Yvonne Mowery

Positions:

Butler Harris Assistant Professor in Radiation Oncology

Radiation Oncology
School of Medicine

Assistant Professor of Radiation Oncology

Radiation Oncology
School of Medicine

Assistant Professor in Head and Neck Surgery & Communication Sciences

Head and Neck Surgery & Communication Sciences
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

M.D. 2012

Duke University

Ph.D. 2012

Duke University

Intern, Medicine

Duke University School of Medicine

Resident, Radiation Oncology

Duke University School of Medicine

Grants:

The Duke Preclinical Research Resources for Quantitative Imaging Biomarkers

Administered By
Radiology
Awarded By
National Institutes of Health
Role
Co Investigator
Start Date
End Date

Patient Reported Outcomes and Financial Toxicity in Head and Neck Cancer

Administered By
Radiation Oncology
Awarded By
Radiation Oncology Institute
Role
Principal Investigator
Start Date
End Date

SARC Spore - Bridge Funding

Administered By
Radiation Oncology
Awarded By
Sarcoma Alliance for Research Through Collaboration
Role
Co Investigator
Start Date
End Date

Mechanisms that Regulate Sarcoma Response to Immune Checkpoint Inhibition of PD-1

Administered By
Radiation Oncology
Awarded By
Sarcoma Alliance for Research Through Collaboration
Role
Investigator
Start Date
End Date

Mechanisms that Regulate Sarcoma Response to Immune Checkpoint Inhibition of PD-1

Administered By
Radiation Oncology
Awarded By
Sarcoma Alliance for Research Through Collaboration
Role
Investigator
Start Date
End Date

Publications:

Evaluation of GRID and Spatially Fractionated Radiation Therapy: Dosimetry and Preclinical Trial

Authors
Johnson, T; Bassil, A; Kent, C; Williams, N; Palmer, G; Mowery, Y; Oldham, M
MLA Citation
Johnson, T., et al. “Evaluation of GRID and Spatially Fractionated Radiation Therapy: Dosimetry and Preclinical Trial.” Medical Physics, vol. 48, no. 6, 2021.
URI
https://scholars.duke.edu/individual/pub1495039
Source
wos-lite
Published In
Medical Physics
Volume
48
Published Date

Biologically Guided Deep Learning for Post-Radiation PET Image Outcome Prediction: A Feasibility Study of Oropharyngeal Cancer Application

Authors
Wang, C; Ji, H; Bertozzi, A; Brizel, D; Mowery, Y; Yin, F; Lafata, K
URI
https://scholars.duke.edu/individual/pub1495088
Source
wos-lite
Published In
Medical Physics
Volume
48
Published Date

Failure Mode and Effects Analysis for PET Applications in Radiation Therapy Quality Management

Authors
Rodrigues, A; O'Daniel, J; Mowery, Y; Yin, F; Cui, Y
MLA Citation
Rodrigues, A., et al. “Failure Mode and Effects Analysis for PET Applications in Radiation Therapy Quality Management.” Medical Physics, vol. 47, no. 6, 2020, pp. E780–E780.
URI
https://scholars.duke.edu/individual/pub1498193
Source
wos-lite
Published In
Medical Physics
Volume
47
Published Date
Start Page
E780
End Page
E780

Ex Vivo MR Histology and Cytometric Feature Mapping Connect Three-dimensional in Vivo MR Images to Two-dimensional Histopathologic Images of Murine Sarcomas.

Purpose To establish a platform for quantitative tissue-based interpretation of cytoarchitecture features from tumor MRI measurements. Materials and Methods In a pilot preclinical study, multicontrast in vivo MRI of murine soft-tissue sarcomas in 10 mice, followed by ex vivo MRI of fixed tissues (termed MR histology), was performed. Paraffin-embedded limb cross-sections were stained with hematoxylin-eosin, digitized, and registered with MRI. Registration was assessed by using binarized tumor maps and Dice similarity coefficients (DSCs). Quantitative cytometric feature maps from histologic slides were derived by using nuclear segmentation and compared with registered MRI, including apparent diffusion coefficients and transverse relaxation times as affected by magnetic field heterogeneity (T2* maps). Cytometric features were compared with each MR image individually by using simple linear regression analysis to identify the features of interest, and the goodness of fit was assessed on the basis of R2 values. Results Registration of MR images to histopathologic slide images resulted in mean DSCs of 0.912 for ex vivo MR histology and 0.881 for in vivo MRI. Triplicate repeats showed high registration repeatability (mean DSC, >0.9). Whole-slide nuclear segmentations were automated to detect nuclei on histopathologic slides (DSC = 0.8), and feature maps were generated for correlative analysis with MR images. Notable trends were observed between cell density and in vivo apparent diffusion coefficients (best line fit: R2 = 0.96, P < .001). Multiple cytoarchitectural features exhibited linear relationships with in vivo T2* maps, including nuclear circularity (best line fit: R2 = 0.99, P < .001) and variance in nuclear circularity (best line fit: R2 = 0.98, P < .001). Conclusion An infrastructure for registering and quantitatively comparing in vivo tumor MRI with traditional histologic analysis was successfully implemented in a preclinical pilot study of soft-tissue sarcomas. Keywords: MRI, Pathology, Animal Studies, Tissue Characterization Supplemental material is available for this article. © RSNA, 2021.
Authors
Blocker, SJ; Cook, J; Mowery, YM; Everitt, JI; Qi, Y; Hornburg, KJ; Cofer, GP; Zapata, F; Bassil, AM; Badea, CT; Kirsch, DG; Johnson, GA
MLA Citation
Blocker, Stephanie J., et al. “Ex Vivo MR Histology and Cytometric Feature Mapping Connect Three-dimensional in Vivo MR Images to Two-dimensional Histopathologic Images of Murine Sarcomas.Radiol Imaging Cancer, vol. 3, no. 3, May 2021, p. e200103. Pubmed, doi:10.1148/rycan.2021200103.
URI
https://scholars.duke.edu/individual/pub1483145
PMID
34018846
Source
pubmed
Published In
Radiol Imaging Cancer
Volume
3
Published Date
Start Page
e200103
DOI
10.1148/rycan.2021200103

Unsupervised Machine Learning of Metabolic Response from Radiomic Expression of Oropharyngeal Cancers

Authors
Lafata, K; Chang, Y; Wang, C; Mowery, Y; Vergalasova, I; Liu, J; Brizel, D; Yin, F
MLA Citation
Lafata, K., et al. “Unsupervised Machine Learning of Metabolic Response from Radiomic Expression of Oropharyngeal Cancers.” Medical Physics, vol. 47, no. 6, 2020, pp. E266–E266.
URI
https://scholars.duke.edu/individual/pub1498245
Source
wos-lite
Published In
Medical Physics
Volume
47
Published Date
Start Page
E266
End Page
E266

Research Areas:

Hypopharyngeal Neoplasms
Immunotherapy
Laryngeal Neoplasms
Neck--Cancer--Radiotherapy
Oropharyngeal Neoplasms
Radiation Oncology
Salivary Gland Neoplasms
Skin--Cancer--Radiotherapy
Tumors--Animal models