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:

Establishing ADC-Based Histogram and Texture Features for Early Treatment-Induced Changes in Head and Neck Squamous Cell Carcinoma.

The purpose of this study was to assess baseline variability in histogram and texture features derived from apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (DW-MRI) examinations and to identify early treatment-induced changes to these features in patients with head and neck squamous cell carcinoma (HNSCC) undergoing definitive chemoradiation. Patients with American Joint Committee on Cancer Stage III-IV (7th edition) HNSCC were prospectively enrolled on an IRB-approved study to undergo two pre-treatment baseline DW-MRI examinations, performed 1 week apart, and a third early intra-treatment DW-MRI examination during the second week of chemoradiation. Forty texture and six histogram features were derived from ADC maps. Repeatability of the features from the baseline ADC maps was assessed with the intra-class correlation coefficient (ICC). A Wilcoxon signed-rank test compared average baseline and early treatment feature changes. Data from nine patients were used for this study. Comparison of the two baseline ADC maps yielded 11 features with an ICC ≥ 0.80, indicating that these features had excellent repeatability: Run Gray-Level Non-Uniformity, Coarseness, Long Zone High Gray-Level, Variance (Histogram Feature), Cluster Shade, Long Zone, Variance (Texture Feature), Run Length Non-Uniformity, Correlation, Cluster Tendency, and ADC Median. The Wilcoxon signed-rank test resulted in four features with significantly different early treatment-induced changes compared to the baseline values: Run Gray-Level Non-Uniformity (p = 0.005), Run Length Non-Uniformity (p = 0.005), Coarseness (p = 0.006), and Variance (Histogram) (p = 0.006). The feasibility of histogram and texture analysis as a potential biomarker is dependent on the baseline variability of each metric, which disqualifies many features.
Authors
Rodrigues, A; Loman, K; Nawrocki, J; Hoang, JK; Chang, Z; Mowery, YM; Oyekunle, T; Niedzwiecki, D; Brizel, DM; Craciunescu, O
MLA Citation
Rodrigues, Anna, et al. “Establishing ADC-Based Histogram and Texture Features for Early Treatment-Induced Changes in Head and Neck Squamous Cell Carcinoma.Front Oncol, vol. 11, 2021, p. 708398. Pubmed, doi:10.3389/fonc.2021.708398.
URI
https://scholars.duke.edu/individual/pub1497057
PMID
34540674
Source
pubmed
Published In
Frontiers in Oncology
Volume
11
Published Date
Start Page
708398
DOI
10.3389/fonc.2021.708398

Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning.

We are developing imaging methods for a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform longitudinal micro-computed tomography (micro-CT) of mice to detect lung metastasis after treatment. This work explores deep learning (DL) as a fast approach for automated lung nodule detection. We used data from control mice both with and without primary lung tumors. To augment the number of training sets, we have simulated data using real augmented tumors inserted into micro-CT scans. We employed a convolutional neural network (CNN), trained with four competing types of training data: (1) simulated only, (2) real only, (3) simulated and real, and (4) pretraining on simulated followed with real data. We evaluated our model performance using precision and recall curves, as well as receiver operating curves (ROC) and their area under the curve (AUC). The AUC appears to be almost identical (0.76-0.77) for all four cases. However, the combination of real and synthetic data was shown to improve precision by 8%. Smaller tumors have lower rates of detection than larger ones, with networks trained on real data showing better performance. Our work suggests that DL is a promising approach for fast and relatively accurate detection of lung tumors in mice.
Authors
Holbrook, MD; Clark, DP; Patel, R; Qi, Y; Bassil, AM; Mowery, YM; Badea, CT
MLA Citation
Holbrook, Matthew D., et al. “Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning.Tomography, vol. 7, no. 3, Aug. 2021, pp. 358–72. Pubmed, doi:10.3390/tomography7030032.
URI
https://scholars.duke.edu/individual/pub1492390
PMID
34449750
Source
pubmed
Published In
Tomography
Volume
7
Published Date
Start Page
358
End Page
372
DOI
10.3390/tomography7030032

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

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

Research Areas:

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