Breast cancer affects 1 in 8 women in the United States. It is the second leading cause of cancer deaths amongst women, resulting in 39,000 deaths per year. As a result, much work has been done to optimize the evaluation of cancer through mammography. However, assessing mammograms is limited by variations among practitioners in practice, including deficiencies in their reporting of these imaging examinations as well as in their interpretations. Two main sources of these variations in practice are incompleteness of the radiological observations reported to characterize the abnormalities seen in images and inconsistency of these observations with respect to the radiologists'overall impression. We hypothesize that the quantification and enforcement of completeness and consistency of radiological observations will improve the positive predictive value of diagnostic mammography. We propose a decision support system that provides feedback to radiologists during the reporting of their radiological observations. We will develop this system by creating novel statistical models to link radiological observations, computational imaging features, and disease to recognize incompleteness and inconsistency in reporting. We will then harness these models to create a quantifiable metric of observation quality. We propose a research plan with the following specific aims: (1) To characterize breast lesions seen in mammography images by capturing computationally- derived (""""""""quantitative"""""""") imaging features and radiologist-derived observational (""""""""semantic"""""""") features, (2) develop a radiological Decision Support System (DSS), (3) evaluate our DSS in mammography practice. Our methods will lead not only to better diagnostic accuracy and positive predictive value of diagnostic mammography, but they will be extensible to other imaging domains and possibly to other medical domains where diagnostic reasoning is documented in dictated reports.

Public Health Relevance

The results of this project have the potential to impact the consistency and completeness of mammography reporting methods. This will potentially positively impact communication between referring physicians and mammography specialists as well as provide better positive predictive value of mammograms. It is relevant to better diagnosis and treatment of breast cancer.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Predoctoral Individual National Research Service Award (F31)
Project #
5F31CA171789-02
Application #
8703501
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Vallejo-Estrada, Yolanda
Project Start
2013-07-01
Project End
2015-12-29
Budget Start
2014-09-30
Budget End
2015-09-29
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Stanford University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
City
Stanford
State
CA
Country
United States
Zip Code
94304
Gimenez, Francisco J; Wu, Yirong; Burnside, Elizabeth S et al. (2014) A novel method to assess incompleteness of mammography reports. AMIA Annu Symp Proc 2014:1758-67