by Jing Wang, Pam Gottschal, Lilu Ding, Daniëlle.W.A van
Veldhuizen, Wenli Lu, Nehmat Houssami, Marcel J.W. Greuter, Geertruida H. de
Bock
The Breast: VOLUME 55, P69-74, FEBRUARY 01,
2021
Highlights
•Mammographic sensitivity is a key indicator of screening
effectiveness.
•Previous model using logistic function might overestimate
size-specific sensitivity.
•Our model showed that sensitivity increased from 0 to 85%
for tumor sizes from 2 to 20 mm.
•Our model may provide a better representation of data
observed in screening programs.
Background
Instead of a single value for mammographic sensitivity, a
sensitivity function based on tumor size more realistically reflects
mammography’s detection capability. Because previous models may have
overestimated size-specific sensitivity, we aimed to provide a novel approach
to improve sensitivity estimation as a function of tumor size.
Methods
Using aggregated data on interval and screen-detected
cancers, observed tumor sizes were back-calculated to the time of screening
using an exponential tumor growth model and a follow-up time of 4 years. From
the observed number of detected cancers and an estimation of the number of
false-negative cancers, a model for the sensitivity as a function of tumor size
was determined. A univariate sensitivity analysis was conducted by varying
follow-up time and tumor volume doubling time (TVDT). A systematic review was
conducted for external validation of the sensitivity model.
Results
Aggregated data of 22,915 screen-detected and 10,670
interval breast cancers from the Dutch screening program were used. The model
showed that sensitivity increased from 0 to 85% for tumor sizes from 2 to
20 mm. When TVDT was set at the upper and lower limits of the confidence
interval, sensitivity for a 20-mm tumor was 74% and 93%, respectively. The
estimated sensitivity gave comparable estimates to those from two of three
studies identified by our systematic review.
Conclusion
Derived from aggregated breast screening outcomes data, our
model’s estimation of sensitivity as a function of tumor size may provide a
better representation of data observed in screening programs than other models.
