Kyle Jon Lafata
Thaddeus V. Samulski Assistant Professor of Radiation Oncology
Kyle Lafata is the Thaddeus V. Samulski Assistant Professor at Duke University in the Departments of Radiation Oncology, Radiology, Medical Physics, and Electrical & Computer Engineering. After earning his PhD in Medical Physics in 2018, he completed postdoctoral training at the U.S. Department of Veterans Affairs in the Big Data Scientist Training Enhancement Program. Prof. Lafata has broad expertise in imaging science, digital pathology, computer vision, biophysics, and applied mathematics. His dissertation work focused on the applied analysis of stochastic differential equations and high-dimensional radiomic phenotyping, where he developed physics-based computational methods and soft-computing paradigms to interrogate images. These included stochastic modeling, self-organization, and quantum machine learning (i.e., an emerging branch of research that explores the methodological and structural similarities between quantum systems and learning systems).
Prof. Lafata has worked in various areas of computational medicine and biology, resulting in 39 peer-reviewed journal publications, 15 invited talks, and more than 50 national conference presentations. At Duke, the Lafata Lab focuses on the theory, development, and application of multiscale computational biomarkers. Using computational and mathematical methods, they study the appearance and behavior of disease across different physical length-scales (i.e., radiomics ~10−3 m, pathomics ~10−6 m, and genomics ~10−9 m) and time-scales (e.g., the natural history of disease, response to treatment). The overarching goal of the lab is to develop and apply new technology that transforms imaging into basic science findings and computational biomarker discovery.
Current Research Interests
Current Appointments & Affiliations
- Thaddeus V. Samulski Assistant Professor of Radiation Oncology, Radiation Oncology, Clinical Science Departments 2023
- Assistant Professor of Radiation Oncology, Radiation Oncology, Clinical Science Departments 2021
- Assistant Professor in Radiology, Radiology, Clinical Science Departments 2022
- Member of the Duke Cancer Institute, Duke Cancer Institute, Institutes and Centers 2020
Contact Information
- Radiation Physics, Box 3295 DUMC, Durham, NC 27710
- Radiation Physics, Box 3295 DUMC, Durham, NC 27710
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kyle.lafata@duke.edu
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kylelafata.com
- Background
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Education, Training, & Certifications
- Postdoctoral Associate, Radiation Oncology/Radiation Physics Division, Duke University School of Medicine 2018 - 2020
- C., Duke University 2018
- Ph.D., Duke University 2018
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Previous Appointments & Affiliations
- Assistant Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2021 - 2022
- Assistant Professor in Radiology, Radiology, Clinical Science Departments 2021 - 2022
- Assistant Professor in Radiology, Radiology, Clinical Science Departments 2020 - 2021
- Assistant Professor of Radiation Oncology, Radiation Oncology, Clinical Science Departments 2020
- Research
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Selected Grants
- Targeting the B Cell Response to Treat Antibody-Mediated Rejection awarded by National Institutes of Health 2021 - 2028
- Dynamic imaging and tissue biomarker models to delineate indolent from aggressive breast calcifications awarded by National Institutes of Health 2022 - 2027
- Computational Pathology of Proteinuric Diseases (R01) awarded by National Institutes of Health 2018 - 2026
- Multi-scale characterization of radiation resistance in head and neck squamous cell carcinoma awarded by Department of Defense 2021 - 2024
- Publications & Artistic Works
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Selected Publications
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Academic Articles
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Rigiroli, Francesca, Jocelyn Hoye, Reginald Lerebours, Peijie Lyu, Kyle J. Lafata, Anru R. Zhang, Alaattin Erkanli, et al. “Exploratory analysis of mesenteric-portal axis CT radiomic features for survival prediction of patients with pancreatic ductal adenocarcinoma.” Eur Radiol, March 10, 2023. https://doi.org/10.1007/s00330-023-09532-0.Full Text Link to Item
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Kelleher, Colm B., Jacob Macdonald, Tracy A. Jaffe, Brian C. Allen, Kevin R. Kalisz, Travis H. Kauffman, Jordan D. Smith, et al. “A Faster Prostate MRI: Comparing a Novel Denoised, Single-Average T2 Sequence to the Conventional Multiaverage T2 Sequence Regarding Lesion Detection and PI-RADS Score Assessment.” J Magn Reson Imaging, January 6, 2023. https://doi.org/10.1002/jmri.28577.Full Text Link to Item
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DeFreitas, Mariana R., Amadu Toronka, Marybeth A. Nedrud, Sarah Cubberley, Islam H. Zaki, Brandon Konkel, Hope E. Uronis, et al. “CT-derived body composition measurements as predictors for neoadjuvant treatment tolerance and survival in gastroesophageal adenocarcinoma.” Abdom Radiol (Ny) 48, no. 1 (January 2023): 211–19. https://doi.org/10.1007/s00261-022-03695-y.Full Text Link to Item
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Kierans, Andrea S., Kyle J. Lafata, Daniel R. Ludwig, Lauren M. B. Burke, Victoria Chernyak, Kathryn J. Fowler, Tyler J. Fraum, et al. “Comparing Survival Outcomes of Patients With LI-RADS-M Hepatocellular Carcinomas and Intrahepatic Cholangiocarcinomas.” J Magn Reson Imaging 57, no. 1 (January 2023): 308–17. https://doi.org/10.1002/jmri.28218.Full Text Link to Item
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Wang, Yuqi, Xiang Li, Meghana Konanur, Brandon Konkel, Elisabeth Seyferth, Nathan Brajer, Jian-Guo Liu, Mustafa R. Bashir, and Kyle J. Lafata. “Towards optimal deep fusion of imaging and clinical data via a model-based description of fusion quality.” Med Phys, December 22, 2022. https://doi.org/10.1002/mp.16181.Full Text Link to Item
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Yang, Zhenyu, Kyle J. Lafata, Xinru Chen, James Bowsher, Yushi Chang, Chunhao Wang, and Fang-Fang Yin. “Quantification of lung function on CT images based on pulmonary radiomic filtering.” Med Phys 49, no. 11 (November 2022): 7278–86. https://doi.org/10.1002/mp.15837.Full Text Link to Item
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Carpenter, David J., Brahma Natarajan, Muzamil Arshad, Divya Natesan, Olivia Schultz, Michael J. Moravan, Charlotte Read, et al. “Prognostic Model for Intracranial Progression after Stereotactic Radiosurgery: A Multicenter Validation Study.” Cancers (Basel) 14, no. 21 (October 22, 2022). https://doi.org/10.3390/cancers14215186.Full Text Link to Item
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Lafata, Kyle J., Yuqi Wang, Brandon Konkel, Fang-Fang Yin, and Mustafa R. Bashir. “Radiomics: a primer on high-throughput image phenotyping.” Abdom Radiol (Ny) 47, no. 9 (September 2022): 2986–3002. https://doi.org/10.1007/s00261-021-03254-x.Full Text Link to Item
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Jiang, Hanyu, Bin Song, Yun Qin, Meghana Konanur, Yuanan Wu, Matthew D. F. McInnes, Kyle J. Lafata, and Mustafa R. Bashir. “Modifying LI-RADS on Gadoxetate Disodium-Enhanced MRI: A Secondary Analysis of a Prospective Observational Study.” J Magn Reson Imaging 56, no. 2 (August 2022): 399–412. https://doi.org/10.1002/jmri.28056.Full Text Link to Item
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Ding, Yuqin, Mathias Meyer, Peijie Lyu, Francesca Rigiroli, Juan Carlos Ramirez-Giraldo, Kyle Lafata, Siyun Yang, and Daniele Marin. “Can radiomic analysis of a single-phase dual-energy CT improve the diagnostic accuracy of differentiating enhancing from non-enhancing small renal lesions?” Acta Radiol 63, no. 6 (June 2022): 828–38. https://doi.org/10.1177/02841851211010396.Full Text Link to Item
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Allphin, Alex J., Yvonne M. Mowery, Kyle J. Lafata, Darin P. Clark, Alex M. Bassil, Rico Castillo, Diana Odhiambo, Matthew D. Holbrook, Ketan B. Ghaghada, and Cristian T. Badea. “Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden.” Tomography 8, no. 2 (March 10, 2022): 740–53. https://doi.org/10.3390/tomography8020061.Full Text Open Access Copy Link to Item
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Glass, Carolyn, Kyle J. Lafata, William Jeck, Roarke Horstmeyer, Colin Cooke, Jeffrey Everitt, Matthew Glass, David Dov, and Michael A. Seidman. “The Role of Machine Learning in Cardiovascular Pathology.” Can J Cardiol 38, no. 2 (February 2022): 234–45. https://doi.org/10.1016/j.cjca.2021.11.008.Full Text Link to Item
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Jiang, Hanyu, Bin Song, Yun Qin, Yi Wei, Meghana Konanur, Yuanan Wu, Islam H. Zaki, Matthew D. F. McInnes, Kyle J. Lafata, and Mustafa R. Bashir. “Data-Driven Modification of the LI-RADS Major Feature System on Gadoxetate Disodium-Enhanced MRI: Toward Better Sensitivity and Simplicity.” J Magn Reson Imaging 55, no. 2 (February 2022): 493–506. https://doi.org/10.1002/jmri.27824.Full Text Link to Item
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Ji, Hangjie, Kyle Lafata, Yvonne Mowery, David Brizel, Andrea L. Bertozzi, Fang-Fang Yin, and Chunhao Wang. “Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.” Front Oncol 12 (2022): 895544. https://doi.org/10.3389/fonc.2022.895544.Full Text Link to Item
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Rigiroli, Francesca, Jocelyn Hoye, Reginald Lerebours, Kyle J. Lafata, Cai Li, Mathias Meyer, Peijie Lyu, et al. “CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study.” Radiology 301, no. 3 (December 2021): 610–22. https://doi.org/10.1148/radiol.2021210699.Full Text Open Access Copy Link to Item
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Li, Xiang, Richard C. Davis, Yuemei Xu, Zehan Wang, Nao Souma, Gina Sotolongo, Jonathan Bell, et al. “Deep learning segmentation of glomeruli on kidney donor frozen sections.” J Med Imaging (Bellingham) 8, no. 6 (November 2021): 067501. https://doi.org/10.1117/1.JMI.8.6.067501.Full Text Link to Item
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Jiang, Hanyu, Hubert C. Chen, Kyle J. Lafata, and Mustafa R. Bashir. “Week 4 Liver Fat Reduction on MRI as an Early Predictor of Treatment Response in Participants with Nonalcoholic Steatohepatitis.” Radiology 300, no. 2 (August 2021): 361–68. https://doi.org/10.1148/radiol.2021204325.Full Text Link to Item
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Lafata, Kyle J., Yushi Chang, Chunhao Wang, Yvonne M. Mowery, Irina Vergalasova, Donna Niedzwiecki, David S. Yoo, Jian-Guo Liu, David M. Brizel, and Fang-Fang Yin. “Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers.” Med Phys 48, no. 7 (July 2021): 3767–77. https://doi.org/10.1002/mp.14926.Full Text Link to Item
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Chang, Yushi, Zhuoran Jiang, William Paul Segars, Zeyu Zhang, Kyle Lafata, Jing Cai, Fang-Fang Yin, and Lei Ren. “A generative adversarial network (GAN)-based technique for synthesizing realistic respiratory motion in the extended cardiac-torso (XCAT) phantoms.” Phys Med Biol 66, no. 11 (May 31, 2021). https://doi.org/10.1088/1361-6560/ac01b4.Full Text Link to Item
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Lafata, Kyle J., Michael N. Corradetti, Junheng Gao, Corbin D. Jacobs, Jingxi Weng, Yushi Chang, Chunhao Wang, et al. “Radiogenomic Analysis of Locally Advanced Lung Cancer Based on CT Imaging and Intratreatment Changes in Cell-Free DNA.” Radiol Imaging Cancer 3, no. 4 (April 2021): e200157. https://doi.org/10.1148/rycan.2021200157.Full Text Link to Item
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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.Full Text Link to Item
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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.” Quant Imaging Med Surg 10, no. 10 (October 2020): 1917–29. https://doi.org/10.21037/qims-19-883.Full Text Link to Item
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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.Full Text Link to Item
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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.Full Text Link to Item
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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.” Front Oncol 10 (2020): 1592. https://doi.org/10.3389/fonc.2020.01592.Full Text Open Access Copy Link to Item
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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.Full Text Open Access Copy Link to Item
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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.Full Text Open Access Copy Link to Item
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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.Full Text Open Access Copy Link to Item
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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 (2019): 748–52. https://doi.org/10.1016/j.adro.2019.05.004.Full Text Link to Item
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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.Full Text Open Access Copy
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Lafata, Kyle, Jing Cai, Chunhao Wang, Julian Hong, Chris R. Kelsey, and Fang-Fang Yin. “Spatial-temporal variability of radiomic features and its effect on the classification of lung cancer histology.” Phys Med Biol 63, no. 22 (November 8, 2018): 225003. https://doi.org/10.1088/1361-6560/aae56a.Full Text Open Access Copy Link to Item
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Ackerson, Bradley G., Betty C. Tong, Julian C. Hong, Lin Gu, Junzo Chino, Jacob W. Trotter, Thomas A. D’Amico, et al. “Stereotactic body radiation therapy versus sublobar resection for stage I NSCLC.” Lung Cancer 125 (November 2018): 185–91. https://doi.org/10.1016/j.lungcan.2018.09.020.Full Text Link to Item
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Yin, F. F., K. Lafata, J. C. Hong, and C. R. Kelsey. “Effects of Motion on Radiomics Analysis of Thoracic Cancers.” Int J Radiat Oncol Biol Phys 98, no. 1 (May 1, 2017): 250. https://doi.org/10.1016/j.ijrobp.2017.01.246.Full Text Link to Item
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Lafata, Kyle J., Harry Bushe, and Jesse N. Aronowitz. “A simple technique for the generation of institution-specific nomograms for permanent prostate cancer brachytherapy.” J Contemp Brachytherapy 6, no. 3 (October 2014): 293–96. https://doi.org/10.5114/jcb.2014.45582.Full Text Link to Item
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Stauder, Michael C., O Kenneth Macdonald, Kenneth R. Olivier, Jason A. Call, Kyle Lafata, Charles S. Mayo, Robert C. Miller, Paul D. Brown, Heather J. Bauer, and Yolanda I. Garces. “Early pulmonary toxicity following lung stereotactic body radiation therapy delivered in consecutive daily fractions.” Radiother Oncol 99, no. 2 (May 2011): 166–71. https://doi.org/10.1016/j.radonc.2011.04.002.Full Text Link to Item
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Conference Papers
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Yang, Zhenyu, Zongsheng Hu, Hangjie Ji, Kyle Lafata, Eugene Vaios, Scott Floyd, Fang-Fang Yin, and Chunhao Wang. “A neural ordinary differential equation model for visualizing deep neural network behaviors in multi-parametric MRI-based glioma segmentation.” In Med Phys, 2023. https://doi.org/10.1002/mp.16286.Full Text Link to Item
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Ray, A., X. Li, L. Barisoni, K. Chakrabarty, and K. Lafata. “Decoding the Encoder.” In Conference Proceedings Ieee Southeastcon, 2023-April:179–86, 2023. https://doi.org/10.1109/SoutheastCon51012.2023.10115100.Full Text
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Hu, Zongsheng, Zhenyu Yang, Kyle J. Lafata, Fang-Fang Yin, and Chunhao Wang. “A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images.” In Med Phys, 49:3213–22, 2022. https://doi.org/10.1002/mp.15582.Full Text Link to Item
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Allphin, A. J., Y. Mowery, K. J. Lafata, D. P. Clark, A. Basil, R. Castillo, M. D. Holbrook, K. B. Ghaghada, and C. T. Badea. “Spectral micro-CT and nanoradiomic analysis for classification of tumors based on lymphocytic burden in cancer therapy studies.” In Progress in Biomedical Optics and Imaging Proceedings of Spie, Vol. 12036, 2022. https://doi.org/10.1117/12.2611519.Full Text
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Song, H., J. Milligan, K. Lafata, G. Kelly, A. Chilkoti, J. Cai, and F. Yin. “Measuring Distribution of An I-125 Labeled Elastin-Like Polypeptide (ELP) Nanoparticle Within Mice Tumors for Consideration as a Novel Technique of Delivering Brachytherapy.” In Medical Physics, Vol. 48, 2021.Link to Item
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Sotolongo, Gina, Jihyeon Je, Jarcy Zee, Yijiang Chen, Xiang Li, Yuqi Wang, Jeffrey Hodgin, et al. “Cortical Tubulointerstitial Mononuclear Inflammation in Renal Biopsies is a Quantitative Biomarker of Clinical Outcomes in NEPTUNE Glomerular Diseases.” In Modern Pathology, 34:1018–19, 2021.Link to Item
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Sotolongo, Gina, Jihyeon Je, Jarcy Zee, Yijiang Chen, Xiang Li, Yuqi Wang, Jeffrey Hodgin, et al. “Cortical Tubulointerstitial Mononuclear Inflammation in Renal Biopsies is a Quantitative Biomarker of Clinical Outcomes in NEPTUNE Glomerular Diseases.” In Laboratory Investigation, 101:1018–19, 2021.Link to Item
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Toronka, A., M. Defreitas, B. Konkel, M. Nedrud, I. Zaki, A. Valentine, S. Cubberley, F. Yin, M. Bashir, and K. Lafata. “Multi-Domain Statistical Modeling of Treatment Tolerance in Patients with Gastric and Esophageal Adenocarcinoma.” In Medical Physics, Vol. 48, 2021.Link to Item
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Wang, C., H. Ji, A. Bertozzi, D. Brizel, Y. Mowery, F. Yin, and K. Lafata. “Biologically Guided Deep Learning for Post-Radiation PET Image Outcome Prediction: A Feasibility Study of Oropharyngeal Cancer Application.” In Medical Physics, Vol. 48, 2021.Link to Item
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Wang, Y., X. Li, M. Konanur, B. Konkel, E. Seyferth, N. Brajer, M. Bashir, and K. Lafata. “Computer-Assisted Diagnosis of Hepatic Portal Hypertension: A Novel, Attention-Guided Deep Learning Framework Based On CT Imaging and Laboratory Data Integration.” In Medical Physics, Vol. 48, 2021.Link to Item
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Davis, Richard, Kyle Lafata, Xiang Li, Nao Souma, David Howell, Xiling Shen, and Laura Barisoni. “A Deep Learning Approach to Analysis of Kidney Transplant Frozen Sections.” In Laboratory Investigation, 100:1573–1573. NATURE PUBLISHING GROUP, 2020.Link to Item
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Davis, Richard, Kyle Lafata, Xiang Li, Nao Souma, David Howell, Xiling Shen, and Laura Barisoni. “A Deep Learning Approach to Analysis of Kidney Transplant Frozen Sections.” In Modern Pathology, 33:1573–1573. NATURE PUBLISHING GROUP, 2020.Link to Item
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Chang, Y., K. Lafata, P. Segars, F. Yin, and L. Ren. “Development of Realistic Multi-Contrast Textured XCAT (MT-XCAT) Phantoms Using a Dual-Discriminator Conditional-Generative Adversarial Network (D-CGAN).” In Medical Physics, 47:E269–70, 2020.Link to Item
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Chang, Y., Z. Jiang, K. Lafata, Z. Zhang, P. Segars, J. Cai, F. Yin, and L. Ren. “A Generative Adversarial Network (GAN)-Based Technique for Synthesizing Realistic Respiratory Motion in the Extended Cardiac-Torso (XCAT) Phantoms.” In Medical Physics, 47:E286–E286, 2020.Link to Item
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Chen, X., K. Lafata, Z. Yang, and F. Yin. “Quantification of Lung Ventilation Using Voxel-Based Delta Radiomics Extracted from Thoracic 4DCT.” In Medical Physics, 47:E435–E435, 2020.Link to Item
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Lafata, K., Y. Chang, C. Wang, Y. Mowery, I. Vergalasova, J. Liu, D. Brizel, and F. Yin. “Unsupervised Machine Learning of Metabolic Response from Radiomic Expression of Oropharyngeal Cancers.” In Medical Physics, 47:E266–E266, 2020.Link to Item
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Li, X., J. Zhang, Y. Sheng, K. Lafata, N. Eclov, Y. Cui, W. Giles, et al. “A Machine Learning Model for Brain V12 Gy/V60% Prediction of LINAC-Based Single-Iso-Multiple-Targets (SIMT) Stereotactic Radiosurgery (SRS): A Longitudinal Study.” In Medical Physics, 47:E568–69, 2020.Link to Item
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Song, H., J. Milligan, K. Lafata, G. Kelly, A. Chilkoti, J. Cai, and F. Yin. “Determining Diffusion of Radioactive Activity Within Mice Tumor Model From a Novel Elastin-Like Polypeptide (ELP) Brachytherapy Source.” In Medical Physics, 47:E740–E740, 2020.Link to Item
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Wang, C., X. Li, Y. Sheng, J. Zhang, K. Lafata, F. Yin, Q. Wu, and Y. Ge. “A Lightweight Deep-Learning Model for Automatic IMRT Planning Via Fluence Map Prediction with a 2.5D Implementation: A Study of Head-And-Neck IMRT Application.” In Medical Physics, 47:E330–E330, 2020.Link to Item
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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.” In Frontiers in Oncology, Vol. 10, 2020.
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Yang, Z., K. Lafata, X. Chen, Y. Chang, and F. Yin. “Association of Lung CT Voxel-Based Radiomics Feature Map with Galligas PET Lung Ventilation Imaging.” In Medical Physics, 47:E409–E409, 2020.Link to Item
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Chang, Y., C. Liu, K. Lafata, C. Wang, Y. Cui, L. Ren, Y. M. Mowery, D. M. Brizel, and F. F. Yin. “PET Radiotherapy Response Assessment Using Encoder-Decoder Convolutional Neural Network and Pre-treatment Information: A Feasibility of Oropharynx Cancer IMRT.” In International Journal of Radiation Oncology*Biology*Physics, 105:E413–E413. Elsevier BV, 2019. https://doi.org/10.1016/j.ijrobp.2019.06.1615.Full Text
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Lafata, K., J. Gao, C. D. Jacobs, Y. Chang, X. Wang, C. R. Kelsey, F. F. Yin, and M. N. Corradetti. “Identification of Radiomic Biomarkers for Patients with Locally Advanced Lung Cancer.” In International Journal of Radiation Oncology*Biology*Physics, 105:E515–E515. Elsevier BV, 2019. https://doi.org/10.1016/j.ijrobp.2019.06.1350.Full Text
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Chang, Y., K. Lafata, C. Liu, C. Wang, Y. Cui, L. Ren, X. Li, Y. Mowery, D. Brizel, and F. Yin. “An Encoder-Decoder Based Convolutional Neural Network (ED-CNN) for PET Image Response Prediction Using Pre-RT Information: A Feasibility of Oropharynx Cancer IMRT.” In Medical Physics, 46:E283–E283. WILEY, 2019.Link to Item
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Lafata, K., Y. Chang, C. Wang, Y. Mowery, D. Brizel, and F. Yin. “Intra-Treatment 18F-FDG PET/CT Radiomic Signature Predicts InField Recurrence Following Definitive Chemo-Radiation Therapy for Oropharyngeal Cancer.” In Medical Physics, 46:E405–E405. WILEY, 2019.Link to Item
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Lafata, K., Y. Gao, Y. Chang, C. Wang, C. Kelsey, and F. Yin. “Intratumoral and Peritumoral CT Radiomic Modeling to Predict Treatment Failure of Early Stage Non-Small Cell Lung Cancers.” In Medical Physics, 46:E295–E295. WILEY, 2019.Link to Item
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Liu, C., C. Wang, K. Lafata, Y. Chang, Y. Cui, and F. Yin. “Dose-Specific PET Image-Based Outcome Prediction: A Deep Learning Study for Oropharyngeal Cancer IMRT Application.” In Medical Physics, 46:E525–E525. WILEY, 2019.Link to Item
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Chen, Y., K. Lafata, F. Yin, and L. Ren. “Daily Edge Deformation Prediction Using Artificial Neural Network Regression for Prior Contour Based Total Variation Reconstruction (PCTV-ANN) for LOW Dose CBCT.” In Medical Physics, 45:E415–E415. WILEY, 2018.Link to Item
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Lafata, K., J. Cai, J. Liu, K. Sidhu, and F. Yin. “Deep Learning of Pulmonary Function in CT Images Based On Radiomic Filtering.” In Medical Physics, 45:E158–E158. WILEY, 2018.Link to Item
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Lafata, K., R. Geng, B. Ackerson, C. Kelsey, J. Torok, and F. Yin. “Predicting Lung SBRT Clinical Outcomes Using Planning-CT Radiomics.” In Medical Physics, 45:E556–E556. WILEY, 2018.Link to Item
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Pegues, H., K. Lafata, K. Sidhu, and F. Yin. “A Radiomics Approach to Evaluate Changes in Pulmonary Vasculature Following Radiation Therapy for Thoracic Cancers: Initial Development and Pilot Study.” In Medical Physics, 45:E410–E410. WILEY, 2018.Link to Item
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Geng, R., K. Lafata, and F. Yin. “Effect of Lung SBRT Fractionation On Feature Variability of Longitudinal Cone-Beam CT Radiomics.” In Medical Physics, 45:E411–E411, 2018.Link to Item
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Lafata, K., J. Cai, C. Wang, J. C. Hong, C. Kelsey, and F. F. Yin. “Sensitivity of Radiomic Features to Acquisition Noise and Respiratory Motion.” In International Journal of Radiation Oncology*Biology*Physics, 99:S93–94. Elsevier BV, 2017. https://doi.org/10.1016/j.ijrobp.2017.06.226.Full Text
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Geng, R., K. Lafata, Y. Zhang, and F. Yin. “Harmonization of Radiomic Features On Planning CT and On-Board CBCT.” In Medical Physics, 44:3287–3287. WILEY, 2017.Link to Item
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Lafata, K., J. Cai, C. Kelsey, and F. Yin. “A Radiomics Approach for Hyper-Dimensional Lung Function Mapping.” In Medical Physics, 44:3287–3287. WILEY, 2017.Link to Item
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Lafata, K., J. Cai, C. Wang, J. Hong, C. Kelsey, and F. Yin. “Sensitivity of Radiomic Features to Image Noise and Respiratory Motion.” In Medical Physics, Vol. 44. WILEY, 2017.Link to Item
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Lafata, K., J. Cai, L. Ren, Q. Wu, J. C. Hong, C. R. Kelsey, and F. F. Yin. “Development of a Machine Learning Methodology to Estimate Lung Stereotactic Body Radiation Therapy Dosimetric Endpoints Based on Patient-Specific Anatomic Features.” In International Journal of Radiation Oncology Biology Physics, 96:E420–21, 2016.Link to Item
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Lafata, K., J. Cai, L. Ren, Q. Wu, J. C. Hong, C. R. Kelsey, and F. F. Yin. “Development of a Machine Learning Methodology to Estimate Lung Stereotactic Body Radiation Therapy Dosimetric Endpoints Based on Patient-Specific Anatomic Features.” In Int J Radiat Oncol Biol Phys, 96:E420–21, 2016. https://doi.org/10.1016/j.ijrobp.2016.06.1687.Full Text Link to Item
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Lafata, K., L. Ren, J. Cai, and F. Yin. “SU-G-JeP3-01: A Method to Quantify Lung SBRT Target Localization Accuracy Based On Digitally Reconstructed Fluoroscopy.” In Med Phys, 43:3670, 2016. https://doi.org/10.1118/1.4957066.Full Text Link to Item
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Lafata, K., L. Ren, Q. Wu, C. Kelsey, J. Hong, J. Cai, and F. Yin. “SU-D-204-01: A Methodology Based On Machine Learning and Quantum Clustering to Predict Lung SBRT Dosimetric Endpoints From Patient Specific Anatomic Features.” In Med Phys, 43:3332, 2016. https://doi.org/10.1118/1.4955606.Full Text Link to Item
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Lambson, K., K. Lafata, J. Schaal, D. Miles, S. Yoon, W. Liu, M. Oldham, and J. Cai. “SU-F-T-10: Validation of ELP Dosimetry Using PRESAGE Dosimeter: Feasibility Test and Practical Considerations.” In Med Phys, 43:3463, 2016. https://doi.org/10.1118/1.4956144.Full Text Link to Item
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Lafata, K., J. Schaal, W. Liu, and J. Cai. “MO-FG-BRA-01: Development of An Image-Guided Dosimetric Planning System for Injectable Brachytherapy Using ELP Nanoparticles.” In Med Phys, 42:3564, 2015. https://doi.org/10.1118/1.4925405.Full Text Link to Item
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Lafata, K., B. Czito, M. Palta, M. Bashir, F. Yin, and J. Cai. “Verification of 4D-MRI Internal Target Volume Using Cine MRI.” In Medical Physics, 41:201–201. WILEY, 2014.Link to Item
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Lafata, K., B. Czito, M. Palta, M. Bashir, F. Yin, and J. Cai. “SU-E-J-192: Verification of 4D-MRI Internal Target Volume Using Cine MRI.” In Med Phys, 41:201, 2014. https://doi.org/10.1118/1.4888245.Full Text Link to Item
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Preprints
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Hu, Zongsheng, Zhenyu Yang, Haozhao Zhang, Eugene Vaios, Kyle Lafata, Fang-Fang Yin, and Chunhao Wang. “A Deep Learning Model with Radiomics Analysis Integration for Glioblastoma Post-Resection Survival Prediction,” March 11, 2022.Link to Item
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- Teaching & Mentoring
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Recent Courses
- ECE 392: Projects in Electrical and Computer Engineering 2023
- ECE 494: Projects in Electrical and Computer Engineering 2023
- ECE 891: Internship 2023
- MEDPHY 507: Radiation Biology 2023
- MEDPHY 791: Independent Study in Medical Physics 2023
- ECE 392: Projects in Electrical and Computer Engineering 2022
- ECE 891: Internship 2022
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