Kyle Lafata

Overview:

Kyle Lafata is an Assistant Professor of Radiology, Radiation Oncology, and Electrical & Computer Engineering at Duke University. As an imaging physicist and data scientist, Dr. Lafata’s research interests are in image-based phenotyping and computational biomarkers. His dissertation work focused on nature-inspired computational methods and soft-computing paradigms, including the applied analysis of stochastic differential equations, 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). He has broad expertise in imaging science, digital pathology, computer vision, feature engineering, and applied mathematics.


The Lafata Laboratory focuses on multi-scale imaging biomarkers. They study the imaging phenotype across multiple physical length-scales, including radiological (i.e., ~10-3 m), pathological (i.e., ~10-6 m), and molecular (i.e., ~10-9 m) domains. To accomplish this, the lab develops mathematical methods, computational imaging techniques, and measurement tools to characterize and quantify the appearance and behavior of disease. This technology is applied to interrogate underlying biology, characterize tissue microenvironments, diagnose disease, predict disease progression, quantify treatment response, and enable personalized therapy.

Positions:

Assistant Professor of Radiation Oncology

Radiation Oncology
School of Medicine

Assistant Professor in Radiology

Radiology
School of Medicine

Assistant Professor in the Department of Electrical and Computer Engineering

Electrical and Computer Engineering
Pratt School of Engineering

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

Ph.D. 2018

Duke University

C. 2018

Duke University

Postdoctoral Associate, Radiation Oncology/Radiation Physics Division

Duke University School of Medicine

Grants:

Targeting the B Cell Response to Treat Antibody-Mediated Rejection

Administered By
Surgery, Abdominal Transplant Surgery
Awarded By
National Institutes of Health
Role
Co Investigator
Start Date
End Date

Computational Pathology of Proteinuric Diseases

Administered By
Medicine, Nephrology
Awarded By
National Institutes of Health
Role
Co Investigator
Start Date
End Date

Publications:

Data-Driven Modification of the LI-RADS Major Feature System on Gadoxetate Disodium-Enhanced MRI: Toward Better Sensitivity and Simplicity.

BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS) is widely accepted as a reliable diagnostic scheme for hepatocellular carcinoma (HCC) in at-risk patients. However, its application is hampered by substantial complexity and suboptimal diagnostic sensitivity. PURPOSE: To propose data-driven modifications to the LI-RADS version 2018 (v2018) major feature system (rLI-RADS) on gadoxetate disodium (EOB)-enhanced magnetic resonance imaging (MRI) to improve sensitivity and simplicity while maintaining high positive predictive value (PPV) for detecting HCC. STUDY TYPE: Retrospective. POPULATION: Two hundred and twenty-four consecutive at-risk patients (training dataset: 169, independent testing dataset: 55) with 742 LR-3 to LR-5 liver observations (HCC: N = 498 [67%]) were analyzed from a prospective observational registry collected between July 2015 and September 2018. FIELD STRENGTH/SEQUENCE: 3.0 T/T2-weighted fast spin-echo, diffusion-weighted spin-echo based echo-planar and three-dimensional (3D) T1-weighted gradient echo sequences. ASSESSMENT: All images were evaluated by three independent abdominal radiologists who were blinded to all clinical, pathological, and follow-up information. Composite reference standards of either histopathology or imaging follow-up were used. STATISTICAL TESTS: In the training dataset, LI-RADS v2018 major features were used to develop rLI-RADS based on their associated PPV for HCC. In an independent testing set, diagnostic performances of LI-RADS v2018 and rLI-RADS were computed using a generalized estimating equation model and compared with McNemar's test. A P value <0.05 was considered statistically significant. RESULTS: The median (interquartile range) size of liver observations was 13 mm (7-27 mm). The diagnostic table for rLI-RADS encompassed 9 cells, as opposed to 16 cells for LI-RADS v2018. In the testing set, compared to LI-RADS v2018, rLI-RADS category 5 demonstrated a significantly superior sensitivity (76% vs. 61%) while maintaining comparably high PPV (92.5% vs. 94.1%, P = 0.126). DATA CONCLUSION: Compared with LI-RADS v2018, rLI-RADS demonstrated improved simplicity and significantly superior diagnostic sensitivity for HCC in at-risk patients. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.
Authors
Jiang, H; Song, B; Qin, Y; Wei, Y; Konanur, M; Wu, Y; Zaki, IH; McInnes, MDF; Lafata, KJ; Bashir, MR
MLA Citation
URI
https://scholars.duke.edu/individual/pub1487782
PMID
34236120
Source
pubmed
Published In
J Magn Reson Imaging
Published Date
DOI
10.1002/jmri.27824

MO-FG-BRA-01: Development of An Image-Guided Dosimetric Planning System for Injectable Brachytherapy Using ELP Nanoparticles.

PURPOSE: To develop, validate, and evaluate a methodology for determining dosimetry for intratumoral injections of elastin-like-polypeptide (ELP) brachytherapy nanoparticles. These organic-polymer-based nanoparticles are injectable, biodegradable, and genetically tunable. We present a genetically encoded polymer-solution, composed of novel radiolabeled-ELP nanoparticles that are custom-designed to self-assemble into a local source upon intratumoral injection. Our preliminary results of a small animal study demonstrate 100% tumor response, effective radionuclide retention-rates, strong in vivo stability, and no polymer-induced toxicities. While our approach is therefore highly promising for improved brachytherapy, the current workflow lacks a dosimetry framework. METHODS: We are developing a robust software framework that provides image-guided dosimetric-planning capabilities for ELP brachytherapy. The user graphically places ELP injection sites within a µCT-planning-image, and independently defines each injection volume, concentration, and radioisotope to be used. The resulting internal dosimetry is then pre-determined by first modeling post-injection ELP advection-diffusion, and then calculating the resulting dose distribution based on a point- dose-kernel-convolution algorithm. We have experimentally measured ELP steady-state concentrations via µSPECT acquisition, and validated our dose calculation algorithm against Monte Carlo simulations of several radioactivity distributions. Finally, we have investigated potential advantages and limitations of various ELP injection parameters. RESULTS: The µSPECT results demonstrated inhomogeneous steady-state distributions of ELP in tissue, and Monte Carlo radioactivity distributions were designed accordingly. Our algorithm yielded a root-mean-square-error of less than 2% for each distribution tested (average root-mean-square-error was 0.73%). Dose-Volume-Histogram analysis of five different plans showed how strategic injection placement, and an injection volume-tapering technique, could be used to achieve D95% target coverage. CONCLUSION: We have preliminarily developed a novel planning framework for ELP brachytherapy. Its dosimetry accuracy has been validated against Monte Carlo, and we have started to investigate the potential advantages of injection-based planning. This system, once fully developed, will serve as the technical foundation for our novel approach.
Authors
Lafata, K; Schaal, J; Liu, W; Cai, J
MLA Citation
Lafata, K., et al. “MO-FG-BRA-01: Development of An Image-Guided Dosimetric Planning System for Injectable Brachytherapy Using ELP Nanoparticles.Med Phys, vol. 42, no. 6, 2015, p. 3564. Pubmed, doi:10.1118/1.4925405.
URI
https://scholars.duke.edu/individual/pub1075502
PMID
26128702
Source
pubmed
Published In
Medical Physics
Volume
42
Published Date
Start Page
3564
DOI
10.1118/1.4925405

Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers.

PURPOSE: This study investigated the prognostic potential of intra-treatment PET radiomics data in patients undergoing definitive (chemo) radiation therapy for oropharyngeal cancer (OPC) on a prospective clinical trial. We hypothesized that the radiomic expression of OPC tumors after 20 Gy is associated with recurrence-free survival (RFS). MATERIALS AND METHODS: Sixty-four patients undergoing definitive (chemo)radiation for OPC were prospectively enrolled on an IRB-approved study. Investigational 18 F-FDG-PET/CT images were acquired prior to treatment and 2 weeks (20 Gy) into a seven-week course of therapy. Fifty-five quantitative radiomic features were extracted from the primary tumor as potential biomarkers of early metabolic response. An unsupervised data clustering algorithm was used to partition patients into clusters based only on their radiomic expression. Clustering results were naïvely compared to residual disease and/or subsequent recurrence and used to derive Kaplan-Meier estimators of RFS. To test whether radiomic expression provides prognostic value beyond conventional clinical features associated with head and neck cancer, multivariable Cox proportional hazards modeling was used to adjust radiomic clusters for T and N stage, HPV status, and change in tumor volume. RESULTS: While pre-treatment radiomics were not prognostic, intra-treatment radiomic expression was intrinsically associated with both residual/recurrent disease (P = 0.0256, χ 2 test) and RFS (HR = 7.53, 95% CI = 2.54-22.3; P = 0.0201). On univariate Cox analysis, radiomic cluster was associated with RFS (unadjusted HR = 2.70; 95% CI = 1.26-5.76; P = 0.0104) and maintained significance after adjustment for T, N staging, HPV status, and change in tumor volume after 20 Gy (adjusted HR = 2.69; 95% CI = 1.03-7.04; P = 0.0442). The particular radiomic characteristics associated with outcomes suggest that metabolic spatial heterogeneity after 20 Gy portends complete and durable therapeutic response. This finding is independent of baseline metabolic imaging characteristics and clinical features of head and neck cancer, thus providing prognostic advantages over existing approaches. CONCLUSIONS: Our data illustrate the prognostic value of intra-treatment metabolic image interrogation, which may potentially guide adaptive therapy strategies for OPC patients and serve as a blueprint for other disease sites. The quality of our study was strengthened by its prospective image acquisition protocol, homogenous patient cohort, relatively long patient follow-up times, and unsupervised clustering formalism that is less prone to hyper-parameter tuning and over-fitting compared to supervised learning.
Authors
Lafata, KJ; Chang, Y; Wang, C; Mowery, YM; Vergalasova, I; Niedzwiecki, D; Yoo, DS; Liu, J-G; Brizel, DM; Yin, F-F
MLA Citation
Lafata, Kyle J., et al. “Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers.Med Phys, vol. 48, no. 7, July 2021, pp. 3767–77. Pubmed, doi:10.1002/mp.14926.
URI
https://scholars.duke.edu/individual/pub1482142
PMID
33959972
Source
pubmed
Published In
Med Phys
Volume
48
Published Date
Start Page
3767
End Page
3777
DOI
10.1002/mp.14926

Computer-Assisted Diagnosis of Hepatic Portal Hypertension: A Novel, Attention-Guided Deep Learning Framework Based On CT Imaging and Laboratory Data Integration

Authors
Wang, Y; Li, X; Konanur, M; Konkel, B; Seyferth, E; Brajer, N; Bashir, M; Lafata, K
URI
https://scholars.duke.edu/individual/pub1494971
Source
wos-lite
Published In
Medical Physics
Volume
48
Published Date

Multi-Domain Statistical Modeling of Treatment Tolerance in Patients with Gastric and Esophageal Adenocarcinoma

Authors
Toronka, A; Defreitas, M; Konkel, B; Nedrud, M; Zaki, I; Valentine, A; Cubberley, S; Yin, F; Bashir, M; Lafata, K
MLA Citation
URI
https://scholars.duke.edu/individual/pub1494972
Source
wos-lite
Published In
Medical Physics
Volume
48
Published Date