Publications of Kyle J. Lafata    :chronological  alphabetical  combined  bibtex listing:

Papers Published

1. Mouheb, K; Nejad, MG; Dahal, L; Samei, E; Lafata, KJ; Segars, WP; Lo, JY, Large Intestine 3D Shape Refinement Using Conditional Latent Point Diffusion Models, Lecture Notes in Computer Science, vol. 16171 LNCS (January, 2026), pp. 103-116 [doi]  [abs]
2. Xia, S-J; Vancoillie, L; Sotoudeh-Paima, S; Zarei, M; Ho, FC; Tushar, FI; Chen, X; Dahal, L; Lafata, KJ; Abadi, E; Lo, JY; Samei, E, Evaluation of unified harmonization of CT images across multiple tasks: A step towards AI generalizability., Med Phys, vol. 52 no. 11 (November, 2025), pp. e70084 [doi]  [abs]
3. Chisholm, M; Jabal, MS; He, H; Wang, Y; Kalisz, K; Lafata, KJ; Calabrese, E; Bashir, MR; Tailor, TD; Magudia, K, Investigating the Role of Area Deprivation Index in Observed Differences in CT-Based Body Composition by Race., J Am Coll Radiol, vol. 22 no. 10 (October, 2025), pp. 1182-1192 [doi]  [abs]
4. Stevens, JB; Je, J; Riley, BA; Mowery, YM; Brizel, DM; Liu, J-G; Wang, C; Lafata, KJ, Development and application of a novel tumor habitat analysis technique based on dynamical modeling., Med Phys, vol. 52 no. 9 (September, 2025), pp. e18032 [doi]  [abs]
5. Fan, F; Liu, Q; Zee, J; Ozeki, T; Demeke, D; Yang, Y; Bitzer, M; O'Connor, CL; Farris, AB; Wang, B; Shah, M; Jacobs, J; Mariani, L; Lafata, KJ; Rubin, J; Chen, Y; Holzman, LB; Hodgin, JB; Madabhushi, A; Barisoni, L; Janowczyk, A, Clinical relevance of computationally derived tubular features and their spatial relationships with the interstitial microenvironment in minimal change disease/focal segmental glomerulosclerosis., Kidney Int, vol. 108 no. 2 (August, 2025), pp. 293-309 [doi]  [abs]
6. Wang, AJ; Tushar, FI; Harowicz, MR; Tong, BC; Lafata, KJ; Tailor, TD; Lo, JY, The Duke Lung Cancer Screening (DLCS) Dataset: A Reference Dataset of Annotated Low-Dose Screening Thoracic CT., Radiol Artif Intell, vol. 7 no. 4 (July, 2025), pp. e240248 [doi]  [abs]
7. Dahal, L; Ghojoghnejad, M; Vancoillie, L; Ghosh, D; Bhandari, Y; Kim, D; Ho, FC; Tushar, FI; Luo, S; Lafata, KJ; Abadi, E; Samei, E; Lo, JY; Segars, WP, XCAT 3.0: A comprehensive library of personalized digital twins derived from CT scans., Med Image Anal, vol. 103 (July, 2025), pp. 103636 [doi]  [abs]
8. Tushar, FI; Vancoillie, L; McCabe, C; Kavuri, A; Dahal, L; Harrawood, B; Fryling, M; Zarei, M; Sotoudeh-Paima, S; Ho, FC; Ghosh, D; Harowicz, MR; Tailor, TD; Luo, S; Segars, WP; Abadi, E; Lafata, KJ; Lo, JY; Samei, E, Virtual Lung Screening Trial (VLST): An In Silico Study Inspired by the National Lung Screening Trial for Lung Cancer Detection., ArXiv (April, 2025)  [abs]
9. Zhao, J; Vaios, E; Yang, Z; Lu, K; Floyd, S; Yang, D; Ji, H; Reitman, ZJ; Lafata, KJ; Fecci, P; Kirkpatrick, JP; Wang, C, Radiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis., Med Phys, vol. 52 no. 4 (April, 2025), pp. 2661-2674 [doi]  [abs]
10. Wang, Y; Gupta, A; Tushar, FI; Riley, B; Wang, A; Tailor, TD; Tantum, S; Liu, J-G; Bashir, MR; Lo, JY; Lafata, KJ, 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, vol. 160 (February, 2025), pp. 103055 [doi]  [abs]
11. Xia, S-J; Vancoillie, L; Sotoudeh-Paima, S; Zarei, M; Ho, FC; Tushar, FI; Chen, X; Dahal, L; Lafata, KJ; Abadi, E; Lo, JY; Samei, E, The Role of Harmonization: A Systematic Analysis of Various Task-based Scenarios., Proc SPIE Int Soc Opt Eng, vol. 13405 (February, 2025) [doi]  [abs]
12. 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, vol. 37 no. 1 (January, 2025), pp. e14954 [doi]  [abs]
13. Wang, L; Yang, Z; LaBella, D; Reitman, Z; Ginn, J; Zhao, J; Adamson, J; Lafata, K; Calabrese, E; Kirkpatrick, J; Wang, C, Uncertainty quantification in multi-parametric MRI-based meningioma radiotherapy target segmentation., Front Oncol, vol. 15 (2025), pp. 1474590 [doi]  [abs]
14. Chen, Y; Wang, B; Demeke, D; Fan, F; Berthier, C; Mariani, L; Lafata, K; Holzman, L; Hodgin, J; Janowczyk, A; Barisoni, L; Madabhushi, A, Clinical Relevance of Computational Pathology Analysis of Interplay Between Kidney Microvasculature and Interstitial Microenvironment., Clin J Am Soc Nephrol, vol. 20 no. 2 (December, 2024), pp. 239-255 [doi]  [abs]
15. Zhao, J; Vaios, E; Wang, Y; Yang, Z; Cui, Y; Reitman, ZJ; Lafata, KJ; Fecci, P; Kirkpatrick, J; Fang Yin, F; Floyd, S; Wang, C, Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction., Int J Radiat Oncol Biol Phys, vol. 120 no. 2 (October, 2024), pp. 603-613 [doi]  [abs]
16. Domanski, P; Ray, A; Lafata, K; Firouzi, F; Chakrabarty, K; Pflüger, D, Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics, Biocybernetics and Biomedical Engineering, vol. 44 no. 3 (July, 2024), pp. 481-500 [doi]  [abs]
17. Lafata, KJ; Read, C; Tong, BC; Akinyemiju, T; Wang, C; Cerullo, M; Tailor, TD, 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, vol. 21 no. 5 (May, 2024), pp. 767-777 [doi]  [abs]
18. Stevens, JB; Riley, BA; Je, J; Gao, Y; Wang, C; Mowery, YM; Brizel, DM; Yin, F-F; Liu, J-G; Lafata, KJ, Radiomics on spatial-temporal manifolds via Fokker-Planck dynamics., Med Phys, vol. 51 no. 5 (May, 2024), pp. 3334-3347 [doi]  [abs]
19. Riley, BA; Stevens, JB; Li, X; Yang, Z; Wang, C; Mowery, YM; Brizel, DM; Yin, F-F; Lafata, KJ, Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma., J Med Imaging (Bellingham), vol. 11 no. 2 (March, 2024), pp. 024007 [doi]  [abs]
20. Yang, Z; Lafata, K; Vaios, E; Hu, Z; Mullikin, T; Yin, F-F; Wang, C, Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection., Med Phys, vol. 51 no. 3 (March, 2024), pp. 1931-1943 [doi]  [abs]
21. Ray, A; Firouzi, F; Lafata, K; Chakrabarty, K, Preserving Accuracy While Stealing Watermarked Deep Neural Networks, Proceedings 2024 International Conference on Machine Learning and Applications Icmla 2024 (January, 2024), pp. 1466-1473 [doi]  [abs]
22. Ray, A; Dannull, L; Firouzi, F; Lafata, K; Chakrabarty, K, Inference Serving System for Stable Diffusion as a Service, Proceeding 2024 IEEE Cloud Summit Cloud Summit 2024 (January, 2024), pp. 13-16 [doi]  [abs]
23. Liardo, A; Ray, A; Firouzi, F; Lafata, K; Chakrabarty, K, Neural Architecture Search for Blood Glucose Prediction in Type-1 Diabetics, 2024 IEEE 20th International Conference on Body Sensor Networks Bsn 2024 Proceedings (January, 2024) [doi]  [abs]
24. Tushar, FI; Vancoillie, L; McCabe, C; Kavuri, A; Dahal, L; Harrawood, B; Fryling, M; Zarei, M; Sotoudeh-Paima, S; Ho, FC; Ghosh, D; Luo, S; Segars, WP; Abadi, E; Lafata, KJ; Samei, E; Lo, JY, Virtual NLST: Towards Replicating National Lung Screening Trial, Progress in Biomedical Optics and Imaging Proceedings of SPIE, vol. 12925 (January, 2024) [doi]  [abs]
25. Kang, J; Lafata, K; Kim, E; Yao, C; Lin, F; Rattay, T; Nori, H; Katsoulakis, E; Lee, CI, Artificial intelligence across oncology specialties: current applications and emerging tools., BMJ Oncol, vol. 3 no. 1 (2024), pp. e000134 [doi]  [abs]
26. Li, X; Heirman, CC; Rickard, AG; Sotolongo, G; Castillo, R; Adanlawo, T; Everitt, JI; Hodgin, JB; Watts, TL; Janowczyk, A; Mowery, YM; Barisoni, L; Lafata, KJ, Computational staining of CD3/CD20 positive lymphocytes in human tissues with experimental confirmation in a genetically engineered mouse model., Front Immunol, vol. 15 (2024), pp. 1451261 [doi]  [abs]
27. Kreiss, L; Jiang, S; Li, X; Xu, S; Zhou, KC; Lee, KC; Mühlberg, A; Kim, K; Chaware, A; Ando, M; Barisoni, L; Lee, SA; Zheng, G; Lafata, KJ; Friedrich, O; Horstmeyer, R, Digital staining in optical microscopy using deep learning - a review, Photonix, vol. 4 no. 1 (December, 2023) [doi]  [abs]
28. Kelleher, CB; Macdonald, J; Jaffe, TA; Allen, BC; Kalisz, KR; Kauffman, TH; Smith, JD; Maurer, KR; Thomas, SP; Coleman, AD; Zaki, IH; Kannengiesser, S; Lafata, K; Gupta, RT; Bashir, MR, 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, vol. 58 no. 2 (August, 2023), pp. 620-629 [doi]  [abs]
29. Rigiroli, F; Hoye, J; Lerebours, R; Lyu, P; Lafata, KJ; Zhang, AR; Erkanli, A; Mettu, NB; Morgan, DE; Samei, E; Marin, D, Exploratory analysis of mesenteric-portal axis CT radiomic features for survival prediction of patients with pancreatic ductal adenocarcinoma., Eur Radiol, vol. 33 no. 8 (August, 2023), pp. 5779-5791 [doi]  [abs]
30. Yang, Z; Hu, Z; Ji, H; Lafata, K; Vaios, E; Floyd, S; Yin, F-F; Wang, C, A neural ordinary differential equation model for visualizing deep neural network behaviors in multi-parametric MRI-based glioma segmentation., Med Phys, vol. 50 no. 8 (August, 2023), pp. 4825-4838 [doi]  [abs]
31. Wang, Y; Li, X; Konanur, M; Konkel, B; Seyferth, E; Brajer, N; Liu, J-G; Bashir, MR; Lafata, KJ, Towards optimal deep fusion of imaging and clinical data via a model-based description of fusion quality., Med Phys, vol. 50 no. 6 (June, 2023), pp. 3526-3537 [doi]  [abs]
32. Chen, Y; Zee, J; Janowczyk, AR; Rubin, J; Toro, P; Lafata, KJ; Mariani, LH; Holzman, LB; Hodgin, JB; Madabhushi, A; Barisoni, L, Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies., Kidney360, vol. 4 no. 5 (May, 2023), pp. 648-658 [doi]  [abs]
33. Konkel, B; Macdonald, J; Lafata, K; Zaki, IH; Bozdogan, E; Chaudhry, M; Wang, Y; Janas, G; Wiggins, WF; Bashir, MR, Systematic Analysis of Common Factors Impacting Deep Learning Model Generalizability in Liver Segmentation., Radiol Artif Intell, vol. 5 no. 3 (May, 2023), pp. e220080 [doi]  [abs]
34. Kierans, AS; Lafata, KJ; Ludwig, DR; Burke, LMB; Chernyak, V; Fowler, KJ; Fraum, TJ; McGinty, KA; McInnes, MDF; Mendiratta-Lala, M; Cunha, GM; Allen, BC; Hecht, EM; Jaffe, TA; Kalisz, KR; Ranathunga, DS; Wildman-Tobriner, B; Cardona, DM; Aslam, A; Gaur, S; Bashir, MR, Comparing Survival Outcomes of Patients With LI-RADS-M Hepatocellular Carcinomas and Intrahepatic Cholangiocarcinomas., J Magn Reson Imaging, vol. 57 no. 1 (January, 2023), pp. 308-317 [doi]  [abs]
35. DeFreitas, MR; Toronka, A; Nedrud, MA; Cubberley, S; Zaki, IH; Konkel, B; Uronis, HE; Palta, M; Blazer, DG; Lafata, KJ; Bashir, MR, CT-derived body composition measurements as predictors for neoadjuvant treatment tolerance and survival in gastroesophageal adenocarcinoma., Abdom Radiol (NY), vol. 48 no. 1 (January, 2023), pp. 211-219 [doi]  [abs]
36. Ray, A; Li, X; Barisoni, L; Chakrabarty, K; Lafata, K, Decoding the Encoder, Conference Proceedings IEEE SOUTHEASTCON, vol. 2023-April (January, 2023), pp. 179-186, ISBN 9781665476119 [doi]  [abs]
37. Domanski, P; Ray, A; Firouzi, F; Lafata, K; Chakrabarty, K; Pfluger, D, Blood Glucose Prediction for Type-1 Diabetics using Deep Reinforcement Learning, Proceedings 2023 IEEE International Conference on Digital Health Icdh 2023 (January, 2023), pp. 339-347 [doi]  [abs]
38. Dahal, L; Wang, Y; Tushar, FI; Montero, I; Lafata, K; Abadi, E; Samei, E; Segars, WP; Lo, JY, Automatic quality control in computed tomography volumes segmentation using a small set of XCAT as reference images, Progress in Biomedical Optics and Imaging Proceedings of SPIE, vol. 12463 (January, 2023) [doi]  [abs]
39. Ray, A; Lafata, K; Zhang, Z; Xiong, Y; Chakrabarty, K, Privacy-preserving Job Scheduler for GPU Sharing, Proceedings 23rd IEEE ACM International Symposium on Cluster Cloud and Internet Computing Workshops Ccgridw 2023 (January, 2023), pp. 337-339 [doi]  [abs]
40. Ray, A; Lafata, K; Zhang, Z; Xiong, Y; Chakrabarty, K, Job Recommendation Service for GPU Sharing in Kubernetes, Proceedings 2023 IEEE Cloud Summit Cloud Summit 2023 (January, 2023), pp. 7-14 [doi]  [abs]
41. Yang, Z; Wang, C; Wang, Y; Lafata, KJ; Zhang, H; Ackerson, BG; Kelsey, C; Tong, B; Yin, F-F, 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, vol. 13 (2023), pp. 1185771 [doi]  [abs]
42. Yang, Z; Lafata, KJ; Chen, X; Bowsher, J; Chang, Y; Wang, C; Yin, F-F, Quantification of lung function on CT images based on pulmonary radiomic filtering., Med Phys, vol. 49 no. 11 (November, 2022), pp. 7278-7286 [doi]  [abs]
43. Carpenter, DJ; Natarajan, B; Arshad, M; Natesan, D; Schultz, O; Moravan, MJ; Read, C; Lafata, KJ; Giles, W; Fecci, P; Mullikin, TC; Reitman, ZJ; Kirkpatrick, JP; Floyd, SR; Chmura, SJ; Hong, JC; Salama, JK, Prognostic Model for Intracranial Progression after Stereotactic Radiosurgery: A Multicenter Validation Study., Cancers (Basel), vol. 14 no. 21 (October, 2022) [doi]  [abs]
44. Lafata, KJ; Wang, Y; Konkel, B; Yin, F-F; Bashir, MR, Radiomics: a primer on high-throughput image phenotyping., Abdom Radiol (NY), vol. 47 no. 9 (September, 2022), pp. 2986-3002 [doi]  [abs]
45. Jiang, H; Song, B; Qin, Y; Konanur, M; Wu, Y; McInnes, MDF; Lafata, KJ; Bashir, MR, Modifying LI-RADS on Gadoxetate Disodium-Enhanced MRI: A Secondary Analysis of a Prospective Observational Study., J Magn Reson Imaging, vol. 56 no. 2 (August, 2022), pp. 399-412 [doi]  [abs]
46. Ding, Y; Meyer, M; Lyu, P; Rigiroli, F; Ramirez-Giraldo, JC; Lafata, K; Yang, S; Marin, D, 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, vol. 63 no. 6 (June, 2022), pp. 828-838 [doi]  [abs]
47. 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., Med Phys, vol. 49 no. 5 (May, 2022), pp. 3213-3222 [doi]  [abs]
48. Allphin, AJ; Mowery, YM; Lafata, KJ; Clark, DP; Bassil, AM; Castillo, R; Odhiambo, D; Holbrook, MD; Ghaghada, KB; Badea, CT, Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden., Tomography, vol. 8 no. 2 (March, 2022), pp. 740-753 [doi]  [abs]
49. Jiang, H; Song, B; Qin, Y; Wei, Y; Konanur, M; Wu, Y; Zaki, IH; McInnes, MDF; Lafata, KJ; Bashir, MR, Data-Driven Modification of the LI-RADS Major Feature System on Gadoxetate Disodium-Enhanced MRI: Toward Better Sensitivity and Simplicity., J Magn Reson Imaging, vol. 55 no. 2 (February, 2022), pp. 493-506 [doi]  [abs]
50. Glass, C; Lafata, KJ; Jeck, W; Horstmeyer, R; Cooke, C; Everitt, J; Glass, M; Dov, D; Seidman, MA, The Role of Machine Learning in Cardiovascular Pathology., Can J Cardiol, vol. 38 no. 2 (February, 2022), pp. 234-245 [doi]  [abs]
51. Allphin, AJ; Mowery, Y; Lafata, KJ; Clark, DP; Basil, A; Castillo, R; Holbrook, MD; Ghaghada, KB; Badea, CT, Spectral micro-CT and nanoradiomic analysis for classification of tumors based on lymphocytic burden in cancer therapy studies, Progress in Biomedical Optics and Imaging Proceedings of SPIE, vol. 12036 (January, 2022), ISBN 9781510649477 [doi]  [abs]
52. Ji, H; Lafata, K; Mowery, Y; Brizel, D; Bertozzi, AL; Yin, F-F; Wang, C, Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application., Front Oncol, vol. 12 (2022), pp. 895544 [doi]  [abs]
53. Rigiroli, F; Hoye, J; Lerebours, R; Lafata, KJ; Li, C; Meyer, M; Lyu, P; Ding, Y; Schwartz, FR; Mettu, NB; Zani, S; Luo, S; Morgan, DE; Samei, E; Marin, D, CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study., Radiology, vol. 301 no. 3 (December, 2021), pp. 610-622 [doi]  [abs]
54. Li, X; Davis, RC; Xu, Y; Wang, Z; Souma, N; Sotolongo, G; Bell, J; Ellis, M; Howell, D; Shen, X; Lafata, KJ; Barisoni, L, Deep learning segmentation of glomeruli on kidney donor frozen sections., J Med Imaging (Bellingham), vol. 8 no. 6 (November, 2021), pp. 067501 [doi]  [abs]
55. 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, vol. 300 no. 2 (August, 2021), pp. 361-368 [doi]  [abs]
56. Lafata, KJ; Chang, Y; Wang, C; Mowery, YM; Vergalasova, I; Niedzwiecki, D; Yoo, DS; Liu, J-G; Brizel, DM; Yin, F-F, Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers., Med Phys, vol. 48 no. 7 (July, 2021), pp. 3767-3777 [doi]  [abs]
57. Chang, Y; Jiang, Z; Segars, WP; Zhang, Z; Lafata, K; Cai, J; Yin, F-F; Ren, L, A generative adversarial network (GAN)-based technique for synthesizing realistic respiratory motion in the extended cardiac-torso (XCAT) phantoms., Phys Med Biol, vol. 66 no. 11 (May, 2021) [doi]  [abs]
58. Lafata, KJ; Corradetti, MN; Gao, J; Jacobs, CD; Weng, J; Chang, Y; Wang, C; Hatch, A; Xanthopoulos, E; Jones, G; Kelsey, CR; Yin, F-F, Radiogenomic Analysis of Locally Advanced Lung Cancer Based on CT Imaging and Intratreatment Changes in Cell-Free DNA., Radiol Imaging Cancer, vol. 3 no. 4 (April, 2021), pp. e200157 [doi]  [abs]
59. Sotolongo, G; Je, J; Zee, J; Chen, Y; Li, X; Wang, Y; Hodgin, J; Madabhushi, A; Janowczyk, A; Lafata, K; Barisoni, L, Cortical Tubulointerstitial Mononuclear Inflammation in Renal Biopsies is a Quantitative Biomarker of Clinical Outcomes in NEPTUNE Glomerular Diseases, MODERN PATHOLOGY, vol. 34 no. SUPPL 2 (2021), pp. 1018-1019
60. Sotolongo, G; Je, J; Zee, J; Chen, Y; Li, X; Wang, Y; Hodgin, J; Madabhushi, A; Janowczyk, A; Lafata, K; Barisoni, L, Cortical Tubulointerstitial Mononuclear Inflammation in Renal Biopsies is a Quantitative Biomarker of Clinical Outcomes in NEPTUNE Glomerular Diseases, LABORATORY INVESTIGATION, vol. 101 no. SUPPL 1 (2021), pp. 1018-1019
61. Wang, Y; Li, X; Konanur, M; Konkel, B; Seyferth, E; Brajer, N; Bashir, M; Lafata, K, Computer-Assisted Diagnosis of Hepatic Portal Hypertension: A Novel, Attention-Guided Deep Learning Framework Based On CT Imaging and Laboratory Data Integration, MEDICAL PHYSICS, vol. 48 no. 6 (2021)
62. Toronka, A; Defreitas, M; Konkel, B; Nedrud, M; Zaki, I; Valentine, A; Cubberley, S; Yin, F; Bashir, M; Lafata, K, Multi-Domain Statistical Modeling of Treatment Tolerance in Patients with Gastric and Esophageal Adenocarcinoma, MEDICAL PHYSICS, vol. 48 no. 6 (2021)
63. Song, H; Milligan, J; Lafata, K; Kelly, G; Chilkoti, A; Cai, J; Yin, F, Measuring Distribution of An I-125 Labeled Elastin-Like Polypeptide (ELP) Nanoparticle Within Mice Tumors for Consideration as a Novel Technique of Delivering Brachytherapy, MEDICAL PHYSICS, vol. 48 no. 6 (2021)
64. Wang, C; Ji, H; Bertozzi, A; Brizel, D; Mowery, Y; Yin, F; Lafata, K, Biologically Guided Deep Learning for Post-Radiation PET Image Outcome Prediction: A Feasibility Study of Oropharyngeal Cancer Application, MEDICAL PHYSICS, vol. 48 no. 6 (2021)
65. Barisoni, L; Lafata, KJ; Hewitt, SM; Madabhushi, A; Balis, UGJ, Digital pathology and computational image analysis in nephropathology., Nat Rev Nephrol, vol. 16 no. 11 (November, 2020), pp. 669-685 [doi]  [abs]
66. 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, vol. 10 no. 10 (October, 2020), pp. 1917-1929 [doi]  [abs]
67. 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, vol. 65 no. 6 (March, 2020), pp. 065009 [doi]  [abs]
68. Chang, Y; Lafata, K; Wang, C; Duan, X; Geng, R; Yang, Z; Yin, F-F, Digital phantoms for characterizing inconsistencies among radiomics extraction toolboxes., Biomed Phys Eng Express, vol. 6 no. 2 (March, 2020), pp. 025016 [doi]  [abs]
69. Davis, R; Lafata, K; Li, X; Souma, N; Howell, D; Shen, X; Barisoni, L, A Deep Learning Approach to Analysis of Kidney Transplant Frozen Sections, LABORATORY INVESTIGATION, vol. 100 no. SUPPL 1 (March, 2020), pp. 1573-1573, NATURE PUBLISHING GROUP
70. Davis, R; Lafata, K; Li, X; Souma, N; Howell, D; Shen, X; Barisoni, L, A Deep Learning Approach to Analysis of Kidney Transplant Frozen Sections, MODERN PATHOLOGY, vol. 33 no. SUPPL 2 (March, 2020), pp. 1573-1573, NATURE PUBLISHING GROUP
71. Wang, C; Liu, C; Chang, Y; Lafata, K; Cui, Y; Zhang, J; Sheng, Y; Mowery, Y; Brizel, D; Yin, F-F, Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application, Frontiers in oncology, vol. 10 no. 1 (January, 2020), pp. S81-S82, Elsevier BV [doi]  [abs]
72. Li, X; Zhang, J; Sheng, Y; Lafata, K; Eclov, N; Cui, Y; Giles, W; Adamson, J; Rodrigues, A; Wang, Z; Yoo, S; Yin, F; Wu, Q; Wang, C, A Machine Learning Model for Brain V12 Gy/V60% Prediction of LINAC-Based Single-Iso-Multiple-Targets (SIMT) Stereotactic Radiosurgery (SRS): A Longitudinal Study, MEDICAL PHYSICS, vol. 47 no. 6 (2020), pp. E568-E569
73. Wang, C; Li, X; Sheng, Y; Zhang, J; Lafata, K; Yin, F; Wu, Q; Ge, Y, 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, MEDICAL PHYSICS, vol. 47 no. 6 (2020), pp. E330-E330
74. Chen, X; Lafata, K; Yang, Z; Yin, F, Quantification of Lung Ventilation Using Voxel-Based Delta Radiomics Extracted from Thoracic 4DCT, MEDICAL PHYSICS, vol. 47 no. 6 (2020), pp. E435-E435
75. 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), MEDICAL PHYSICS, vol. 47 no. 6 (2020), pp. E269-E270
76. 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, MEDICAL PHYSICS, vol. 47 no. 6 (2020), pp. E409-E409
77. Song, H; Milligan, J; Lafata, K; Kelly, G; Chilkoti, A; Cai, J; Yin, F, Determining Diffusion of Radioactive Activity Within Mice Tumor Model From a Novel Elastin-Like Polypeptide (ELP) Brachytherapy Source, MEDICAL PHYSICS, vol. 47 no. 6 (2020), pp. E740-E740
78. Lafata, K; Chang, Y; Wang, C; Mowery, Y; Vergalasova, I; Liu, J; Brizel, D; Yin, F, Unsupervised Machine Learning of Metabolic Response from Radiomic Expression of Oropharyngeal Cancers, MEDICAL PHYSICS, vol. 47 no. 6 (2020), pp. E266-E266
79. Chang, Y; Jiang, Z; Lafata, K; Zhang, Z; Segars, P; Cai, J; Yin, F; Ren, L, A Generative Adversarial Network (GAN)-Based Technique for Synthesizing Realistic Respiratory Motion in the Extended Cardiac-Torso (XCAT) Phantoms, MEDICAL PHYSICS, vol. 47 no. 6 (2020), pp. E286-E286
80. Wang, C; Liu, C; Chang, Y; Lafata, K; Cui, Y; Zhang, J; Sheng, Y; Mowery, Y; Brizel, D; Yin, F-F, Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application., Front Oncol, vol. 10 (2020), pp. 1592 [doi]  [abs]
81. Chang, Y; Liu, C; Lafata, K; Wang, C; Cui, Y; Ren, L; Mowery, YM; Brizel, DM; Yin, FF, PET Radiotherapy Response Assessment Using Encoder-Decoder Convolutional Neural Network and Pre-treatment Information: A Feasibility of Oropharynx Cancer IMRT, International Journal of Radiation Oncology*Biology*Physics, vol. 105 no. 1 (September, 2019), pp. E413-E413, Elsevier BV [doi]
82. Lafata, K; Gao, J; Jacobs, CD; Chang, Y; Wang, X; Kelsey, CR; Yin, FF; Corradetti, MN, Identification of Radiomic Biomarkers for Patients with Locally Advanced Lung Cancer, International Journal of Radiation Oncology*Biology*Physics, vol. 105 no. 1 (September, 2019), pp. E515-E515, Elsevier BV [doi]
83. 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, vol. 9 no. 1 (August, 2019), pp. 11509, Springer Science and Business Media LLC [doi]  [abs]
84. 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, MEDICAL PHYSICS, vol. 46 no. 6 (June, 2019), pp. E405-E405, WILEY
85. 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, MEDICAL PHYSICS, vol. 46 no. 6 (June, 2019), pp. E525-E525, WILEY
86. 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, MEDICAL PHYSICS, vol. 46 no. 6 (June, 2019), pp. E295-E295, WILEY
87. Chang, Y; Lafata, K; Liu, C; Wang, C; Cui, Y; Ren, L; Li, X; Mowery, Y; Brizel, D; Yin, F, An Encoder-Decoder Based Convolutional Neural Network (ED-CNN) for PET Image Response Prediction Using Pre-RT Information: A Feasibility of Oropharynx Cancer IMRT, MEDICAL PHYSICS, vol. 46 no. 6 (June, 2019), pp. E283-E283, WILEY
88. Lafata, KJ; Hong, JC; Geng, R; Ackerson, BG; Liu, J-G; Zhou, Z; Torok, J; Kelsey, CR; Yin, F-F, Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy., Phys Med Biol, vol. 64 no. 2 (January, 2019), pp. 025007 [doi]  [abs]
89. Lafata, K; Zhou, Z; Liu, JG; Yin, FF, Data clustering based on Langevin annealing with a self-consistent potential, Quarterly of Applied Mathematics, vol. 77 no. 3 (January, 2019), pp. 591-613, American Mathematical Society (AMS) [doi]  [abs]
90. Corradetti, MN; Torok, JA; Hatch, AJ; Xanthopoulos, EP; Lafata, K; Jacobs, C; Rushing, C; Calaway, J; Jones, G; Kelsey, CR; Nixon, AB, Dynamic Changes in Circulating Tumor DNA During Chemoradiation for Locally Advanced Lung Cancer., Adv Radiat Oncol, vol. 4 no. 4 (2019), pp. 748-752 [doi]  [abs]
91. Chang, Y; Lafata, K; Sun, W; Wang, C; Chang, Z; Kirkpatrick, JP; Yin, F-F, An investigation of machine learning methods in delta-radiomics feature analysis., PLoS One, vol. 14 no. 12 (2019), pp. e0226348 [doi]  [abs]
92. 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, vol. 63 no. 22 (November, 2018), pp. 225003 [doi]  [abs]
93. Ackerson, BG; Tong, BC; Hong, JC; Gu, L; Chino, J; Trotter, JW; D'Amico, TA; Torok, JA; Lafata, K; Chang, C; Kelsey, CR, Stereotactic body radiation therapy versus sublobar resection for stage I NSCLC., Lung Cancer, vol. 125 (November, 2018), pp. 185-191 [doi]  [abs]
94. 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, MEDICAL PHYSICS, vol. 45 no. 6 (June, 2018), pp. E415-E415, WILEY
95. Lafata, K; Cai, J; Liu, J; Sidhu, K; Yin, F, Deep Learning of Pulmonary Function in CT Images Based On Radiomic Filtering, MEDICAL PHYSICS, vol. 45 no. 6 (June, 2018), pp. E158-E158, WILEY
96. 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, MEDICAL PHYSICS, vol. 45 no. 6 (June, 2018), pp. E410-E410, WILEY
97. Lafata, K; Geng, R; Ackerson, B; Kelsey, C; Torok, J; Yin, F, Predicting Lung SBRT Clinical Outcomes Using Planning-CT Radiomics, MEDICAL PHYSICS, vol. 45 no. 6 (June, 2018), pp. E556-E556, WILEY
98. Geng, R; Lafata, K; Yin, F, Effect of Lung SBRT Fractionation On Feature Variability of Longitudinal Cone-Beam CT Radiomics, MEDICAL PHYSICS, vol. 45 no. 6 (2018), pp. E411-E411
99. Lafata, K; Cai, J; Wang, C; Hong, JC; Kelsey, C; Yin, FF, Sensitivity of Radiomic Features to Acquisition Noise and Respiratory Motion, International Journal of Radiation Oncology*Biology*Physics, vol. 99 no. 2 (October, 2017), pp. S93-S94, Elsevier BV [doi]
100. Lafata, K; Cai, J; Wang, C; Hong, J; Kelsey, C; Yin, F, Sensitivity of Radiomic Features to Image Noise and Respiratory Motion, MEDICAL PHYSICS, vol. 44 no. 6 (June, 2017), pp. 1 pages, WILEY
101. Lafata, K; Cai, J; Kelsey, C; Yin, F, A Radiomics Approach for Hyper-Dimensional Lung Function Mapping, MEDICAL PHYSICS, vol. 44 no. 6 (June, 2017), pp. 3287-3287, WILEY
102. Geng, R; Lafata, K; Zhang, Y; Yin, F, Harmonization of Radiomic Features On Planning CT and On-Board CBCT, MEDICAL PHYSICS, vol. 44 no. 6 (June, 2017), pp. 3287-3287, WILEY
103. Yin, FF; Lafata, K; Hong, JC; Kelsey, CR, Effects of Motion on Radiomics Analysis of Thoracic Cancers., Int J Radiat Oncol Biol Phys, vol. 98 no. 1 (May, 2017), pp. 250 [doi]
104. Lafata, K; Cai, J; Ren, L; Wu, Q; Hong, JC; Kelsey, CR; Yin, FF, Development of a Machine Learning Methodology to Estimate Lung Stereotactic Body Radiation Therapy Dosimetric Endpoints Based on Patient-Specific Anatomic Features., Int J Radiat Oncol Biol Phys, vol. 96 no. 2S (October, 2016), pp. E420-E421 [doi]
105. Lafata, K; Cai, J; Ren, L; Wu, Q; Hong, JC; Kelsey, CR; Yin, FF, Development of a Machine Learning Methodology to Estimate Lung Stereotactic Body Radiation Therapy Dosimetric Endpoints Based on Patient-Specific Anatomic Features, INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, vol. 96 no. 2 (October, 2016), pp. E420-E421
106. Lafata, K; Ren, L; Wu, Q; Kelsey, C; Hong, J; Cai, J; Yin, F, SU-D-204-01: A Methodology Based On Machine Learning and Quantum Clustering to Predict Lung SBRT Dosimetric Endpoints From Patient Specific Anatomic Features., Med Phys, vol. 43 no. 6 (June, 2016), pp. 3332 [doi]  [abs]
107. 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., Med Phys, vol. 43 no. 6 (June, 2016), pp. 3670 [doi]  [abs]
108. Lambson, K; Lafata, K; Schaal, J; Miles, D; Yoon, S; Liu, W; Oldham, M; Cai, J, SU-F-T-10: Validation of ELP Dosimetry Using PRESAGE Dosimeter: Feasibility Test and Practical Considerations., Med Phys, vol. 43 no. 6 (June, 2016), pp. 3463 [doi]  [abs]
109. 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., Med Phys, vol. 42 no. 6 (June, 2015), pp. 3564 [doi]  [abs]
110. Lafata, KJ; Bushe, H; Aronowitz, JN, A simple technique for the generation of institution-specific nomograms for permanent prostate cancer brachytherapy., J Contemp Brachytherapy, vol. 6 no. 3 (October, 2014), pp. 293-296 [doi]  [abs]
111. Lafata, K; Czito, B; Palta, M; Bashir, M; Yin, F; Cai, J, Verification of 4D-MRI Internal Target Volume Using Cine MRI, MEDICAL PHYSICS, vol. 41 no. 6 (June, 2014), pp. 201-201, WILEY [doi]  [abs]
112. Stauder, MC; Macdonald, OK; Olivier, KR; Call, JA; Lafata, K; Mayo, CS; Miller, RC; Brown, PD; Bauer, HJ; Garces, YI, Early pulmonary toxicity following lung stereotactic body radiation therapy delivered in consecutive daily fractions., Radiother Oncol, vol. 99 no. 2 (May, 2011), pp. 166-171 [doi]  [abs]

Preprints

1. Hu, Z; Yang, Z; Zhang, H; Vaios, E; Lafata, K; Yin, F-F; Wang, C, A Deep Learning Model with Radiomics Analysis Integration for Glioblastoma Post-Resection Survival Prediction (March, 2022)