The diagnostic stage of early detection of breast cancer is currently far from perfect. With the current clinical imaging technology used for diagnostic work-up of suspicious lesions detected at breast cancer screening, approximately one out of six breast cancers are missed. This is in addition to the one out of six breast cancers already missed at the breast cancer screening stage. In addition, the rate of false positive diagnostic work-ups results in more than two out of three biopsies yielding a negative result. Clearly there is room for improvement in this very important clinical diagnostic procedure. Of the many novel imaging technologies being developed for breast cancer imaging, dedicated breast computed tomography (BCT) is one of very few that results in a true tomographic image of the breast with high contrast resolution and does not require the injection of a contrast agent or radiopharmaceutical. This makes it ideal for use as the frontline imaging technology for working up suspicious lesions detected during breast cancer screening or during clinical breast examination. In this project we propose to perform a prospective clinical trial to compare the accuracy of BCT to the current standard imaging technologies for diagnosis of breast cancer in patients with a suspicious lesion identified during breast cancer screening. To maximize the image quality of BCT we will apply to the BCT novel algorithms we have developed that result in more accurate, higher quality images with true quantitative characteristics. These algorithms involve the correction of the acquired BCT projections due to the presence of x-ray scatter, and a novel reconstruction algorithm that correctly represents the image acquisition process as one involving a spectrum of x-ray energy, rather than mono-energetic x-rays. Successful completion of this project will help introduce BCT to the clinical realm by characterizing its true potential or impact in the breast cancer diagnosis stage, where we expect it will result in fewer missed breast cancers and negative biopsies.

Public Health Relevance

The introduction of a new imaging technology that will make the diagnostic stage of early breast cancer detection more accurate will have a fundamental impact on both the survival rate of breast cancer patients and on the number of negative biopsies performed every year on healthy women. By optimizing the image quality of this new modality before characterizing its benefits we will ensure that this impact on women's healthcare is maximized.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Baker, Houston
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Radboud University Nijmegen Medical Center
Zip Code
Caballo, Marco; Boone, John M; Mann, Ritse et al. (2018) An unsupervised automatic segmentation algorithm for breast tissue classification of dedicated breast computed tomography images. Med Phys 45:2542-2559
Fedon, Christian; Caballo, Marco; Sechopoulos, Ioannis (2018) Internal breast dosimetry in mammography: Monte Carlo validation in homogeneous and anthropomorphic breast phantoms with a clinical mammography system. Med Phys :
Caballo, Marco; Mann, Ritse; Sechopoulos, Ioannis (2018) Patient-based 4D digital breast phantom for perfusion contrast-enhanced breast CT imaging. Med Phys 45:4448-4460
Mettivier, G; Bliznakova, K; Sechopoulos, I et al. (2017) Evaluation of the BreastSimulator software platform for breast tomography. Phys Med Biol 62:6446-6466
Sarno, Antonio; Dance, David R; van Engen, Ruben E et al. (2017) A Monte Carlo model for mean glandular dose evaluation in spot compression mammography. Med Phys 44:3848-3860
Rodríguez-Ruiz, Alejandro; Agasthya, Greeshma A; Sechopoulos, Ioannis (2017) The compressed breast during mammography and breast tomosynthesis: in vivo shape characterization and modeling. Phys Med Biol 62:6920-6937
Dance, David R; Sechopoulos, Ioannis (2016) Dosimetry in x-ray-based breast imaging. Phys Med Biol 61:R271-R304