Line: For other probabilities, the model used population-based estimates for screening mammography accuracy and diagnostic mammography outcomes specific to baseline exams.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.

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Related in: MedlinePlus

Predicted secondary performance measures: positive predictive values for screening and diagnostic mammograms (PPVS, PPVD). Increasing cancer prevalence and screening sensitivity with age mean radiologists detect more cancers and the potential absolute benefit of baseline mammography increases with age. Recall exams are mostly false-positive mammograms, which cause unwanted collateral costs from screening. The relative stability of the recall rate makes the positive predictive value performance measures increase with age. The reciprocal of the positive predictive value is the number of screening recall mammograms or positive diagnostic mammograms needed to detect a cancer.
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Figure 4: Predicted secondary performance measures: positive predictive values for screening and diagnostic mammograms (PPVS, PPVD). Increasing cancer prevalence and screening sensitivity with age mean radiologists detect more cancers and the potential absolute benefit of baseline mammography increases with age. Recall exams are mostly false-positive mammograms, which cause unwanted collateral costs from screening. The relative stability of the recall rate makes the positive predictive value performance measures increase with age. The reciprocal of the positive predictive value is the number of screening recall mammograms or positive diagnostic mammograms needed to detect a cancer.

Mentions: Figures 2 and 3 show the model-predicted primary events (CDR and recall rate) associated with baseline mammography, while Figures 4 and 5 summarize predicted secondary performance measures. Table 3 shows the regression equations for incidence, prevalence and model predictions with the associated R2 value. The increase in CDR with age generally tracks the prevalence 2nd order polynomial, while the recall rate is linear and stable with age. Both the primary events and the secondary performance measures have a wide range of values depending on radiologist performance at the 10th or 90th percentiles. Specificity dominates the recall rate by an order of magnitude: the false-positive outcomes (140/1000) and recall rate (147/1000) at age 50 are about 20 times the CDR (7.2/1000). At age 50, the recall rate range from 10th to 90th percentile is 16.3%, but this decreases only to 16.1% when using a median sensitivity at the extremes of specificity. Therefore, equal absolute percentage changes in radiologist sensitivity or specificity would have disparate effects.


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

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

Predicted secondary performance measures: positive predictive values for screening and diagnostic mammograms (PPVS, PPVD). Increasing cancer prevalence and screening sensitivity with age mean radiologists detect more cancers and the potential absolute benefit of baseline mammography increases with age. Recall exams are mostly false-positive mammograms, which cause unwanted collateral costs from screening. The relative stability of the recall rate makes the positive predictive value performance measures increase with age. The reciprocal of the positive predictive value is the number of screening recall mammograms or positive diagnostic mammograms needed to detect a cancer.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Predicted secondary performance measures: positive predictive values for screening and diagnostic mammograms (PPVS, PPVD). Increasing cancer prevalence and screening sensitivity with age mean radiologists detect more cancers and the potential absolute benefit of baseline mammography increases with age. Recall exams are mostly false-positive mammograms, which cause unwanted collateral costs from screening. The relative stability of the recall rate makes the positive predictive value performance measures increase with age. The reciprocal of the positive predictive value is the number of screening recall mammograms or positive diagnostic mammograms needed to detect a cancer.
Mentions: Figures 2 and 3 show the model-predicted primary events (CDR and recall rate) associated with baseline mammography, while Figures 4 and 5 summarize predicted secondary performance measures. Table 3 shows the regression equations for incidence, prevalence and model predictions with the associated R2 value. The increase in CDR with age generally tracks the prevalence 2nd order polynomial, while the recall rate is linear and stable with age. Both the primary events and the secondary performance measures have a wide range of values depending on radiologist performance at the 10th or 90th percentiles. Specificity dominates the recall rate by an order of magnitude: the false-positive outcomes (140/1000) and recall rate (147/1000) at age 50 are about 20 times the CDR (7.2/1000). At age 50, the recall rate range from 10th to 90th percentile is 16.3%, but this decreases only to 16.1% when using a median sensitivity at the extremes of specificity. Therefore, equal absolute percentage changes in radiologist sensitivity or specificity would have disparate effects.

Bottom Line: For other probabilities, the model used population-based estimates for screening mammography accuracy and diagnostic mammography outcomes specific to baseline exams.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