|
Variable
|
Coefficient (standard error)
|
|---|
|
Baseline
|
–0.906 (1.308)
|
|
Age (year)
|
–0.018 (0.014)
|
|
BMI (kg/m2)
|
–0.080 (0.033)
|
|
Previous breast surgery
| |
|
Noa
|
0
|
|
Yes
|
0.502 (0.286)
|
|
Menopausal and HRT status
| |
|
Premenopausala
|
0
|
|
Postmenopausal and no HRT
|
–0.530 (0.357)
|
|
Postmenopausal and HRT
|
0.208 (0.355)
|
|
Imaging technique
| |
|
Analoga
|
0
|
|
Digital
|
0.416 (0.223)
|
|
Predicted PMD
|
0.032 (0.009)
|
- The model is fitted on the complete dataset. To estimate a patient’s risk for masking, the following steps are necessary: texture features values are calculated from the mammogram, the boosting regression model is applied to obtain the predicted PMD, and patient characteristics and predicted PMD are linearly combined with the logistic regression coefficient to obtain interim value z. Finally, exp (z)/(1 + exp (z)) is the predicted risk for masking
-
aReference category
