Kyle Jon Lafata

Education

• C. Duke University, 2018
• Ph.D. Duke University, 2018

Trainings & Certifications

• Postdoctoral Associate, RADIATION ONCOLOGY/RADIATION PHYSICS DIVISION (2018 - 2020) Duke University, School of Medicine

Positions

• Thaddeus V. Samulski Associate Professor of Radiation Oncology
• Associate Professor of Radiation Oncology
• Associate Professor in Radiology
• Associate Professor in the Department of Electrical and Computer Engineering
• Assistant Professor of Pathology
• Member of the Duke Cancer Institute

Courses Taught

• MEDPHY 791: Independent Study in Medical Physics
• MEDPHY 507: Radiation Biology
• EGR 393: Research Projects in Engineering
• ECE 899: Special Readings in Electrical Engineering
• ECE 891: Internship
• ECE 494: Projects in Electrical and Computer Engineering
• ECE 493: Projects in Electrical and Computer Engineering
• ECE 392: Projects in Electrical and Computer Engineering

Publications

• Wang Y, Gupta A, Tushar FI, Riley B, Wang A, Tailor TD, et al. Concordance-based Predictive Uncertainty (CPU)-Index: Proof-of-concept with application towards improved specificity of lung cancers on low dose screening CT. Artif Intell Med. 2025 Feb;160:103055.
• Zhao J, Vaios E, Yang Z, Lu K, Floyd S, Yang D, et al. Radiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis. In: Med Phys. 2025.
• Wang L, Yang Z, LaBella D, Reitman Z, Ginn J, Zhao J, et al. Uncertainty quantification in multi-parametric MRI-based meningioma radiotherapy target segmentation. Front Oncol. 2025;15:1474590.
• Zhou J, Luo Y, Darcy JW, Lafata KJ, Ruiz JR, Grego S. Long-term, automated stool monitoring using a novel smart toilet: A feasibility study. Neurogastroenterol Motil. 2025 Jan;37(1):e14954.
• Chen Y, Wang B, Demeke D, Fan F, Berthier C, Mariani L, et al. Clinical Relevance of Computational Pathology Analysis of Interplay Between Kidney Microvasculature and Interstitial Microenvironment. Clin J Am Soc Nephrol. 2024 Dec 23;20(2):239u201355.
• Zhao J, Vaios E, Wang Y, Yang Z, Cui Y, Reitman ZJ, et al. Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction. Int J Radiat Oncol Biol Phys. 2024 Oct 1;120(2):603u201313.
• Domanski P, Ray A, Lafata K, Firouzi F, Chakrabarty K, Pflu00fcger D. Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics. Biocybernetics and Biomedical Engineering. 2024 Jul 1;44(3):481u2013500.
• Lafata KJ, Read C, Tong BC, Akinyemiju T, Wang C, Cerullo M, et al. Lung Cancer Screening in Clinical Practice: A 5-Year Review of Frequency and Predictors of Lung Cancer in the Screened Population. J Am Coll Radiol. 2024 May;21(5):767u201377.
• Stevens JB, Riley BA, Je J, Gao Y, Wang C, Mowery YM, et al. Radiomics on spatial-temporal manifolds via Fokker-Planck dynamics. In: Med Phys. 2024. p. 3334u201347.
• Riley BA, Stevens JB, Li X, Yang Z, Wang C, Mowery YM, et al. Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma. J Med Imaging (Bellingham). 2024 Mar;11(2):024007.
• Yang Z, Lafata K, Vaios E, Hu Z, Mullikin T, Yin F-F, et al. Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection. Med Phys. 2024 Mar;51(3):1931u201343.
• Ray A, Dannull L, Firouzi F, Lafata K, Chakrabarty K. Inference Serving System for Stable Diffusion as a Service. In: Proceeding - 2024 IEEE Cloud Summit, Cloud Summit 2024. 2024. p. 13u20136.
• Li X, Heirman CC, Rickard AG, Sotolongo G, Castillo R, Adanlawo T, et al. Computational staining of CD3/CD20 positive lymphocytes in human tissues with experimental confirmation in a genetically engineered mouse model. Front Immunol. 2024;15:1451261.
• Tushar FI, Vancoillie L, McCabe C, Kavuri A, Dahal L, Harrawood B, et al. Virtual NLST: Towards Replicating National Lung Screening Trial. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2024.
• Kang J, Lafata K, Kim E, Yao C, Lin F, Rattay T, et al. Artificial intelligence across oncology specialties: current applications and emerging tools. BMJ Oncol. 2024;3(1):e000134.
• Liardo A, Ray A, Firouzi F, Lafata K, Chakrabarty K. Neural Architecture Search for Blood Glucose Prediction in Type-1 Diabetics. In: 2024 IEEE 20th International Conference on Body Sensor Networks, BSN 2024 - Proceedings. 2024.
• Kreiss L, Jiang S, Li X, Xu S, Zhou KC, Lee KC, et al. Digital staining in optical microscopy using deep learningu00a0- a review. PhotoniX. 2023 Dec 1;4(1).
• Kelleher CB, Macdonald J, Jaffe TA, Allen BC, Kalisz KR, Kauffman TH, 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. 2023 Aug;58(2):620u20139.
• Rigiroli F, Hoye J, Lerebours R, Lyu P, Lafata KJ, Zhang AR, et al. Exploratory analysis of mesenteric-portal axis CT radiomic features for survival prediction of patients with pancreatic ductal adenocarcinoma. Eur Radiol. 2023 Aug;33(8):5779u201391.
• Yang Z, Hu Z, Ji H, Lafata K, Vaios E, Floyd S, et al. A neural ordinary differential equation model for visualizing deep neural network behaviors in multi-parametric MRI-based glioma segmentation. In: Med Phys. 2023. p. 4825u201338.
• Wang Y, Li X, Konanur M, Konkel B, Seyferth E, Brajer N, et al. Towards optimal deep fusion of imaging and clinical data via a model-based description of fusion quality. Med Phys. 2023 Jun;50(6):3526u201337.
• Chen Y, Zee J, Janowczyk AR, Rubin J, Toro P, Lafata KJ, et al. Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies. Kidney360. 2023 May 1;4(5):648u201358.
• Konkel B, Macdonald J, Lafata K, Zaki IH, Bozdogan E, Chaudhry M, et al. Systematic Analysis of Common Factors Impacting Deep Learning Model Generalizability in Liver Segmentation. Radiol Artif Intell. 2023 May;5(3):e220080.
• Ray A, Li X, Barisoni L, Chakrabarty K, Lafata K. Decoding the Encoder. In: Conference Proceedings - IEEE SOUTHEASTCON. 2023. p. 179u201386.
• Dahal L, Wang Y, Tushar FI, Montero I, Lafata K, Abadi E, et al. Automatic quality control in computed tomography volumes segmentation using a small set of XCAT as reference images. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2023.
• Ray A, Lafata K, Zhang Z, Xiong Y, Chakrabarty K. Job Recommendation Service for GPU Sharing in Kubernetes. In: Proceedings - 2023 IEEE Cloud Summit, Cloud Summit 2023. 2023. p. 7u201314.
• Yang Z, Wang C, Wang Y, Lafata KJ, Zhang H, Ackerson BG, et al. Development of a multi-feature-combined model: proof-of-concept with application to local failure prediction of post-SBRT or surgery early-stage NSCLC patients. Front Oncol. 2023;13:1185771.
• Ray A, Lafata K, Zhang Z, Xiong Y, Chakrabarty K. Privacy-preserving Job Scheduler for GPU Sharing. In: Proceedings - 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing Workshops, CCGridW 2023. 2023. p. 337u20139.
• Kierans AS, Lafata KJ, Ludwig DR, Burke LMB, Chernyak V, Fowler KJ, et al. Comparing Survival Outcomes of Patients With LI-RADS-M Hepatocellular Carcinomas and Intrahepatic Cholangiocarcinomas. J Magn Reson Imaging. 2023 Jan;57(1):308u201317.
• Domanski P, Ray A, Firouzi F, Lafata K, Chakrabarty K, Pfluger D. Blood Glucose Prediction for Type-1 Diabetics using Deep Reinforcement Learning. In: Proceedings - 2023 IEEE International Conference on Digital Health, ICDH 2023. 2023. p. 339u201347.
• DeFreitas MR, Toronka A, Nedrud MA, Cubberley S, Zaki IH, Konkel B, et al. CT-derived body composition measurements as predictors for neoadjuvant treatment tolerance and survival in gastroesophageal adenocarcinoma. Abdom Radiol (NY). 2023 Jan;48(1):211u20139.
• Yang Z, Lafata KJ, Chen X, Bowsher J, Chang Y, Wang C, et al. Quantification of lung function on CT images based on pulmonary radiomic filtering. Med Phys. 2022 Nov;49(11):7278u201386.
• Carpenter DJ, Natarajan B, Arshad M, Natesan D, Schultz O, Moravan MJ, et al. Prognostic Model for Intracranial Progression after Stereotactic Radiosurgery: A Multicenter Validation Study. Cancers (Basel). 2022 Oct 22;14(21).
• Lafata KJ, Wang Y, Konkel B, Yin F-F, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY). 2022 Sep;47(9):2986u20133002.
• Jiang H, Song B, Qin Y, Konanur M, Wu Y, McInnes MDF, et al. Modifying LI-RADS on Gadoxetate Disodium-Enhanced MRI: A Secondary Analysis of a Prospective Observational Study. J Magn Reson Imaging. 2022 Aug;56(2):399u2013412.
• Ding Y, Meyer M, Lyu P, Rigiroli F, Ramirez-Giraldo JC, Lafata K, et al. 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. 2022 Jun;63(6):828u201338.
• Hu Z, Yang Z, Lafata KJ, Yin F-F, Wang C. A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images. In: Med Phys. 2022. p. 3213u201322.
• Hu Z, Yang Z, Zhang H, Vaios E, Lafata K, Yin F-F, et al. A Deep Learning Model with Radiomics Analysis Integration forn Glioblastoma Post-Resection Survival Prediction. 2022.
• Allphin AJ, Mowery YM, Lafata KJ, Clark DP, Bassil AM, Castillo R, et al. Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden. Tomography. 2022 Mar 10;8(2):740u201353.
• Jiang H, Song B, Qin Y, Wei Y, Konanur M, Wu Y, et al. Data-Driven Modification of the LI-RADS Major Feature System on Gadoxetate Disodium-Enhanced MRI: Toward Better Sensitivity and Simplicity. J Magn Reson Imaging. 2022 Feb;55(2):493u2013506.
• Glass C, Lafata KJ, Jeck W, Horstmeyer R, Cooke C, Everitt J, et al. The Role of Machine Learning in Cardiovascular Pathology. Can J Cardiol. 2022 Feb;38(2):234u201345.
• Allphin AJ, Mowery Y, Lafata KJ, Clark DP, Basil A, Castillo R, et al. 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. 2022.
• Ji H, Lafata K, Mowery Y, Brizel D, Bertozzi AL, Yin F-F, et al. Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application. Front Oncol. 2022;12:895544.
• Rigiroli F, Hoye J, Lerebours R, Lafata KJ, Li C, Meyer M, et al. CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study. Radiology. 2021 Dec;301(3):610u201322.
• Li X, Davis RC, Xu Y, Wang Z, Souma N, Sotolongo G, et al. Deep learning segmentation of glomeruli on kidney donor frozen sections. J Med Imaging (Bellingham). 2021 Nov;8(6):067501.
• Jiang H, Chen HC, Lafata KJ, Bashir MR. Week 4 Liver Fat Reduction on MRI as an Early Predictor of Treatment Response in Participants with Nonalcoholic Steatohepatitis. Radiology. 2021 Aug;300(2):361u20138.
• Lafata KJ, Chang Y, Wang C, Mowery YM, Vergalasova I, Niedzwiecki D, et al. Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers. Med Phys. 2021 Jul;48(7):3767u201377.
• Chang Y, Jiang Z, Segars WP, Zhang Z, Lafata K, Cai J, et al. A generative adversarial network (GAN)-based technique for synthesizing realistic respiratory motion in the extended cardiac-torso (XCAT) phantoms. Phys Med Biol. 2021 May 31;66(11).
• Lafata KJ, Corradetti MN, Gao J, Jacobs CD, Weng J, Chang Y, et al. Radiogenomic Analysis of Locally Advanced Lung Cancer Based on CT Imaging and Intratreatment Changes in Cell-Free DNA. Radiol Imaging Cancer. 2021 Apr;3(4):e200157.
• Wang Y, Li X, Konanur M, Konkel B, Seyferth E, Brajer N, et al. 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. 2021.
• Song H, Milligan J, Lafata K, Kelly G, Chilkoti A, Cai J, et al. 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. 2021.
• Toronka A, Defreitas M, Konkel B, Nedrud M, Zaki I, Valentine A, et al. Multi-Domain Statistical Modeling of Treatment Tolerance in Patients with Gastric and Esophageal Adenocarcinoma. In: MEDICAL PHYSICS. 2021.
• Wang C, Ji H, Bertozzi A, Brizel D, Mowery Y, Yin F, et al. Biologically Guided Deep Learning for Post-Radiation PET Image Outcome Prediction: A Feasibility Study of Oropharyngeal Cancer Application. In: MEDICAL PHYSICS. 2021.
• Sotolongo G, Je J, Zee J, Chen Y, Li X, Wang Y, et al. Cortical Tubulointerstitial Mononuclear Inflammation in Renal Biopsies is a Quantitative Biomarker of Clinical Outcomes in NEPTUNE Glomerular Diseases. In: LABORATORY INVESTIGATION. 2021. p. 1018u20139.
• Sotolongo G, Je J, Zee J, Chen Y, Li X, Wang Y, et al. Cortical Tubulointerstitial Mononuclear Inflammation in Renal Biopsies is a Quantitative Biomarker of Clinical Outcomes in NEPTUNE Glomerular Diseases. In: MODERN PATHOLOGY. 2021. p. 1018u20139.
• Barisoni L, Lafata KJ, Hewitt SM, Madabhushi A, Balis UGJ. Digital pathology and computational image analysis in nephropathology. Nat Rev Nephrol. 2020 Nov;16(11):669u201385.
• Liu C, Hu S-C, Wang C, Lafata K, Yin F-F. Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data. Quant Imaging Med Surg. 2020 Oct;10(10):1917u201329.
• Chang Y, Lafata K, Segars WP, Yin F-F, Ren L. Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN). Phys Med Biol. 2020 Mar 19;65(6):065009.
• Chang Y, Lafata K, Wang C, Duan X, Geng R, Yang Z, et al. Digital phantoms for characterizing inconsistencies among radiomics extraction toolboxes. Biomed Phys Eng Express. 2020 Mar 2;6(2):025016.
• Davis R, Lafata K, Li X, Souma N, Howell D, Shen X, et al. A Deep Learning Approach to Analysis of Kidney Transplant Frozen Sections. In: MODERN PATHOLOGY. NATURE PUBLISHING GROUP; 2020. p. 1573u20131573.
• Davis R, Lafata K, Li X, Souma N, Howell D, Shen X, et al. A Deep Learning Approach to Analysis of Kidney Transplant Frozen Sections. In: LABORATORY INVESTIGATION. NATURE PUBLISHING GROUP; 2020. p. 1573u20131573.
• Wang C, Liu C, Chang Y, Lafata K, Cui Y, Zhang J, et al. Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application. Front Oncol. 2020;10:1592.
• Chang Y, Lafata K, Segars P, Yin F, Ren L. Development of Realistic Multi-Contrast Textured XCAT (MT-XCAT) Phantoms Using a Dual-Discriminator Conditional-Generative Adversarial Network (D-CGAN). In: MEDICAL PHYSICS. 2020. p. E269u201370.
• Wang C, Liu C, Chang Y, Lafata K, Cui Y, Zhang J, et al. Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application. In: Frontiers in oncology. 2020.
• Lafata K, Chang Y, Wang C, Mowery Y, Vergalasova I, Liu J, et al. Unsupervised Machine Learning of Metabolic Response from Radiomic Expression of Oropharyngeal Cancers. In: MEDICAL PHYSICS. 2020. p. E266u2013E266.
• Chen X, Lafata K, Yang Z, Yin F. Quantification of Lung Ventilation Using Voxel-Based Delta Radiomics Extracted from Thoracic 4DCT. In: MEDICAL PHYSICS. 2020. p. E435u2013E435.
• Song H, Milligan J, Lafata K, Kelly G, Chilkoti A, Cai J, et al. Determining Diffusion of Radioactive Activity Within Mice Tumor Model From a Novel Elastin-Like Polypeptide (ELP) Brachytherapy Source. In: MEDICAL PHYSICS. 2020. p. E740u2013E740.
• Yang Z, Lafata K, Chen X, Chang Y, Yin F. Association of Lung CT Voxel-Based Radiomics Feature Map with Galligas PET Lung Ventilation Imaging. In: MEDICAL PHYSICS. 2020. p. E409u2013E409.
• Li X, Zhang J, Sheng Y, Lafata K, Eclov N, Cui Y, 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. 2020. p. E568u20139.
• Wang C, Li X, Sheng Y, Zhang J, Lafata K, Yin F, et al. 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. 2020. p. E330u2013E330.
• Chang Y, Jiang Z, Lafata K, Zhang Z, Segars P, Cai J, et al. A Generative Adversarial Network (GAN)-Based Technique for Synthesizing Realistic Respiratory Motion in the Extended Cardiac-Torso (XCAT) Phantoms. In: MEDICAL PHYSICS. 2020. p. E286u2013E286.
• Chang Y, Liu C, Lafata K, Wang C, Cui Y, Ren L, et al. 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. Elsevier BV; 2019. p. E413u2013E413.
• Lafata K, Gao J, Jacobs CD, Chang Y, Wang X, Kelsey CR, et al. Identification of Radiomic Biomarkers for Patients with Locally Advanced Lung Cancer. In: International Journal of Radiation Oncology*Biology*Physics. Elsevier BV; 2019. p. E515u2013E515.
• Lafata KJ, Zhou Z, Liu J-G, Hong J, Kelsey CR, Yin F-F. An Exploratory Radiomics Approach to Quantifying Pulmonary Function in CT Images. Sci Rep. 2019 Aug 8;9(1):11509.
• Lafata K, Chang Y, Wang C, Mowery Y, Brizel D, Yin F. Intra-Treatment 18F-FDG PET/CT Radiomic Signature Predicts InField Recurrence Following Definitive Chemo-Radiation Therapy for Oropharyngeal Cancer. In: MEDICAL PHYSICS. WILEY; 2019. p. E405u2013E405.
• Liu C, Wang C, Lafata K, Chang Y, Cui Y, Yin F. Dose-Specific PET Image-Based Outcome Prediction: A Deep Learning Study for Oropharyngeal Cancer IMRT Application. In: MEDICAL PHYSICS. WILEY; 2019. p. E525u2013E525.
• Chang Y, Lafata K, Liu C, Wang C, Cui Y, Ren L, et al. 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. WILEY; 2019. p. E283u2013E283.
• Lafata K, Gao Y, Chang Y, Wang C, Kelsey C, Yin F. Intratumoral and Peritumoral CT Radiomic Modeling to Predict Treatment Failure of Early Stage Non-Small Cell Lung Cancers. In: MEDICAL PHYSICS. WILEY; 2019. p. E295u2013E295.
• Lafata KJ, Hong JC, Geng R, Ackerson BG, Liu J-G, Zhou Z, et al. Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy. Phys Med Biol. 2019 Jan 8;64(2):025007.
• Chang Y, Lafata K, Sun W, Wang C, Chang Z, Kirkpatrick JP, et al. An investigation of machine learning methods in delta-radiomics feature analysis. PLoS One. 2019;14(12):e0226348.
• Lafata K, Zhou Z, Liu JG, Yin FF. Data clustering based on Langevin annealing with a self-consistent potential. Quarterly of Applied Mathematics. 2019 Jan 1;77(3):591u2013613.
• Corradetti MN, Torok JA, Hatch AJ, Xanthopoulos EP, Lafata K, Jacobs C, et al. Dynamic Changes in Circulating Tumor DNA During Chemoradiation for Locally Advanced Lung Cancer. Adv Radiat Oncol. 2019;4(4):748u201352.
• Lafata K, Cai J, Wang C, Hong J, Kelsey CR, Yin F-F. Spatial-temporal variability of radiomic features and its effect on the classification of lung cancer histology. Phys Med Biol. 2018 Nov 8;63(22):225003.
• Ackerson BG, Tong BC, Hong JC, Gu L, Chino J, Trotter JW, et al. Stereotactic body radiation therapy versus sublobar resection for stage I NSCLC. Lung Cancer. 2018 Nov;125:185u201391.
• Chen Y, Lafata K, Yin F, Ren L. 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. WILEY; 2018. p. E415u2013E415.
• Pegues H, Lafata K, Sidhu K, Yin F. A Radiomics Approach to Evaluate Changes in Pulmonary Vasculature Following Radiation Therapy for Thoracic Cancers: Initial Development and Pilot Study. In: MEDICAL PHYSICS. WILEY; 2018. p. E410u2013E410.
• Lafata K, Geng R, Ackerson B, Kelsey C, Torok J, Yin F. Predicting Lung SBRT Clinical Outcomes Using Planning-CT Radiomics. In: MEDICAL PHYSICS. WILEY; 2018. p. E556u2013E556.
• Lafata K, Cai J, Liu J, Sidhu K, Yin F. Deep Learning of Pulmonary Function in CT Images Based On Radiomic Filtering. In: MEDICAL PHYSICS. WILEY; 2018. p. E158u2013E158.
• Geng R, Lafata K, Yin F. Effect of Lung SBRT Fractionation On Feature Variability of Longitudinal Cone-Beam CT Radiomics. In: MEDICAL PHYSICS. 2018. p. E411u2013E411.
• Lafata K, Cai J, Wang C, Hong JC, Kelsey C, Yin FF. Sensitivity of Radiomic Features to Acquisition Noise and Respiratory Motion. In: International Journal of Radiation Oncology*Biology*Physics. Elsevier BV; 2017. p. S93u20134.
• Lafata K, Cai J, Wang C, Hong J, Kelsey C, Yin F. Sensitivity of Radiomic Features to Image Noise and Respiratory Motion. In: MEDICAL PHYSICS. WILEY; 2017.
• Geng R, Lafata K, Zhang Y, Yin F. Harmonization of Radiomic Features On Planning CT and On-Board CBCT. In: MEDICAL PHYSICS. WILEY; 2017. p. 3287u20133287.
• Lafata K, Cai J, Kelsey C, Yin F. A Radiomics Approach for Hyper-Dimensional Lung Function Mapping. In: MEDICAL PHYSICS. WILEY; 2017. p. 3287u20133287.
• Yin FF, Lafata K, Hong JC, Kelsey CR. Effects of Motion on Radiomics Analysis of Thoracic Cancers. Int J Radiat Oncol Biol Phys. 2017 May 1;98(1):250.
• Lafata K, Cai J, Ren L, Wu Q, Hong JC, Kelsey CR, et al. 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. 2016. p. E420u20131.
• Lafata K, Cai J, Ren L, Wu Q, Hong JC, Kelsey CR, et al. 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. 2016. p. E420u20131.
• Lambson K, Lafata K, Schaal J, Miles D, Yoon S, Liu W, et al. SU-F-T-10: Validation of ELP Dosimetry Using PRESAGE Dosimeter: Feasibility Test and Practical Considerations. In: Med Phys. 2016. p. 3463.
• Lafata K, Ren L, Wu Q, Kelsey C, Hong J, Cai J, et al. 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. 2016. p. 3332.
• Lafata K, Ren L, Cai J, Yin F. SU-G-JeP3-01: A Method to Quantify Lung SBRT Target Localization Accuracy Based On Digitally Reconstructed Fluoroscopy. In: Med Phys. 2016. p. 3670.
• Lafata K, Schaal J, Liu W, Cai J. MO-FG-BRA-01: Development of An Image-Guided Dosimetric Planning System for Injectable Brachytherapy Using ELP Nanoparticles. In: Med Phys. 2015. p. 3564.
• Lafata KJ, Bushe H, Aronowitz JN. A simple technique for the generation of institution-specific nomograms for permanent prostate cancer brachytherapy. J Contemp Brachytherapy. 2014 Oct;6(3):293u20136.
• Lafata K, Czito B, Palta M, Bashir M, Yin F, Cai J. SU-E-J-192: Verification of 4D-MRI Internal Target Volume Using Cine MRI. In: Med Phys. 2014. p. 201.
• Lafata K, Czito B, Palta M, Bashir M, Yin F, Cai J. Verification of 4D-MRI Internal Target Volume Using Cine MRI. In: MEDICAL PHYSICS. WILEY; 2014. p. 201u2013201.
• Stauder MC, Macdonald OK, Olivier KR, Call JA, Lafata K, Mayo CS, et al. Early pulmonary toxicity following lung stereotactic body radiation therapy delivered in consecutive daily fractions. Radiother Oncol. 2011 May;99(2):166u201371.