Department of Radiology

The CBIG is actively working on developing a rigorous research program that investigates the role of imaging as a quantitative biomarker for improving personalized clinical decision making for breast cancer screening, prognosis, and treatment. We are working on a multimodality approach using emerging breast imaging technologies such as digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and diffusion-weighted (DW) MR imaging.



Identifying women at high-risk of breast cancer is critical for implementing personalized breast cancer screening protocols and forming preventive strategies. We are working on developing computational methods to characterize breast tissue composition, including breast density and parenchymal texture, from emerging digital breast images modalities and estimating the predictive value of these imaging features to assess a woman's risk of developing breast cancer. Our goal is to incorporate novel imaging biomarkers of cancer risk and clinical breast cancer risk information to improve breast cancer risk assessment for women.

Breast density estimation from digital mammography
Breast density estimation from digital mammography using adaptive fuzzy C-means clustering dense tissue segmentation

Selected Publications:

  1. McCarthy AM, Keller BM, Pantalone LM, Hsieh MK, Synnestvedt M, Conant EF, Armstrong K, Kontos D. Racial Differences in Quantitative Measures of Area and Volumetric Breast Density. J Natl Cancer Inst. 2016 Apr 29;108(10). pii: djw104. doi: 10.1093/jnci/djw104. Print 2016 Oct. PubMed PMID: 27130893.
  2. Pertuz S, McDonald ES, Weinstein SP, Conant EF, Kontos D. Fully Automated Quantitative Estimation of Volumetric Breast Density from Digital Breast Tomosynthesis Images: Preliminary Results and Comparison with Digital Mammography and MR Imaging. Radiology. 2016 Apr;279(1):65-74. doi: 10.1148/radiol.2015150277. Epub 2015 Oct 21. PubMed PMID: 26491909.
  3. Keller BM, Chen J, Daye D, Conant EF, Kontos D. Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography. Breast Cancer Res. 2015 Aug 25;17:117. doi: 10.1186/s13058-015-0626-8. PubMed PMID: 26303303.
  4. Zheng Y, Keller BM, Ray S, Wang Y, Conant EF, Gee JC, Kontos D. Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment. Med Phys. 2015 Jul;42(7):4149-60. doi: 10.1118/1.4921996. PubMed PMID: 26133615.


We are working on developing methods to characterize the imaging characteristics of cancer tumors from multimodality breast imaging data. Our goal is to investigate the value of these imaging features as prognostic biomarkers and incorporate this information in clinical decision making. We are working on elucidating associations between histopathology, cancer receptors, gene expression profiles, and the corresponding imaging phenotype for breast. These imaging biomarkers could complement the current methods for assessing breast cancer prognosis and guide clinical decisions for identifying women who would benefit from specific tailored treatment options.

Breast tumor segmentation from dynamic contrast-enhanced magnetic resonance imaging and kinetic curve characterization

Selected Publications:

  1. Mahrooghy M, Ashraf AB, Daye D, McDonald ES, Rosen M, Mies C, Feldman M, Kontos D. Pharmacokinetic Tumor Heterogeneity as a Prognostic Biomarker for Classifying Breast Cancer Recurrence Risk. IEEE Trans Biomed Eng. 2015 Jun;62(6):1585-94. doi: 10.1109/TBME.2015.2395812. Epub 2015 Jan 23. PubMed PMID: 25622311.
  2. Ashraf AB, Daye D, Gavenonis S, Mies C, Feldman M, Rosen M, Kontos D. Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. Radiology. 2014 Aug;272(2):374-84. doi: 10.1148/radiol.14131375. Epub 2014 Apr 4. PubMed PMID: 24702725.
  3. Ashraf AB, Gavenonis SC, Daye D, Mies C, Rosen MA, Kontos D. A multichannel Markov random field framework for tumor segmentation with an application to classification of gene expression-based breast cancer recurrence risk. IEEE Trans Med Imaging. 2013 Apr;32(4):637-48. doi: 10.1109/TMI.2012.2219589. Epub 2012 Sep 19. PubMed PMID: 23008246.


We are developing approaches to integrate structural and functional information from multimodality breast images that could be used to assess and guide personalized breast cancer treatment, including chemotherapy, endocrine therapy, and radiation treatment. In addition, we are looking into the effect of preventative interventions for high-risk women, such as chemoprevention and lifestyle interventions that can effectively reduce the risk of developing breast cancer. Imaging biomarkers in this setting can be used to quantify the effect of treatment, assess the effectiveness of drugs in development, and identify targets for new therapeutic agents.

Breast tumor segmentation
Pre-treatment DCE-MRI kinetic maps of a neo-adjuvant chemotherapy responder

Selected Publications:

  1. Ou Y, Weinstein SP, Conant EF, Englander S, Da X, Gaonkar B, Hsieh MK, Rosen M, DeMichele A, Davatzikos C, Kontos D. Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy. Magn Reson Med. 2015 Jun;73(6):2343-56. doi: 10.1002/mrm.25368. Epub 2014 Jul 15. PubMed PMID: 25046843.
  2. Ashraf A, Gaonkar B, Mies C, DeMichele A, Rosen M, Davatzikos C, Kontos D. Breast DCE-MRI Kinetic Heterogeneity Tumor Markers: Preliminary Associations With Neoadjuvant Chemotherapy Response. Transl Oncol. 2015 Jun;8(3):154-62. doi: 10.1016/j.tranon.2015.03.005. PubMed PMID: 26055172.
  3. Wu S, Weinstein SP, DeLeo MJ 3rd, Conant EF, Chen J, Domchek SM, Kontos D. Quantitative assessment of background parenchymal enhancement in breast MRI predicts response to risk-reducing salpingo-oophorectomy: preliminary evaluation in a cohort of BRCA1/2 mutation carriers. Breast Cancer Res. 2015 May 19;17:67. doi: 10.1186/s13058-015-0577-0. PubMed PMID: 25986460.


NIH/NCI R01 (1R01CA197000-01A1)
"Multi-Parametric 4-D Imaging Biomarkers For Neoadjuvant Treatment Response"
PI: D. Kontos

NIH/NCI U24 (1U24CA189523-01A1)
"Cancer Imaging Phenomics Software Suite: Application to Brain and Breast Cancer"
PI: C. Davatzikos/D. Kontos

Penn ITMAT Translational Bio-Imaging Center (TBIC) "Integrating Imaging with Genomic Biomarkers for Breast Cancer Prognostication"
PI: D. Kontos
02/01/2013- 01/31/2014

NIH/NCI R01 (R01-CA164305-01)
"Statistical Methods for Cancer Absolute Risk Prediction"
PI: J. Chen (Univ. of Pennsylvania)

Penn Basser Center for BRCA ½
"Imaging Biomarkers for Risk-Reduction Management of BRCA1/2 Carriers"
PI: D. Kontos
07/01/2013- 06/30/2015

Penn Center for Biomedical Image Computing and Analytics "Quantitative Characterization of Spatio-temporal Tumor Heterogeneity via 4D Breast DCE-MRI Registration"
PI: D. Kontos
07/01/2013- 06/30/2014

NIH/NCI R01 (1R01CA183086-01A1)
PI: J. Bernstein/M. Pike (MSKCC)

NIH/NCI (1R01CA161749-01A1)
"Effect of Breast Density on Screening Recall with Digital Breast Tomosynthesis"
PI: D. Kontos
05/04/2012 – 03/31/2017

American Cancer Society (RSGHP CPHPS-119586)
"Computer-Assisted Risk Estimation (CARe) for Breast Cancer with Digital Mammography"
PI: D. Kontos
07/01/2010 - 06/30/2015

DOD Concept Award (BC086591)
"Mammographic texture as a biomarker for targeted SERM chemoprevention of breast cancer"
PI: D. Kontos
09/01/2009 - 08/31/2011

Penn ITMAT Translational Bio-Imaging Center (TBIC)
"Quantitative breast magnetic resonance imaging (MRI) biomarkers of breast cancer prognosis"
PI: D. Kontos
02/01/2010 - 01/31/2012

NSF Collaborative Research III Grant (IIS-0916690)
"Modeling, detection and analysis of branching structures in medical imaging"
PI: V. Megalooikonomou (Temple Univ.)/ P.R. Bakic (Univ. Penn Subcontract)
05/01/2009 - 04/30/2012

DOD BCRP HBCU Partnership Training Award (BC083639)
"Image Based Biomarkers of Breast Cancer Risk: Analysis of Risk Disparity among Minority Populations"
PI: F. Liu (Delaware State Univ.), A.D.A. Maidment (Univ. Penn Subcontract)
04/01/2009 - 03/31/2013

National Institutes of Health/National Cancer Institute (1U54CA163313-01)
"Penn Center for Innovation in Personalized Breast Screening"
Center PIs: K. Armstrong, M.D. Schnall
Project 2 "Novel Imaging Biomarkers for Guiding Personalized Screening Recommendations"
Leader: D. Kontos
09/21/2011 - 05/31/2017

Breast Cancer Alliance
"Effective Utilization of Breast Tomosynthesis using Imaging Markers to Guide Personalized Screening"
PI: D. Kontos
01/01/2012 - 12/31/2013

NIH/NCI (1R21CA155906-01A1)
"Breast Tomosynthesis Texture-Based Segmentation for Volumetric Density Estimation"
PI: D. Kontos
03/09/2012 - 12/31/2014

Share This Page: