||1R01CA226805-01 Interpret this number
||Boston University Medical Campus
||Improving Accuracy and Reliability in Cancer Screening Tests
Current clinical practice in screening tests involves subjective interpretation of patients' test results such as
mammograms by trained experts. Substantial variability is often reported between radiologists' visual
classifications of breast images, impacting the accuracy and consistency of common screening tests
including mammography. Factors related to patients and raters and the technology itself may impact
experts' ratings of breast cancer and density, an important predictor of breast cancer. However, the study of
accuracy and consistency between radiologists' ratings in large-scale cancer longitudinal screening studies
is challenging due to the ordinal nature of the classifications and many experts each contributing ratings.
Newly emerging processes including automated 3-D procedures provide exciting potential for estimating
breast density in routine clinical settings. Currently very few statistical approaches and summary measures
exist to model the consistency and accuracy between several radiologists' ordinal ratings. Further, few
methods can investigate the influence of patient and radiologist characteristics, the use of automated
procedures and comparison of the different technologies upon accuracy and consistency.
Our goals are to develop new statistical methods based upon generalized linear mixed models and latent
variable models to study accuracy and consistency amongst many experts in large-scale screening studies.
Our approach can flexibly accommodate many experts' ratings and other factors to examine their influence
on consistency and accuracy. We will derive novel model-based summary measures of agreement and
accuracy. We will implement our new statistical methods in recent large-scale breast imaging studies. A key
strength of our proposed research is to provide medical researchers with a flexible modeling approach and
novel summary measures that utilize all the data simultaneously, where conclusions can be drawn about
the consistency between typical experts and patients in the populations, greatly increasing efficiency and
power. The study of patient and rater characteristics on the levels of consistency and accuracy between
raters' classifications will translate to improvements in training radiologists and practice of interpreting
mammograms, and ultimately, a more effective breast screening procedure.
A paired kappa to compare binary ratings across two medical tests.
, Edwards D.
Statistics in medicine, 2019-07-30; 38(17), p. 3272-3287.