
Elizabeth McDonald
VerifiedUniversity of Pennsylvania · Rehabilitation Medicine
Active 1996–2024
Research topics
- Internal medicine
- Medicine
- Nuclear medicine
- Oncology
- Radiology
- Pathology
- Pharmacology
- Medical physics
Selected publications
Radiology Imaging Cancer · 2022 · 23 citations
- Medicine
- Medical physics
- Nuclear medicine
F-FTT, Investigational New Drug © RSNA, 2022.
npj Breast Cancer · 2020 · 81 citations
- Medicine
- Oncology
- Internal medicine
Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.
Radiology · 2020 · 43 citations
1st authorCorresponding- Medicine
- Nuclear medicine
- Radiology
-value acquisition is a simple and sufficient diffusion-weighted MRI metric to augment diagnostic performance of breast MRI compared with more complex approaches to apparent diffusion coefficient measurement. © RSNA, 2020
Journal of Breast Imaging · 2020 · 17 citations
- Medicine
- Radiology
- Nuclear medicine
OBJECTIVE: The A6702 multisite trial confirmed that apparent diffusion coefficient (ADC) measures can improve breast MRI accuracy and reduce unnecessary biopsies, but also found that technical issues rendered many lesions non-evaluable on diffusion-weighted imaging (DWI). This secondary analysis investigated factors affecting lesion evaluability and impact on diagnostic performance. METHODS: -value, echo-planar imaging sequence. Scans were reviewed for multiple quality factors (artifacts, signal-to-noise, misregistration, and fat suppression); lesions were considered non-evaluable if there was low confidence in ADC measurement. Associations of lesion evaluability with imaging and lesion characteristics were determined. Areas under the receiver operating characteristic curves (AUCs) were compared using bootstrapping. RESULTS: = 0.001). Smaller (≤10 mm) lesions were more commonly non-evaluable than larger lesions (p <0.03), though not significant after multiplicity correction. The AUC for differentiating benign and malignant lesions increased after excluding non-evaluable lesions, from 0.61 (95% CI: 0.50-0.71) to 0.75 (95% CI: 0.65-0.84). CONCLUSION: Image quality remains a technical challenge in breast DWI, particularly for smaller lesions. Protocol optimization and advanced acquisition and post-processing techniques would help to improve clinical utility.
Radiology · 2020 · 105 citations
- Medicine
- Internal medicine
See also the editorial by Moy and Heller in this issue.
Frequent coauthors
- 37 shared
Emily F. Conant
Hospital of the University of Pennsylvania
- 27 shared
Savannah C. Partridge
Memorial Sloan Kettering Cancer Center
- 27 shared
Susan P. Weinstein
University of Pennsylvania
- 26 shared
Mitchell D. Schnall
University of Pennsylvania
- 20 shared
Despina Kontos
Columbia University
- 20 shared
Constance D. Lehman
Massachusetts General Hospital
- 19 shared
Habib Rahbar
University of Washington
- 18 shared
Thomas Walter
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