Evaluating the association between radiomic features on breast MRI and dormant tumor cell presence in the SURMOUNT trial.

Kalina Polet Slavkova, Elizabeth S. McDonald, Eric A. Cohen, Lauren Pantalone, Lauren Bayne, Isoris Nivar, Pauleen Sanchez, Walter C. Mankowski, Melanie Brown, Amy Sanders Clark, Killian Rohn, Lewis A. Chodosh, Despina Kontos, Angela DeMichele

Journal of Clinical Oncology - Supplement, vol. 42(16), American Society of Clinical Oncology, 2024 Jun

Sat2Nu: a modular deep learning pipeline for converting fat-suppressed breast MRIs to nonfat-suppressed images with foreseeable applications in abbreviated breast MRI.

Kalina Slavkova, Nehal Doiphode, Chunrui Zou, Alex Anh-Tu Nguyen, Arunima Sharma, Anne Marie McCarthy, Despina Kontos

1317416, 17th International Workshop on Breast Imaging, SPIE, 2024 May 29, p. 1317416

MRI Radiomic phenotypes derived from the ECOG-ACRIN E4112 Trial to assess high-risk ductal carcinoma in situ

Kalina Slavkova, Ruya Kang, Vivian Belenky, Anum Kazerouni, Debosmita Biswas, Hannah Horng, Rhea Chitalia, Michael Hirano, Jennifer Xiao, Ralph Corsetti, Sarah Javid, Derrick Spell, Antonio Wolff, Joseph Sparano, Seema Khan, Christopher Comstock, Justin Romanoff, Jon Steingrimsson, Constantine Gatsonis, Constance Lehman, Savannah Partridge, Despina Kontos, Habib Rahbar

vol. 84(9), American Association for Cancer Research, San Antonio Breast Cancer Symposium, 2024 May 1

An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data

Kalina P. Slavkova, Julie C. DiCarlo, Viraj Wadhwa, Chengyue Wu, John Virostko, Sidharth Kumar, Thomas E. Yankeelov, Jonathan I. Tamir

Magnetic Resonance in Medicine, vol. 89(4), 2023, pp. 1617-1633

Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma

Kalina P. Slavkova, Sahil H. Patel, Zachary Cacini, Anum S. Kazerouni, Andrea L. Gardner, Thomas E. Yankeelov, David A. Hormuth II

Scientific Reports, vol. 13, 2023, p. 2916

Combining radiomic features with background parenchymal enhancement from DCE-MRI data for predicting treatment response in breast cancer

Kalina P. Slavkova, Eric A Cohen, Rhea Chitalia, Snekha Thakran, Walter C Mankowski, Alex Nguyen, Hannah Horng, Elizabeth S McDonald, Michael D Feldman, Angela DeMichele, Despina Kontos

International Society for Magnetic Resonance in Medicine, 2023

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology.

Chengyue Wu, Guillermo Lorenzo, II David A. Hormuth, Ernesto A. B. F. Lima, Kalina P. Slavkova, Julie C. DiCarlo, John Virostko, Caleb M. Phillips, Debra Patt, Caroline Chung, Thomas E. Yankeelov

Biophysics Reviews, vol. 3, 2022, p. 021304

Analysis of Simplicial Complexes to Determine When to Sample for Quantitative DCE-MRI of the Breast.

Julie C. DiCarlo, Angela M. Jarrett, Anum S. Kazerouni, John Virostko, Anna G. Sorace, Kalina P. Slavkova, Deborah Patt, Boone Goodgame, Stephanie Woodard, Sarah Avery, Thomas E. Yankeelov

Magnetic Resonance in Medicine, vol. 89(3), 2022 Feb, pp. 1134-1150

Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting

John Virostko, Anna G. Sorace, Kalina P. Slavkova, Anum S. Kazerouni, Angela M. Jarrett, Julie C. DiCarlo, Stefanie Woodard, Sarah Avery, Boone Goodgame, Debra Patt, Thomas E. Yankeelov

Breast Cancer Research, vol. 23(1), 2021

Characterizing Errors in Pharmacokinetic Parameters from Analyzing Quantitative Abbreviated DCE-MRI Data in Breast Cancer

Kalina P. Slavkova, Julie C. DiCarlo, Anum S. Kazerouni, John Virostko, Anna G. Sorace, Debra Patt, Boone Goodgame, Thomas E. Yankeelov

Tomography, vol. 7(3), 2021, pp. 253-267

Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor

Keesha E. Erickson, Oleksii S. Rukhlenko, Md Shahinuzzaman, Kalina P. Slavkova, Yen Ting Lin, Ryan Suderman, Edward C. Stites, Marian Anghel, Richard G. Posner, Dipak Barua, Boris N. Kholodenko, William S. Hlavacek

PLoS Computational Biology, vol. 15(1), 2019, pp. e1006706