Mary Soo

Overview:

I am currently studying alternative and adjunctive imaging modalities for detecting carcinoma in the breast. This includes PET imaging, MRI and techniques for improvements in sonographic imaging. I am also studying various modalities for imaging breast implants.

Positions:

Professor of Radiology

Radiology, Breast Imaging
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

M.D. 1987

Wake Forest University

Grants:

Reducing Benign Breast Biopsies with Computer Modeling

Administered By
Radiology, Breast Imaging
Awarded By
National Institutes of Health
Role
Clinical Investigator
Start Date
End Date

FDG-PEM Detection - Characterization of Breast Cancer

Administered By
Radiology, Breast Imaging
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

A novel strategy to see and treat breast cancer: translation to intra-operative breast margin assessment

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

Pain, Distress and Mammography Use in Breast Cancer Patients

Administered By
Psychiatry & Behavioral Sciences, Behavioral Medicine
Awarded By
National Institutes of Health
Role
Co Investigator
Start Date
End Date

Computer-Aided Diagnosis Of Breast Cancer Invasion

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

Publications:

Computer aided prediction of breast implant rupture based on mammographic findings

© 1995 SPIE. All rights reserved. A computer aided diagnostic system has been developed to predict the status of a breast implant (intact/ruptured) based on mammographic findings. Mammograms were obtained from 112 patients who presented for surgical removal of breast implants. Findings were recorded by radiologists for each patient. Of these 112 cases, 77 were ruptured while 35 were intact at the time of surgery. An artificial neural network (ANN) was trained to output the implant status when given the mammographic findings as inputs. The ANN was a backpropagation network with nine inputs, one hidden layer with 4 nodes, and one output node (implant status). The network was trained using the round-robin technique and evaluated using ROC analysis. The network performed well with an ROC area of 0.84. This was better than the radiologists's performance with sensitivity of 0.67 and specificity of 0.72. At a sensitivity of 0.67 (to match the radiologists), the network had a specificity of 0.89. At a specificity of 0.72 (to match the radiologists), the network had a sensitivity of 0.78. An ANN has been developed which demonstrates encouraging diagnostic performance for predicting the status of breast implants from mammographic findings.
Authors
Floyd, CE; Soo, MS; Tourassi, GD; Kornguth, PJ
MLA Citation
Floyd, C. E., et al. “Computer aided prediction of breast implant rupture based on mammographic findings.” Proceedings of Spie  the International Society for Optical Engineering, vol. 2434, 1995, pp. 471–77. Scopus, doi:10.1117/12.208718.
URI
https://scholars.duke.edu/individual/pub1425312
Source
scopus
Published In
Smart Structures and Materials 2005: Active Materials: Behavior and Mechanics
Volume
2434
Published Date
Start Page
471
End Page
477
DOI
10.1117/12.208718

Exploiting heat shock protein expression to develop a non-invasive diagnostic tool for breast cancer.

Leveraging the unique surface expression of heat shock protein 90 (Hsp90) in breast cancer provides an exciting opportunity to develop rapid diagnostic tests at the point-of-care setting. Hsp90 has previously been shown to have elevated expression levels across all breast cancer receptor subtypes. We have developed a non-destructive strategy using HS-27, a fluorescently-tethered Hsp90 inhibitor, to assay surface Hsp90 expression on intact tissue specimens and validated our approach in clinical samples from breast cancer patients across estrogen receptor positive, Her2-overexpressing, and triple negative receptor subtypes. Utilizing a pre-clinical biopsy model, we optimized three imaging parameters that may affect the specificity of HS-27 based diagnostics - time between tissue excision and staining, agent incubation time, and agent dose, and translated our strategy to clinical breast cancer samples. Findings indicated that HS-27 florescence was highest in tumor tissue, followed by benign tissue, and finally followed by mammoplasty negative control samples. Interestingly, fluorescence in tumor samples was highest in Her2+ and triple negative subtypes, and inversely correlated with the presence of tumor infiltrating lymphocytes indicating that HS-27 fluorescence increases in aggressive breast cancer phenotypes. Development of a Gaussian support vector machine classifier based on HS-27 fluorescence features resulted in a sensitivity and specificity of 82% and 100% respectively when classifying tumor and benign conditions, setting the stage for rapid and automated tissue diagnosis at the point-of-care.
Authors
Crouch, BT; Gallagher, J; Wang, R; Duer, J; Hall, A; Soo, MS; Hughes, P; Haystead, T; Ramanujam, N
MLA Citation
Crouch, Brian T., et al. “Exploiting heat shock protein expression to develop a non-invasive diagnostic tool for breast cancer..” Sci Rep, vol. 9, no. 1, Mar. 2019. Pubmed, doi:10.1038/s41598-019-40252-y.
URI
https://scholars.duke.edu/individual/pub1373597
PMID
30837677
Source
pubmed
Published In
Scientific Reports
Volume
9
Published Date
Start Page
3461
DOI
10.1038/s41598-019-40252-y

Can breast cancer molecular subtype help to select patients for preoperative MR imaging?

PURPOSE: To assess whether breast cancer molecular subtype classified by surrogate markers can be used to predict the extent of clinically relevant disease with preoperative breast magnetic resonance (MR) imaging. MATERIALS AND METHODS: In this HIPAA-compliant, institutional review board-approved study, informed consent was waived. Preoperative breast MR imaging reports from 441 patients were reviewed for multicentric and/or multifocal disease, lymph node involvement, skin and/or nipple invasion, chest wall and/or pectoralis muscle invasion, or contralateral disease. Pathologic reports were reviewed to confirm the MR imaging findings and for hormone receptors (estrogen and progesterone subtypes), human epidermal growth factor receptor type 2 (HER2 subtype), tumor size, and tumor grade. Surrogates were used to categorize tumors by molecular subtype: hormone receptor positive and HER2 negative (luminal A subtype); hormone receptor positive and HER2 positive (luminal B subtype); hormone receptor negative and HER2 positive (HER2 subtype); hormone receptor negative and HER2 negative (basal subtype). All patients included in the study had a histologic correlation with MR imaging findings or they were excluded. χ(2) analysis was used to compare differences between subtypes, with multivariate logistic regression analysis used to assess for variable independence. RESULTS: Identified were 289 (65.5%) luminal A, 45 (10.2%) luminal B, 26 (5.9%) HER2, and 81 (18.4%) basal subtypes. Among subtypes, significant differences were found in the frequency of multicentric and/or multifocal disease (luminal A, 27.3% [79 of 289]; luminal B, 53.3% [24 of 45]; HER2, 65.4% [17 of 26]; basal, 27.2% [22 of 81]; P < .001) and lymph node involvement (luminal A, 17.3% [50 of 289]; luminal B, 35.6% [26 of 45]; HER2, 34.6% [nine of 26]; basal 24.7% [20 of 81]; P = .014). Multivariate analysis showed that molecular subtype was independently predictive of multifocal and/or multicentric disease. CONCLUSION: Preoperative breast MR imaging is significantly more likely to help detect multifocal and/or multicentric disease and lymph node involvement in luminal B and HER2 molecular subtype breast cancers. Molecular subtype may help to select patients for preoperative breast MR imaging.
MLA Citation
Grimm, Lars J., et al. “Can breast cancer molecular subtype help to select patients for preoperative MR imaging?.” Radiology, vol. 274, no. 2, Feb. 2015, pp. 352–58. Pubmed, doi:10.1148/radiol.14140594.
URI
https://scholars.duke.edu/individual/pub1048320
PMID
25325325
Source
pubmed
Published In
Radiology
Volume
274
Published Date
Start Page
352
End Page
358
DOI
10.1148/radiol.14140594

Breast imaging: Case 8

Authors
Williford, ME; Soo, MS
MLA Citation
Williford, M. E., and M. S. Soo. “Breast imaging: Case 8.” Duke Radiology Case Review: Imaging, Differential Diagnosis, and Discussion: 2nd Edition, 2012, pp. 76–77.
URI
https://scholars.duke.edu/individual/pub1136687
Source
scopus
Published Date
Start Page
76
End Page
77

Comparative performance of multiview stereoscopic and mammographic display modalities for breast lesion detection.

PURPOSE: Mammography is known to be one of the most difficult radiographic exams to interpret. Mammography has important limitations, including the superposition of normal tissue that can obscure a mass, chance alignment of normal tissue to mimic a true lesion and the inability to derive volumetric information. It has been shown that stereomammography can overcome these deficiencies by showing that layers of normal tissue lay at different depths. If standard stereomammography (i.e., a single stereoscopic pair consisting of two projection images) can significantly improve lesion detection, how will multiview stereoscopy (MVS), where many projection images are used, compare to mammography? The aim of this study was to assess the relative performance of MVS compared to mammography for breast mass detection. METHODS: The MVS image sets consisted of the 25 raw projection images acquired over an arc of approximately 45 degrees using a Siemens prototype breast tomosynthesis system. The mammograms were acquired using a commercial Siemens FFDM system. The raw data were taken from both of these systems for 27 cases and realistic simulated mass lesions were added to duplicates of the 27 images at the same local contrast. The images with lesions (27 mammography and 27 MVS) and the images without lesions (27 mammography and 27 MVS) were then postprocessed to provide comparable and representative image appearance across the two modalities. All 108 image sets were shown to five full-time breast imaging radiologists in random order on a state-of-the-art stereoscopic display. The observers were asked to give a confidence rating for each image (0 for lesion definitely not present, 100 for lesion definitely present). The ratings were then compiled and processed using ROC and variance analysis. RESULTS: The mean AUC for the five observers was 0.614 +/- 0.055 for mammography and 0.778 +/- 0.052 for multiview stereoscopy. The difference of 0.164 +/- 0.065 was statistically significant with a p-value of 0.0148. CONCLUSIONS: The differences in the AUCs and the p-value suggest that multiview stereoscopy has a statistically significant advantage over mammography in the detection of simulated breast masses. This highlights the dominance of anatomical noise compared to quantum noise for breast mass detection. It also shows that significant lesion detection can be achieved with MVS without any of the artifacts associated with tomosynthesis.
Authors
Webb, LJ; Samei, E; Lo, JY; Baker, JA; Ghate, SV; Kim, C; Soo, MS; Walsh, R
MLA Citation
Webb, Lincoln J., et al. “Comparative performance of multiview stereoscopic and mammographic display modalities for breast lesion detection..” Med Phys, vol. 38, no. 4, Apr. 2011, pp. 1972–80. Pubmed, doi:10.1118/1.3562901.
URI
https://scholars.duke.edu/individual/pub733126
PMID
21626930
Source
pubmed
Published In
Medical Physics
Volume
38
Published Date
Start Page
1972
End Page
1980
DOI
10.1118/1.3562901