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Kyle Jon Lafata, PhD

Assistant Professor of Radiation Oncology
Assistant Professor in the Department of Electrical and Computer Engineering
Office: Radiation Physics, Box 3295 DUMC, Durham, NC 27710
Campus Mail: DUMC Radiation Physics, Box 3295 DUMC, Durham, NC 27710

My research focuses on novel mathematical methods and computational techniques that facilitate the discovery of biomarkers otherwise dormant in biomedical images. By incorporating multi-scale information from both radiological images (i.e., radiomics) and digital pathology images (i.e., pathomics), my work aims to characterize the appearance and behavior of disease across different spatial, temporal, and functional domains. Methodologically, I incorporate various computational and mathematical techniques into my research, including image processing, machine learning, statistical pattern recognition, and the applied analysis of stochastic differential equations.

Education and Training

  • Postdoctoral Associate, Radiation Oncology/Radiation Physics Division, Duke University School of Medicine, 2018 - 2020
  • C., Duke University, 2018
  • Ph.D., Duke University, 2018

Publications

Barisoni, Laura, Kyle J. Lafata, Stephen M. Hewitt, Anant Madabhushi, and Ulysses G. J. Balis. “Digital pathology and computational image analysis in nephropathology.” Nat Rev Nephrol 16, no. 11 (November 2020): 669–85. https://doi.org/10.1038/s41581-020-0321-6.

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Liu, Chenyang, Shen-Chiang Hu, Chunhao Wang, Kyle Lafata, and Fang-Fang Yin. “Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data.” Quantitative Imaging in Medicine and Surgery 10, no. 10 (October 2020): 1917–29. https://doi.org/10.21037/qims-19-883.

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Chang, Yushi, Kyle Lafata, William Paul Segars, Fang-Fang Yin, and Lei Ren. “Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN).” Phys Med Biol 65, no. 6 (March 19, 2020): 065009. https://doi.org/10.1088/1361-6560/ab7309.

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Chang, Yushi, Kyle Lafata, Chunhao Wang, Xiaoyu Duan, Ruiqi Geng, Zhenyu Yang, and Fang-Fang Yin. “Digital phantoms for characterizing inconsistencies among radiomics extraction toolboxes.” Biomed Phys Eng Express 6, no. 2 (March 2, 2020): 025016. https://doi.org/10.1088/2057-1976/ab779c.

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Wang, Chunhao, Chenyang Liu, Yushi Chang, Kyle Lafata, Yunfeng Cui, Jiahan Zhang, Yang Sheng, Yvonne Mowery, David Brizel, and Fang-Fang Yin. “Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application.” Frontiers in Oncology 10 (January 2020): 1592. https://doi.org/10.3389/fonc.2020.01592.

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Corradetti, Michael N., Jordan A. Torok, Ace J. Hatch, Eric P. Xanthopoulos, Kyle Lafata, Corbin Jacobs, Christel Rushing, et al. “Dynamic Changes in Circulating Tumor DNA During Chemoradiation for Locally Advanced Lung Cancer.” Adv Radiat Oncol 4, no. 4 (October 2019): 748–52. https://doi.org/10.1016/j.adro.2019.05.004.

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Lafata, Kyle J., Zhennan Zhou, Jian-Guo Liu, Julian Hong, Chris R. Kelsey, and Fang-Fang Yin. “An Exploratory Radiomics Approach to Quantifying Pulmonary Function in CT Images.” Sci Rep 9, no. 1 (August 8, 2019): 11509. https://doi.org/10.1038/s41598-019-48023-5.

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Lafata, Kyle J., Julian C. Hong, Ruiqi Geng, Bradley G. Ackerson, Jian-Guo Liu, Zhennan Zhou, Jordan Torok, Chris R. Kelsey, and Fang-Fang Yin. “Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy.” Phys Med Biol 64, no. 2 (January 8, 2019): 025007. https://doi.org/10.1088/1361-6560/aaf5a5.

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Chang, Yushi, Kyle Lafata, Wenzheng Sun, Chunhao Wang, Zheng Chang, John P. Kirkpatrick, and Fang-Fang Yin. “An investigation of machine learning methods in delta-radiomics feature analysis.” Plos One 14, no. 12 (2019): e0226348. https://doi.org/10.1371/journal.pone.0226348.

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Lafata, K., Z. Zhou, J. G. Liu, and F. F. Yin. “Data clustering based on Langevin annealing with a self-consistent potential.” Quarterly of Applied Mathematics 77, no. 3 (January 1, 2019): 591–613. https://doi.org/10.1090/qam/1521.

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