ARFI, VisR and DDAI Ultrasound for Improving Discrimination of Malignant and Unresponsive Breast Cancer
Conditions: Breast Cancer
Sponsor: UNC Lineberger Comprehensive Cancer Center

LCCC 1745: ARFI, VisR, and DDAI Ultrasound for Improving Discrimination of 
Malignant and Unresponsive Breast Cancer 
 
 
NCT Number: NCT03785782 
Document Date: October 26, 2020 

LINEBERGER COMPREHENSIVE CANCER CENTER 
 
CLINICAL ONCOLOGY RESEARCH PROGRAM 
UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL 
 
LCCC  1745:  ARFI, VisR AND DDAI ULTRASOUND FOR IMPROVING 
DISCRIMINATION OF MALIGNANT AND UNRESPONSIVE BREAST CANCER  
 
Principal Investigator 
Caterina M. Gallippi, Ph.D  
Dept. of Biomedical Engineering 
UNC Chapel Hill 
446 MacNider Bldg. CB# 7575 
333 S. Columbia St. 
Chapel Hill, NC, USA 27599 
919-843-6647 (PHONE) 
919-966-2963 (FAX) 
cmgallip@email.unc.edu 
 
Principal Co-Investigator  
Cherie M. Kuzmiak, DO  
Associate Professor of Radiology  
Director, Breast Imaging Division  
UNC School of Medicine  
CB #7510, Department of Radiology  
Physicians' Office Building, Rm#118  
170 Manning Drive  
Chapel Hill, NC 27599  
(984) 974-8123 (PHONE) 
(984) 974-2608 (FAX) 
cherie_kuzmiak@med.unc.edu 
 
Co-Investigators  
Kristalyn Gallagher, DO  
Clinical Assistant Professor of Surgery 
UNC Department of Surgery 
Division of Surgical Oncology, CB #7213 
UNC Department of Surgery 
Chapel Hill, NC  27599-7213 
919-966-5221 (PHONE) 
919-966-8806 (FAX) 
kristalyn_gallagher@med.unc.edu 
 
Carey K. Anders, MD 
Associate Professor of Medicine  
University of North Carolina at Chapel Hill 
Lineberger Comprehensive Cancer Center 
Chapel Hill, NC 
919-966-1997 (PHONE) 
carey_anders@med.unc.edu 

LINEBERGER COMPREHENSIVE CANCER CENTER 
 
CLINICAL ONCOLOGY RESEARCH PROGRAM 
UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL 
 
 
Biostatistician  
Melissa Caughey, Ph.D.  
Cardiology 
UNC Chapel Hill 
104 Mason Farm Rd. 
Chapel Hill, NC, 27514 
melissa_caughey@med.unc.edu 
 
Study Coordinators: 
Shanah Kirk 
Research Specialist 
Department of Radiology 
UNC School of Medicine 
517 Old Infirmary 
Campus Box 7510 
Chapel Hill, NC 27599 
919-966-6957 (PHONE) 
shanah_kirk@med.unc.edu 
 
Terry Hartman 
Clinical Research Manager 
Department of Radiology 
UNC School of Medicine 
517 Old Infirmary 
Campus Box 7510 
Chapel Hill, NC 27599 
(919) 966-4997 (PHONE) 
terry_hartman@med.unc.edu 
 
Sponsor:  Lineberger Comprehensive Cancer Center (LCCC) 
 
Funding Source: LCCC Pilot award 
 
Version Date: October 26, 2020 
 

LINEBERGER COMPREHENSIVE CANCER CENTER 
 
CLINICAL ONCOLOGY RESEARCH PROGRAM 
UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL 
 
LCCC  1745: ARFI, VisR AND DDAI ULTRASOUND FOR IMPROVING 
DISCRIMINATION OF MALIGNANT AND UNRESPONSIVE BREAST CANCER 
 
Principal Investigator 
Caterina M. Gallippi, Ph.D  
Dept. of Biomedical Engineering 
UNC Chapel Hill 
446 MacNider Bldg. CB# 7575 
333 S. Columbia St. 
Chapel Hill, NC, USA 27599 
919-843-6647 (PHONE) 
919-966-2963 (FAX) 
cmgallip@email.unc.edu 
 
 
 
 
 
 
 
Signature Page 
 
The signature below constitutes the approval of this protocol and the attachments, and 
provides the necessary assurances that this trial will be conducted according to all 
stipulations of the protocol, including all statements regarding confidentiality, and 
according to local legal and regulatory requirements and applicable U.S. federal regulations 
and ICH guidelines. 
 
Principal Investigator (PI) Name:______Caterina Gallippi, Ph.D._________________ 
 
PI Signature: ___________________ 
 
Date:_____12/7/19_______________ 
 
 
 
 

LCCC 1745 
 
CONFIDENTIAL 
PI: Gallippi 
 
UNIVERSITY OF NORTH CAROLINA 
Version: February 21, 2018 
 
 
i 
TABLE OF CONTENTS 
1.0 
BACKGROUND AND RATIONALE ..................................................................1 
1.1 
Study Synopsis ......................................................................................................1 
1.2 
Disease Background (if applicable) ......................................................................1 
1.3 
Diagnostic and Prognostic Studies ........................................................................2 
1.4 
Rationale................................................................................................................3 
1.5 
Compliance Statement...........................................................................................4 
2.0 
STUDY OBJECTIVES ...........................................................................................4 
2.1 
Aim #1: Quantify the ability of ultrasound-derived stiffness, elasticity, viscosity, 
and anisotropy to detect malignancy. ...............................................................................4 
2.2 
Aim #2: Quantify the ability of ultrasound-derived stiffness, elasticity, viscosity, 
and anisotropy to predict a positive response to treatment. ..............................................5 
2.3 
Endpoints ...............................................................................................................7 
3.0 
PATIENT ELIGIBILITY ......................................................................................7 
3.1 
Inclusion Criteria ...................................................................................................7 
3.2 
Exclusion Criteria ..................................................................................................8 
4.0 
STUDY PLAN .........................................................................................................9 
4.1 
Schema ..................................................................................................................9 
4.2 
Imaging Schedule ..................................................................................................9 
4.3 
Patient Identification and Consent ......................................................................11 
4.4 
Abstraction of Medical Records (if applicable) ..................................................11 
4.5 
Other Sections as Appropriate to Describe Study Methods ..... Error! Bookmark 
not defined. 
5.0 
EXPECTED RISKS/UNANTICIPATED PROBLEMS/SAFETY 
MONITORING ................................................................................................................11 

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5.1 
Assessment of Safety ..........................................................................................11 
5.2 
Expected Risks (include Applicable Sections)....................................................12 
5.3 
UNANTICIPATED CONCERNS ......................................................................13 
5.4 
Unanticipated Adverse Device Effect (UADE) ..................................................13 
5.5 
Unanticipated Problems (UP)..............................................................................13 
5.6 
Reporting .............................................................................................................13 
5.7 
UP ........................................................................................................................14 
5.8 
Data and Safety Monitoring Plan ........................................................................14 
6.0 
STATISTICAL CONSIDERATIONS ................................................................15 
6.1 
Study Design/Study Endpoints ...........................................................................15 
6.2 
Sample Size and Accrual.....................................................................................15 
6.3 
Data Analysis Plans .............................................................................................15 
7.0 
STUDY MANAGEMENT ....................................................................................18 
7.1 
Institutional Review Board (IRB) Approval and Consent ..................................18 
7.2 
Required Documentation.....................................................................................18 
7.3 
Registration Procedures.......................................................................................19 
7.4 
Data Management and Monitoring/Auditing ......................................................19 
7.5 
Adherence to the Protocol ...................................................................................19 
7.6 
Amendments to the Protocol ...............................................................................21 
7.7 
Record Retention .................................................................................................21 
7.8 
Obligations of Investigators ................................................................................21 
8.0 
REFERENCES ......................................................................................................22 

LCCC 1745 
 
CONFIDENTIAL 
PI: Gallippi 
 
UNIVERSITY OF NORTH CAROLINA 
Version: February 21, 2018 
 
 
1 
1.0 
BACKGROUND AND RATIONALE 
1.1 
Study Synopsis 
The primary objective of the proposed research is to evaluate in vivo the 
diagnostic  relevance of ultrasound-derived metrics for stiffness, elasticity, 
viscosity, and anisotropy. These biomarkers will be measured using novel, 
noninvasive ultrasound technologies under development in Dr. Gallippi’s 
laboratory: 1) Acoustic Radiation Force Impulse  (ARFI) ultrasound for 
interrogating tissue stiffness, 2) Viscoelastic Response (VisR)  ultrasound for 
assessing tissue elasticity and viscosity, and 3) Dynamic Displacement 
Anisotropy Imaging (DDAI) for measuring tissue anisotropy.  
 
We hypothesize that ultrasound-derived stiffness, elasticity, viscosity, and 
anisotropy will detect lesion malignancy and predict response to treatment. To 
test this hypothesis, we will pursue the following specific aims: 
 
Aim #1: Quantify the ability of ultrasound-derived stiffness, elasticity, 
viscosity, and anisotropy to detect malignancy. ARFI, VisR, and DDAI 
imaging will be performed on suspicious breast lesions in 40 women with 
BIRADS-4a, 4b, 4c, or -5 ratings. The diagnostic accuracy of imaging metrics 
will analyzed, with malignancy confirmed by histology as the outcome. 
 
Aim #2: Quantify the ability of ultrasound-derived stiffness, elasticity, 
viscosity, and anisotropy to predict a positive response to treatment. ARFI, 
VisR, and DDAI imaging will be performed serially over the course of 
neoadjuvant chemotherapy (NAC) - on malignant breast lesions in 40 women. 
Changes in outcome metrics over time will be correlated to overall reduction in 
tumor size (diameter and area). The ability of ultrasound metrics to predict a 
positive response to treatment will be examined.  A positive response to treatment 
will be determined according to the Response Evaluation Criteria in Solid Tumors 
(RECIST) guidelines.1,2 
1.2 
Disease Background (if applicable) 
The primary objective of breast cancer screening is to identify early stage cancer, 
or precancerous lesions, at a time before symptoms emerge and when treatment is 
likely to result in a cure. Screening is beneficial when it averts progression of 
disease to metastasis and/or death, but adverse effects to patients (and 
unnecessary medical expense) may result downstream from false positives and 
indiscrimination of masses that will not respond to treatment. The sensitivity of 
digital mammography, the current screening standard in the US, has been reported 
in the range of 0.40 to 0.85,1 with a positive predictive value of 0.31.2 Sensitivity 
is increased by augmenting mammography with MRI and B-Mode ultrasound, but 
false positive rates may also increase3. There exists a vital need for a screening 
technology that exhibits high sensitivity and specificity for cancer detection with 
early identification of unresponsive masses.4  

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This urgent need could be met by exploiting new imaging biomarkers. 
Specifically, the mechanical properties of breast tissue have been used for cancer 
detection, with both elasticity5–8 and viscosity9 demonstrated for discriminating 
malignant from benign lesions. Further, tissue anisotropy has been shown to 
correlate with core biopsy result and tumor grade, with large cancers significantly 
more anisotropic than small cancers.10 Importantly, while both MRI and 
ultrasound can be used to measure these biomarkers, ultrasound’s cost 
effectiveness and ease of implementation render it an efficient platform to pursue. 
1.3 
Diagnostic and Prognostic Studies 
As stated above, we hypothesize that ultrasound-derived tissue stiffness, elasticity, 
viscosity, and anisotropy will detect lesion malignancy and predict response to 
treatment.  Tissue property measures will be made using Acoustic Radiation 
Force Impulse (ARFI), Viscoelastic Response (VisR), and Dynamic Displacement 
Anisotropy Imaging (DDAI), which are described below: 
 
Acoustic Radiation Force Impulse (ARFI) Imaging for Assessing Tissue 
Stiffness: In ARFI imaging, the ultrasound transducer delivers an impulsive ARF 
excitation to the tissue near the position of the imaging focus, and, using the same 
transducer, the induced tissue displacement is monitored over time. Then, 
parametric images of ARFI-induced peak displacement (PD) are rendered to 
convey information regarding the relative stiffness of tissue, i.e. stiffer tissues 
achieve PDs that are smaller than those of softer tissues in response to a consistent 
ARF magnitude.11–14  ARFI PD has been used by our group to characterize 
atherosclerotic plaques and delineate necrotic cores and fibrous caps. Fibrous cap 
thickness measurement ≥ 0.5 mm has been demonstrated to be within 10% error 
of histologically derived measurements. This, and previous work by others,15–21 
suggest that ARFI will be similarly relevant for delineating stiffness differences in 
the breast that will be relevant for detecting necrotic cores, which could aid in 
discriminating malignant lesions and masses that do not respond to treatment.  
 
Viscoelastic Response (VisR) Ultrasound for Assessing Tissue Elasticity and 
Viscosity: Unlike ARFI, which primarily assesses tissue stiffness, VisR uses 
successive ARF excitations to interrogate tissue viscoelasticity.22–24  ViSR has 
been used to study muscle degeneration in boys with Duchenne Muscle 
Dystrophy.  The ViSR measurements provided information on the changes in 
viscosity and elasticity of these muscles and were concordant with inflections in 
matched physical and quantitative muscle testing (QMT) results. The VisR results 
are also consistent with expected cycles of dystrophic degeneration. These data 
support that VisR will similarly be relevant to assessing viscous and elastic 
properties in the breast, which have been shown to be relevant to discriminating 
malignant from benign breast lesions5,6,8,9,25–27 and may be useful for 
distinguishing responsive from unresponsive masses. 
 

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Dynamic Displacement Anisotropy Imaging (DDAI) for Measuring Tissue 
Anisotropy: Anisotropic tissue exhibits mechanical properties that are 
directionally dependent, i.e. the viscous and/or elastic properties of the material 
vary between one direction and another, and the degree of difference is referred to 
as the “degree of anisotropy.”28 Anisotropy is interrogated in DDAI imaging 
using ARF that is configured to have an asymmetrical shape.29 For example, in a 
transversely isotropic (TI) material, ARFI-induced PDs and VisR τ values are 
larger when the long axis of the asymmetrical ARF is aligned along versus across 
the material’s axis of symmetry (AoS). The ratio of induced PDs or τ values along 
versus across the AoS reflects the degree of anisotropy in the material. We have 
developed DDAI imaging using finite element method (FEM) simulations. These 
data suggest that DDAI imaging will differentiate breast lesions with anisotropic 
properties and/or AoS orientations that differ from those of the background breast 
tissue. Previous work suggests that DDAI-derived anisotropy information will 
correlate with breast core biopsy result, tumor grade, and tumor size10,30 and may 
be useful for differentiating malignant from benign lesions and responsive from 
unresponsive masses. 
 
We will correlate ultrasound-derived stiffness, elasticity, viscosity, and anisotropy 
with response to treatment (Aim #2). Please see data analysis plans below for 
more information. 
1.4 
Rationale 
In this exploratory clinical study, we will attempt to demonstrate that ARFI, VisR, 
and DDAI ultrasound reliably detect malignant breast masses (Aim #1) and 
distinguish masses that respond to chemotherapy from those that do not (Aim #2). 
The ARFI, VisR, and DDAI imaging location will be on the surface of the breast, 
above the suspicious or malignant mass. This unblinded, open-label, exploratory 
study will be conducted in 40 women with diagnosed BIRADS breast assessment 
categories of -4a, 4b, 4c,  or -5 masses in Aim #1 and in 40 women with 
malignant masses undergoing neoadjuvant chemotherapy (NAC) in Aim #2. In 
Aim #1, patients will be recruited from those who have undergone breast imaging 
and are indicated for biopsy according to the routine standard of care at UNC 
Hospitals. Both diagnostic imaging and biopsy will be performed according to 
institutional standards and will not be altered for research purposes. In addition to 
standard radiologic examinations, the patients will undergo ARFI, VisR, and 
DDAI ultrasound imaging after BIRADS diagnosis but prior to biopsy. In Aim 
#2, patients will be recruited from those already undergoing NAC for treatment of 
breast cancer according to the standard of care at UNC Hospitals. NAC will be 
performed based on institutional standards and will not be altered for research 
purposes, except that we will perform serial ultrasound imaging over the course of 
treatment. The standard for clinical management during and after NAC will not be 
altered. 
 
We will enroll participants in two groups, as patients with suspicious (Aim #1) 
breast lesions, or as patients with confirmed breast cancer undergoing clinically 

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CONFIDENTIAL 
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UNIVERSITY OF NORTH CAROLINA 
Version: February 21, 2018 
 
 
4 
indicated NAC (Aim #2). There will be no randomization to study arms and no 
intervention beyond ARFI, VisR, and DDAI ultrasound imaging. 
1.5 
Compliance Statement 
This study will be conducted in full accordance to all applicable University of 
North Carolina (UNC) Research Policies and Procedures and all applicable 
Federal and state laws and regulations including 45 CFR 46, and the Health 
Insurance Portability and Accountability Act (HIPAA) Privacy Rule. Any episode 
of noncompliance will be documented. 
 
The investigators will perform the study in accordance with this protocol, will 
obtain consent and assent (unless a waiver is granted), and will report unexpected 
problems in accordance with The UNC IRB Policies and Procedures and all 
federal requirements. Collection, recording, and reporting of data will be accurate 
and will ensure the privacy, health, and welfare of research subjects during and 
after the study.  
2.0 
STUDY OBJECTIVES 
The purpose of this study is to evaluate in vivo the diagnostic relevance of 
ultrasound-derived stiffness, elasticity, viscosity, and anisotropy metrics for breast 
lesion malignancy and response to treatment. 
2.1 
Aim #1: Quantify the ability of ultrasound-derived stiffness, elasticity, 
viscosity, and anisotropy to detect malignancy.   
In vivo ARFI, VisR, and DDAI imaging will be performed in 40 women with a 
breast mass with a BIRADS breast assessment categories of -4a, 4b, 4c, or -5 
rating after diagnostic workup. First, the transducer will be placed lightly above 
the suspicious mass, just to the point of skin contact, to avoid pre-compression 
during imaging.15 The suspicious mass will be positioned at 10 to 40 mm in axial 
depth, with standoff used if needed to achieve the desired axial position. Second, 
the position of the transducer will be rotated through 90o in steps of 30o to identify 
the transducer position yielding maximum ARFI peak displacement in the mass. 
The transducer will be stabilized in this position, with light skin contact, by the 
sonographer or by using a stereotactic clamp. Third, ARFI and VisR data will be 
acquired in immediate succession. Fourth, the transducer will be rotated 90o, re-
stabilized by the sonographer or by using the stereotactic clamp, and ARFI and 
VisR data will be acquired in the new transducer position to enable DDAI 
analysis. Finally, the evaluated mass will undergo clinically indicated biopsy with 
histological evaluation to determine if it is benign or malignant.  
 
The ultrasound data will be processed to calculate the following outcome metrics: 
peak displacement (PD), displacement at a given time (TD) for ARFI; τ, relative 
elasticity (RE), relative viscosity (RV), and tissue mass (TM) for VisR; ratio of 
ARFI PD and ratio of VisR RE values at 90o versus 0o transducer orientations for 
DDAI. These eight outcome metrics, alone and in combinations, will be 
statistically correlated with biopsy finding of malignant or benign. 

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2.1.1 Determine which of the 8 ultrasound metrics best detects malignancy.  
We will statistically evaluate all eight ultrasound outcome metrics independently 
to determine which metric best detects malignant finding. 
2.1.2 Determine the combined diagnostic accuracy of any 2 ultrasound metrics. 
We will systematically combine all possible groupings of two ultrasound outcome 
metrics. We will then determine which grouping of metrics best detects malignant 
finding. 
2.1.3 Determine the combined diagnostic accuracy of ultrasound metrics and 
mammography.  
For ultrasound outcome metrics (singly or combined) with AUC exceeding 0.75 
in 2.1.1 and 2.1.2, we will determine if combining the metrics with 
mammography-derived mass size, density, shape, and margins improves ROC 
performance relative to not combining with mammography. 
2.1.4 Determine which cutpoints for ultrasound metrics detect malignancy with 
optimum sensitivity and specificity.  
Best-balanced cutpoints from the highest performing ultrasound metrics in 2.1.1 
will be derived, and PPV will be calculated.   
2.2 
Aim #2: Quantify the ability of ultrasound-derived stiffness, elasticity, 
viscosity, and anisotropy to predict a positive response to treatment.  
In 40 women with newly diagnosed invasive breast cancer who are scheduled to 
begin clinically indicated NAC, ARFI, VisR, and DDAI imaging will be 
performed (as described in Aim #1) immediately before beginning treatment and 
then over the course of treatment. The eight ARFI, VisR, and DDAI outcome 
metrics described in Aim #1 will be statistically correlated with clinically 
determined change in tumor size following NAC treatment. ARFI, VisR, and 
DDAI outcome metrics will also be evaluated for ability to predict positive 
response to NAC, with positive response determined according to the RECIST 
guidelines.1,2    
2.2.1 Determine which baseline ultrasound metric best predicts positive response 
to NAC.  
We will statistically determine, from among all eight ultrasound outcome metrics 
acquired at each imaging time point, which metric best predicts positive response 
to treatment, with positive response determined according to the RECIST 
guidelines.1,2 
2.2.2 Determine which baseline ultrasound metric correlates most strongly with 
absolute reduction in tumor size.  
We will correlate absolute change in tumor size (diameter and area) from baseline 
to end of chemotherapy type with ultrasound metric values at baseline. 

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2.2.3 Determine the combined ability of any 2 baseline ultrasound metrics for 
prediction of positive response to NAC.  
We will systematically combine all possible groupings of any two ultrasound 
outcome metric values at baseline. We will then statistically analyze the combined 
ability to predict reduction in tumor size. 
2.2.4 Determine if serial changes in ultrasound metrics correlate with positive 
response to NAC.  
We will evaluate the correlation of serial changes in ultrasound metrics with 
tumor size. Assessments of serial change will be made at 3 intervals per 
chemotherapy type: baseline, early chemotherapy type treatment, and end of 
chemotherapy type treatment. 
2.2.5 Determine for which of the 8 ultrasound metrics do serial changes best 
predict positive response to NAC.  
The predictive ability of serial change in each ultrasound metric from baseline to 
end of chemotherapy type will be analyzed statistically. We will systematically 
combine all possible groupings of serial changes in any two ultrasound outcome 
metrics. We will then statistically analyze the combined ability to predict positive 
response to NAC. 
2.2.6 Determine if combinations of changes over time in any ultrasound outcome 
metrics correlate with positive response to NAC.  
We will systematically combine all possible groupings of changes over time in 
any two ultrasound outcome metrics. We will then statistically analyze the 
combined ability to predict positive response to NAC. 
2.2.7 Determine if combinations of serial change for any 2 ultrasound metrics 
predict positive response to NAC.  
We will assess the predictive ability of serial change in ultrasound metrics from 
baseline to end of chemotherapy type treatment using ROC analysis. 
2.2.8 Determine the combined performance of ultrasound metrics and 
mammography for predicting a positive response to NAC.  
For ultrasound outcome metrics with an AUC exceeding 0.75 from 2.2.1, 2.2.3, 
2.2.5, and 2.2.7, their combined diagnostic accuracy with mammography 
measures (mass size, density, shape, and margins) will be analyzed statistically. 
2.2.9 Determine the sensitivity, specificity and PPV of ultrasound outcome metrics 
to predict positive response to NAC.  
Best-balanced cutpoints from the highest performing ultrasound metrics in 2.2.1 
and 2.2.4 will be derived used to analyze sensitivity, specificity, and PPV of 
reduction in tumor size. 

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2.3 
Endpoints 
Primary endpoints are ARFI PD and TD, VisR τ, RE, RV and RM, and DDAI 
degree of anisotropy measured as the ratio of PD and as the ratio of RE values. 
The secondary endpoints are changes in these eight ultrasound-derived metrics 
over time. These primary and secondary endpoints will be used for Aims #1 and 
#2 objectives listed above.  
3.0 
PATIENT ELIGIBILITY 
In Aim #1, patients will be recruited from those who have undergone breast 
imaging and are indicated for biopsy according to the routine standard of care at 
UNC Hospitals. Both diagnostic imaging and biopsy will be performed according 
to institutional standards and will not be altered for research purposes. In addition 
to standard radiologic examinations, the patients will undergo ARFI, VisR, and 
DDAI ultrasound imaging prior to standard of care biopsy.. 
 
In Aim #2, patients will be recruited from those already undergoing NAC for 
treatment of breast cancer according to the standard of care at UNC Hospitals. 
NAC will be performed based on institutional standards and will not be altered for 
research purposes, except that we will perform serial ultrasound imaging over the 
course of treatment. The standard for clinical management during and after NAC 
will not be altered. 
 
Potential study participants will be patients 30-90 years of age who have been 
diagnosed by their treating physician to have a BIRADS breast assessment 
categories of -4a, 4b, 4c, or -5 breast lesion (Aim #1) or to be in need of NAC for 
the treatment of breast cancer (Aim #2). All patients will be recruited from and 
imaged at the University of North Carolina Hospitals. Based on local 
demographics, we estimate that roughly 20% will be racial and ethnic minorities. 
Children are not likely to have breast cancer and will not be included in this study. 
 
We will enroll participants in two groups, as patients with suspicious (Aim #1) 
breast lesions, or as patients with confirmed breast cancer undergoing clinically 
indicated NAC (Aim #2). There will be no randomization to study arms and no 
intervention beyond ARFI, VisR, and DDAI ultrasound imaging. 
3.1 
Inclusion Criteria 
Subjects must meet all of the inclusion criteria for either Aim 1 or Aim 2 in order 
to participate in this study. 
3.1.1 Aim 1 
3.1.1.1 Patients are aged 30-90 years  
3.1.1.2 Patients with BIRADS 4a, 4b, 4c, or 5 rating 
3.1.1.3 Lesion is sonographically visible with B-Mode ultrasound on diagnostic workup 

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3.1.1.4 Informed consent obtained and signed 
3.1.2 Aim 2  
3.1.2.1 Patients are aged 30-90 years 
3.1.2.2 Patients who are or will be undergoing neoadjuvant chemotherapy (NAC) for 
TNM staging of T1-T4, N0-N3 and M0-M1 
3.1.2.3 Lesion is sonographically visible with B-Mode ultrasound on diagnostic workup 
3.1.2.4 Informed consent obtained and signed 
3.2 
Exclusion Criteria 
Subjects who meet any of the exclusion criteria will be excluded from study 
participation for both Aim 1 and Aim 2. (With the exception for Aim 2 – 3.2.5, 
since those subjects would have had a biopsy-proven malignant lesion). 
3.2.1 Inability to provide informed consent 
3.2.2 Inability to communicate in English 
3.2.3 Inability to remain motionless for 15 minutes 
3.2.4 Suspicious or malignant breast mass deeper than 3 cm from skin surface 
3.2.5 Aim 1 - Previous biopsy or surgery to the site of the suspicious or malignant 
mass. Aim 2 – Surgical excision of biopsy-proven lesion of interest. 
3.2.6 Patients who are pregnant 
3.2.7 Patients who are lactating   
3.2.8 Patients with a history of mastectomy  
3.2.9 Patients with breast implants   
 

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4.0 
STUDY PLAN 
4.1 
Schema  
 
 
4.2 
Imaging Schedule 
Study enrollment will occur for approximately 18 months, with anticipated 
recruitment of 4-5 patients per month.  Total ultrasonic imaging time for all 
imaging conditions (ARFI, VisR, DDAI) is approximately 15 minutes.  Therefore, 
Subjects participating in Aim 1 of the study will participate for an expected 
duration of 15 minutes.  Subjects participating in Aim 2 will be participating for 
an expected duration of approximately 15 minutes per imaging session during the 
duration of their NAC treatment as outlined in Section 4.2.2. 
4.2.1 Arm 1 
In addition to standard radiologic examinations, the patients will undergo ARFI, 
VisR, and DDAI ultrasound imaging after BIRADS diagnosis but prior to biopsy. 
Screening and Enrollement
Collection of ARFI, VisR, and DDAI 
Ultrasound on breast lesion
Data Analysis
(compare ultrasound outcomes to clinical 
biopsy finding or change in lesion size)

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4.2.2 Arm 2 
Patients in Arm 2 will be imaged to align with their clinical treatment schedule. Imaging will occur at baseline, at 2 – 3 weeks 
(+/- 7 days) into therapy (driven by treatment plan), and at completion of each chemotherapy type.  In some cases, the final US 
of one chemotherapy type will also serve as the baseline for the next regimen. The below schedule has been included for 
clarity and as an example. Patients undergoing any NAC regimen will be included and their imaging will align with their 
treatment at baseline, early treatment by chemotherapy type, and the end of each chemotherapy type.  In the event that subjects 
undergo more than 3 rounds of chemotherapy, an additional scan will be completed at the end of all chemotherapy.  At 
minimum, patients would undergo 3 US scans, and at maximum of 8 US scans.  
  
Baseline 
Chemotherapy 
Type 1 
Early 
Chemotherapy 
Type 1 
Completion of 
Chemotherapy 
Type 1/ 
Baseline 
Chemotherapy 
Type 2 
Early 
Chemotherapy 
Type 2 
Completion of 
Chemotherapy 
Type 2/ 
Baseline 
Chemotherapy 
Type 3 
Early 
Chemotherapy 
Type 3 
Completion of 
Chemotherapy 
Type 3 
ER+/HER2- day 0 
day 14 (+/-7 
days) 
end of 
chemotherapy 
type (+/- 7 
days) 
day 14 (+/-7 
days) 
end of 
chemotherapy 
type (+/- 7 
days) 
day 14 (+/-7 
days) 
end of 
chemotherapy 
type (+/- 7 
days) 
TNBC 
day 0 
day 14 (+/-7 
days) 
end of 
chemotherapy 
type (+/- 7 
days) 
day 21 (+/-7 
days) 
end of 
chemotherapy 
type (+/- 7 
days) 
day 14 (+/-7 
days) 
end of 
chemotherapy 
type (+/- 7 
days) 
HER2+ 
day 0 
day 14 or 21 
(+/-7 days) 
depending on 
treatment 
schedule 
end of 
chemotherapy 
type (+/- 7 
days) 
day 21 (+/-7 
days) 
end of 
chemotherapy 
type (+/- 7 
days) 
N/A 
N/A 

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4.3 
Patient Identification and Consent 
Informed consent will occur prior to participants receiving any sedation for 
biopsy or NAC, should it be needed. The informed consent will be a written 
document explaining procedures and risks of the study. The Investigator will 
review the consent form with the subject and will answer any questions potential 
participants might have. Once all questions have been answered, the subject will 
be asked if they still want to participate, and if so, to sign the consent and HIPAA 
documents. 
4.4 
Abstraction of Medical Records (if applicable) 
Patient’s medical record will be reviewed to assess response to neoadjuvant 
chemotherapy. We will review each patient’s clinical records, including their 
pathology report from biopsy and information about treatment.  Malignancy will 
be determined as indicated by the pathology report.   
4.5 
Disease Progression 
If signs of progression are captured during study assessments, the patient and her 
medical oncologist (with the patient’s permission) will be notified. The non-FDA 
approved research images cannot be assumed to be of clinical quality and, 
therefore, cannot substitute for a clinical evaluation. Subjects experiencing 
symptoms for which clinical imaging may be appropriate will be advised to see 
their treating physician. 
5.0 
EXPECTED RISKS/UNANTICIPATED PROBLEMS/SAFETY 
MONITORING 
5.1 
Assessment of Safety 
One aspect of this study outside the usual standard of care is the potential 
complication associated with the application of higher intensity ultrasound than is 
used in routine diagnostic examinations. If applied for extended time periods, 
these high-intensity beams could cause tissue damage due to excessive heating. 
The FDA considers thermal increases less than 6 ºC in soft tissue to be safe.31 
Therefore, the beam sequences and timing of data acquisition used for this study 
will be designed to ensure that the cumulative temperature increase does not 
exceed 2ºC, to further reduce patient risk and exposure. This is a research tool 
developed for academic purposes under the direction of Dr. Caterina Gallippi and 
is neither for commercial purposes nor under the direction of Siemens, the 
manufacturer of the ultrasound equipment.  
 
The use of ARFI, VisR, and DDAI technology will build on experience from 
previous clinical studies conducted at UNC Chapel Hill (Gallippi, PI). IRB review 
designated these research ultrasound technologies as minimal risk and qualifying 
for abbreviated IDE (exempt from FDA review). No adverse events occurred. Our 
proposed research study is similar in nature to these trials, but if the IRB review 
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be submitted at that time. Other acoustic radiation force (ARF)-based imaging 
methods are approved for clinical practice in Europe and in the United States. 
Overall, the potential risks to participants would be categorized as minimal. 
Participants will be informed of these risks at the time of informed consent. 
 
Given that the mechanical stresses during ARFI imaging are significantly less 
than those routinely experienced during both physiological arterial pulsation32 and 
normal clinical evaluation by palpation, we believe this protocol poses no 
significant danger to the patient. Unexpected, unusual, or serious complications, 
or an unanticipated frequency of reactions during the course of the clinical 
investigation, will be reported immediately to the IRB. 
 
The Principal Investigator, Co-Investigators, and Study Coordinators will have 
access to real time data on the rate of complications. If there is any increase in the 
rate of complications, we will review the entire protocol to seek solutions. Since 
the only addition to routine patient care associated with this study is ultrasound 
imaging that is believed to pose no significant risk to the patient, the chance of 
this happening is minimal. Any adverse events or unanticipated problems will be 
reported to the LCCC and the UNC IRB immediately. 
5.2 
Expected Risks (include Applicable Sections) 
5.2.1 Patient Confidentiality 
Identifiable information, which will be separated from research data, will be 
retained in a locked file cabinet, in a locked office, and password protected 
indefinitely. When the PI (Dr. Gallippi) determines that the identifiable 
information is no longer needed, it will be shredded (hard copy) and securely 
deleted (electronic). To achieve separation of identifiers from research data, all 
patient volunteers will be assigned unique study identification (ID) number at the 
time of enrollment. All acquired data will be labeled with the study ID number, 
and no personal information will be stored with the collected data. The master list 
linking study ID numbers to personal information will be secured by lock (hard-
copy) and password (electronic copy) in Dr. Gallippi’s laboratory in the UNC-CH 
Joint Department of Biomedical Engineering. Dr. Gallippi’s computational work 
station is password-protected and locked in her research laboratory, so 
unauthorized personnel will not have access to personal identifiers. Any data 
transmission (by hard-copy or electronically) to authorized personnel will be 
stripped of any personal identifiers. The data will be tagged by the unique 
identifier assigned to the participant at the time of enrollment only. Electronically 
recorded data will be stored with study ID number only. 
 
Except when required by law, study subjects will not be identified by name, social 
security number, address, telephone number, or any other direct personal 
identifier in study records disclosed outside of the University of North Carolina 
Hospitals, University of N.C. at Chapel Hill School of Medicine, and 
Departments of Radiology, Biomedical Engineering, and Oncology. No subject 
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volunteers will be assigned unique study identification (ID) number at the time of 
enrollment. All acquired data will be labeled with the study ID number, and no 
personal information will be stored with the collected data. The master list linking 
study ID numbers to personal information will be secured by lock (hard-copy) and 
password (electronic copy) in Dr. Gallippi’s laboratory in the UNC-CH Joint 
Department of Biomedical Engineering. Dr. Gallippi’s computational work 
station is password-protected and locked in her research laboratory, so 
unauthorized personnel will not have access to personal identifiers. 
5.3 
UNANTICIPATED CONCERNS 
5.4 
Unanticipated Adverse Device Effect (UADE) 
The investigational device exemption (IDE) regulations define an unanticipated 
adverse device effect (UADE) as “any serious adverse effect on health or safety 
or any life-threatening problem or death caused by, or associated with, a device, if 
that effect, problem, or death was not previously identified in nature, severity, or 
degree of incidence in the investigational plan or application (including a 
supplementary plan or application), or any other unanticipated serious problem 
associated with a device that relates to the rights, safety, or welfare of subjects” 
(21 CFR 812.3(s)). 
5.5 
Unanticipated Problems (UP) 
As defined by UNC’s IRB, unanticipated problems involving risks to study 
subjects refers to any incident, experience, or outcome that: 
• Is unexpected (in terms of nature, severity, or frequency) given (a) the 
research procedures that are described in the protocol-related documents, 
such as the IRB-approved research protocol and informed consent 
document; and (b) the characteristics of the subject population being 
studied; 
• Is related or possibly related to a subject’s participation in the research; 
and  
• Suggests that the research places subjects or others at a greater risk of 
harm (including physical, psychological, economic, or social harm) related 
to the research than was previously known or recognized. 
5.6 
Reporting 
5.6.1 UADEs 
UADEs must be reported by the clinical investigator to the sponsor and the 
reviewing IRB, as described below:  
 
For this device study, investigators are required to submit a report of a UADE to 
the FDA, the manufacturer of the device and the UNC IRB as soon as possible, 
but in no event later than 10 working days after the investigator first learns of the 
event (§ 812.150(a)(1)), using the MedWatch Form 3500A.   Sponsors (LCCC) 
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the evaluation to FDA, the UNC IRB, and participating investigators within 10 
working days after the sponsor first receives notice of the effect (§§ 812.46(b), 
812.150(b)(1)).  
 
For this device study, we will submit a report of a UADE to the manufacturer and 
the IRB as soon as possible, but no later than 10 working days after the 
investigators first learn of the event. 
5.7 
UP 
Any events that meet the criteria for “Unanticipated Problems” as defined by 
UNC’s IRB must be reported by the Study Coordinator using the IRB’s web-
based reporting system.   
 
Any unanticipated problem that occurs during the conduct of this study and that 
meets at least the criteria listed in section 5.6 must be reported to the UNC IRB 
using the IRB’s web-based reporting system.   
5.8 
Data and Safety Monitoring Plan 
The Principal Investigator will provide continuous monitoring of patient safety in 
this trial with periodic reporting to the Data and Safety Monitoring Committee 
(DSMC).  
 
Meetings/teleconferences will be held at a frequency dependent on study accrual, 
and in consultation with the study Biostatistician.  These meetings will include the 
investigators as well as protocol nurses, clinical research associates, regulatory 
associates, data managers, biostatisticians, and any other relevant personnel the 
principal investigators may deem appropriate.  At these meetings, the research 
team will discuss all issues relevant to study progress, including enrollment, 
safety, regulatory, data collection, etc. 
 
The team will produce summaries or minutes of these meetings. These summaries 
will be available for inspection when requested by any of the regulatory bodies 
charged with the safety of human subjects and the integrity of data including, but 
not limited to, the oversight (Office of Human Research Ethics (OHRE) 
Biomedical IRB, the Oncology Protocol Review Committee (PRC) or the North 
Carolina TraCS Institute Data and Safety Monitoring Board (DSMB).   
 
The UNC LCCC Data and Safety Monitoring Committee (DSMC) will review the 
study on a regular (quarterly to annually) basis, with the frequency of review 
based on risk and complexity as determined by the UNC Protocol Review 
Committee.  The UNC PI will be responsible for submitting the following 
information for review: 1) safety and accrual data including the number of 
patients treated; 2) significant developments reported in the literature that may 
affect the safety of participants or the ethics of the study; 3) preliminary response 
data; and 4) summaries of team meetings that have occurred since the last report.  

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Findings of the DSMC review will be disseminated by memo to the UNC PI, 
PRC, and the UNC IRB and DSMB.   
6.0 
STATISTICAL CONSIDERATIONS 
6.1 
Study Design/Study Endpoints 
For Aims #1 and #2, the ability of ultrasound biomarkers to discriminate binary 
outcomes (malignant/bengin and responsive/unrespsonsive to treatment) will be 
analyzed using logistic regression. The area under the receiver operating 
characteristic curve (AUC) will be quantified, with separate models fit for each of 
the eight ultrasound biomarkers. Because most ultrasound biomarkers are 
continuous measures, the best-balanced cutpoints optimizing sensitivity and 
specificity will be calculated, using Youden’s Index. Outcome discrimination by 
dual biomarkers will be analyzed in 28 separate models with combinations of any 
two ultrasound biomarkers. The combined benefit of ultrasound and 
mammography will be analyzed in 32 models, by adding a single mammography 
metric (lesion size, density, shape, or margins) to each of the eight ultrasound 
biomarker models. In Aim #2, the relationship between biomarker values and 
tumor size, biomarker values and change in tumor size, and change in biomarker 
values and change in tumor size will be analyzed by linear regression. These 
correlations will be analyzed in separate models for each of the eight ultrasound 
biomarkers, in models of dual ultrasound biomarkers, and in models combining 
mammography metrics with ultrasound biomarkers.   
6.2 
Sample Size and Accrual 
A total of 80 participants will be recruited for this study. The study will be 
conducted in 40 women with diagnosed BIRADS-4 or -5 masses and in 40 
women with malignant masses undergoing neoadjuvant chemotherapy. 
 
We project 12 of the 40 women in our study will have malignant cancer, and 12 
of 40 will have masses that do not respond to NAC. Given this, we expect 80% 
power to detect a minimum AUC of 0.70 (95% CI: 0.51 – 0.89). This is consistent 
with the diagnostic accuracy previously reported for malignancy detection by 
ultrasound biomarkers, which have ranged from AUC values of 0.67 to 0.99.5,10 
6.3 
Data Analysis Plans 
 
6.3.1 Aim #1: Correlate ultrasound-derived stiffness, elasticity, viscosity, and 
anisotropy to malignancy.   
In vivo ARFI, VisR, and DDAI imaging will be performed in 40 women with a 
breast mass with a BIRADS breast assessment categories of -4a, 4b, 4c, or -5 
rating after diagnostic workup. First, the transducer will be placed lightly above 
the suspicious mass, just to the point of skin contact, to avoid pre-compression 
during imaging.15 The suspicious mass will be positioned at 10 to 40 mm in axial 
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the position of the transducer will be rotated through 90o in steps of 30o to identify 
the transducer position yielding maximum ARFI peak displacement in the mass. 
The transducer will be stabilized in this position, with light skin contact, by the 
sonographer or by using a stereotactic clamp. Third, ARFI and VisR data will be 
acquired in immediate succession. Fourth, the transducer will be rotated 90o, re-
stabilized by the sonographer or by using the stereotactic clamp, and ARFI and 
VisR data will be acquired in the new transducer position to enable DDAI 
analysis. Finally, the evaluated mass will undergo clinically indicated biopsy with 
histological evaluation to determine if it is benign or malignant.  
 
The ultrasound data will be processed to calculate the following outcome metrics: 
peak displacement (PD), displacement at a given time (TD) for ARFI; τ, relative 
elasticity (RE), relative viscosity (RV), and tissue mass (TM) for VisR; ratio of 
ARFI PD and ratio of VisR RE values at 90o versus 0o transducer orientations for 
DDAI. These eight outcome metrics, alone and in combinations, will be 
statistically correlated with biopsy finding of malignant or benign. 
6.3.1.1 Which of the 8 ultrasound metrics best detects malignancy?  
The diagnostic accuracy of each ultrasound metric will be analyzed by 
constructing separate receiver operating characteristic (ROC) curves. 
6.3.1.2 What is the combined diagnostic accuracy of any 2 ultrasound metrics?  
The incremental value of using a second ultrasound metric will be analyzed by 
constructing separate ROC curves for combinations of any 2 ultrasound metrics. 
6.3.1.3 What is the combined diagnostic accuracy of ultrasound metrics and 
mammography?  
We will select ultrasound metrics with an AUC exceeding 0.75 in C.1.a-b, and 
their combined diagnostic accuracy with mammography measures (mass size, 
density, shape, and margins) will be analyzed in separate ROC curves. 
6.3.1.4 Which cutpoints for ultrasound metrics detect malignancy with optimum 
sensitivity and specificity?  
Best-balanced cutpoints from the highest performing ultrasound metrics in 6.3.1.1 
will be derived from ROC curves, using Youden’s Index. These cutpoints will be 
used to analyze the PPV. 
6.3.2 Aim #2: Correlate ultrasound-derived stiffness, elasticity, viscosity, and 
anisotropy with response to treatment.  
In 40 women with newly diagnosed invasive breast cancer who are scheduled to 
begin clinically indicated NAC, ARFI, VisR, and DDAI imaging will be 
performed (as described in Aim #1) at baseline, early within each chemotherapy 
type, and at the conclusion of each chemotherapy type over the course of 
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6.3.2.1 Which baseline ultrasound metric best predicts positive response to NAC?  
Response to NAC will be dichotomized into a yes/no response according to the 
RECIST guidelines, and the predictive ability of each baseline ultrasound metric 
will be analyzed by constructing separate ROC curves. 
6.3.2.2 Which baseline ultrasound metric correlates most strongly with absolute 
reduction in tumor size (diameter, area, volume)?  
The relationship between ultrasound metric values at baseline and absolute 
change in tumor size from baseline to end of chemotherapy type will be 
visualized by scatterplots. If the relationship appears linear and monotonic, the 
correlation will be assessed by Pearson regression.  
6.3.2.3 What is the combined ability of any 2 baseline ultrasound metrics for 
prediction of positive response to NAC?  
With response to NAC dichotomized into a yes/no response, the combined 
predictive ability will be analyzed by constructing separate ROC curves for 
combinations of any 2 ultrasound metrics.  
6.3.2.4 Do serial changes in ultrasound metrics correlate with reduction in tumor 
size following NAC?  
The relationships between serial changes in ultrasound metrics and tumor size 
will be visualized by scatterplots. If monotonic and linear, the correlation between 
serial change in ultrasound metrics and change in tumor size (area) will be 
analyzed by Pearson regression. Assessments of serial change will be made at 3 
intervals per chemotherapy type: baseline, early chemotherapy type treatment, 
and end of chemotherapy type treatment.  
6.3.2.5 Serial change in which of the 8 ultrasound metrics best predicts positive 
response to NAC?  
Response to NAC will be dichotomized into a yes/no response, and the predictive 
ability of serial change in each ultrasound metric from 0-6 weeks will be analyzed 
by constructing separate ROC curves. 
6.3.2.6 Do combinations of changes over time in any ultrasound outcome metrics 
correlate with reduction in tumor size following NAC?  
Assuming linear relationships exists between serial change in ultrasound metrics 
and serial change in tumor size following NAC, multiple linear regression models 
will be constructed for each of the 8 ultrasound metrics. The model outcome will 
be change in tumor size, and model predictors will be serial change in any 2 
ultrasound metrics.  
6.3.2.7 Which combinations of serial change for any 2 ultrasound metrics best 
predicts positive response to NAC?  
Positive response to NAC will be dichotomized into a yes/no response, and the 
predictive ability of serial change in ultrasound metrics from baseline to end of 

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chemotherapy type will be analyzed by constructing separate ROC curves for 
combinations of any 2 ultrasound metrics.  
6.3.2.8 What is the combined performance of ultrasound metrics and 
mammography for predicting a reduction in tumor size following NAC?  
Reduction in tumor size will be dichotomized into a yes/no response. We will 
select ultrasound metrics with an AUC exceeding 0.75 in 6.3.2.1, 6.3.2.3, 6.3.2.5, 
and 6.3.2.7, and their combined diagnostic accuracy with mammography 
measures (mass size, density, shape, and margins) will be analyzed in separate 
ROC curves.  
6.3.2.9 What are the sensitivity, specificity and PPV of ultrasound outcome 
metrics for identifying masses that positively respond to treatment?  
Best-balanced cutpoints from the highest performing ultrasound metrics in 6.3.2.1 
and 6.3.2.3 will be derived from ROC curves, using Youden’s Index. These 
cutpoints will be used to analyze the PPV of positive response to NAC 
(dichotomized as a yes/no variable). 
7.0 
STUDY MANAGEMENT 
7.1 
Institutional Review Board (IRB) Approval and Consent 
It is expected that the IRB will have the proper representation and function in 
accordance with federally mandated regulations.  The IRB should approve the 
consent form and protocol. 
 
In obtaining and documenting informed consent, the investigator should comply 
with the applicable regulatory requirement(s), and should adhere to Good Clinical 
Practice (GCP) and to ethical principles that have their origin in the Declaration 
of Helsinki. 
 
 
 
Before recruitment and enrollment onto this study, the patient will be given a full 
explanation of the study and will be given the opportunity to review the consent 
form. Each consent form must include all the relevant elements currently required 
by the FDA Regulations and local or state regulations. Once this essential 
information has been provided to the patient and the investigator is assured that 
the patient understands the implications of participating in the study, the patient 
will be asked to give consent to participate in the study by signing an 
IRB-approved consent form. 
 
Prior to a patient’s participation in the trial, the written informed consent form 
should be signed and personally dated by the patient and by the person who 
conducted the informed consent discussion. 
7.2 
Required Documentation 
Before the study can be initiated at any site, the following documentation must be 
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• A copy of the official IRB approval letter for the protocol and 
informed consent 
• IRB membership list 
• CVs and medical licensure for the principal investigator and any sub-
investigators who will be involved in the study. 
• Investigator’s signature documenting understanding of the protocol 
and providing commitment that this trial will be conducted according 
to all stipulations of the protocol College of American Pathologist  
(CAP) and Clinical Laboratory Improvement Amendments (CLIA) 
Laboratory certification numbers and institution lab normal values. 
7.3 
Registration Procedures 
Patients will be registered into OnCore®, a web based clinical research platform 
by one of the Study Coordinators.  The spreadsheet contains each subject enrolled 
in the study identified by the patient first and last initial, study id, date of 
enrollment into study, race and ethnicity.  
7.4 
Data Management and Monitoring/Auditing 
All data management will be performed by the senior graduate student research 
assistant working on this project, with supervision and guidance from the study 
PI, Dr. Caterina Gallippi. Automated methods are already in place to manage data 
acquisitions and storage. First, all raw data files acquired by the Siemens 
ultrasound system are labeled by the date and time of acquisition. Next, data files 
are stored into file folders labeled according to the assigned patient participant 
number 1-100 (which in no way reflects identifiable patient information). The 
data are processed using custom software developed in Matlab and/or C++, and 
the processed data files are stored in subfolders under each patient participant 
number. Analysis of the processed data often includes image rendering as well as 
assessment of contrast; contrast-to-noise; mean, median and standard deviation of 
relevant parameter values, etc. ROC analysis will also be performed. The results 
of data analysis will be stored with appropriate labeling indicating the 
corresponding patient number. Note that data storage will occur using secure 
UNC servers that meet the current standards for PHI security. 
7.5 
Adherence to the Protocol 
Except for an emergency situation in which proper care for the protection, safety, 
and well-being of the study patient requires alternative treatment, the study shall 
be conducted exactly as described in the approved protocol.   
7.5.1 Emergency Modifications 
UNC and Affiliate investigators may implement a deviation from, or a change of, 
the protocol to eliminate an immediate hazard(s) to trial subjects without prior 
UNC or their respective institution’s IRB/IEC approval/favorable opinion.   
 

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For any such emergency modification implemented, a UNC IRB modification 
form must be completed by UNC Research Personnel within five (5) business 
days of making the change.   
7.5.2 Single Patient/Subject Exceptions 
Any request to enroll a single subject who does not meet all the eligibility criteria 
of this study requires the approval of the UNC Principal Investigator and the UNC 
IRB.  
7.5.3 Other Protocol Deviations/Violations 
According to UNC’s IRB, a protocol deviation is any unplanned variance from an 
IRB approved protocol that:  
• Is generally noted or recognized after it occurs 
• Has no substantive effect on the risks to research participants 
• Has no substantive effect on the scientific integrity of the research plan or 
the value of the data collected  
• Did not result from willful or knowing misconduct on the part of the 
investigator(s).  
 
An unplanned protocol variance is considered a violation if the variance meets 
any of the following criteria:  
• Has harmed or increased the risk of harm to one or more research 
participants. 
• Has damaged the scientific integrity of the data collected for the study. 
• Results from willful or knowing misconduct on the part of the 
investigator(s). 
• Demonstrates serious or continuing noncompliance with federal 
regulations, State laws, or University policies. 
 
If a deviation or violation occurs please follow the guidelines below: 
 
Protocol Deviations: UNC personnel will record the deviation in OnCore®, and 
report to any sponsor or data and safety monitoring committee in accordance with 
their policies.  Deviations should be summarized and reported to the IRB at the 
time of continuing review. 
 
Protocol Violations: Violations should be reported by UNC personnel within one 
(1) week of the investigator becoming aware of the event using the same IRB 
online mechanism used to report UPIRSO.   
 
Unanticipated Problems Involving Risks to Subjects or Others (UPIRSO): 
Any events that meet the criteria for “UPIRSO” as defined by UNC’s IRB must 
be reported by the Study Coordinator using the IRB’s web-based reporting 
system.   

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7.6 
Amendments to the Protocol 
Should amendments to the protocol be required, the amendments will be 
originated and documented by the Principal Investigator at UNC.  It should also 
be noted that when an amendment to the protocol substantially alters the study 
design or the potential risk to the patient, a revised consent form might be 
required.   
 
The written amendment, and if required the amended consent form, must be sent 
to UNC’s IRB for approval prior to implementation.   
7.7 
Record Retention 
Study documentation includes all eCRFs, data correction forms or queries, source 
documents, Sponsor-Investigator correspondence, monitoring logs/letters, and 
regulatory documents (e.g., protocol and amendments, IRB correspondence and 
approval, signed patient consent forms). 
 
Source documents include all recordings of observations or notations of clinical 
activities and all reports and records necessary for the evaluation and 
reconstruction of the clinical research study. 
 
Government agency regulations and directives require that all study 
documentation pertaining to the conduct of a clinical trial must be retained by the 
study investigator.  In the case of a study with a drug seeking regulatory approval 
and marketing, these documents shall be retained for at least two years after the 
last approval of marketing application in an International Conference on 
Harmonization (ICH) region.  In all other cases, study documents should be kept 
on file until three years after the completion and final study report of this 
investigational study. 
7.8 
Obligations of Investigators 
The Principal Investigator is responsible for the conduct of the clinical trial at the 
site in accordance with ethical principles originating from Title 21 of the Code of 
Federal Regulations and/or the Declaration of Helsinki, which are consistent with 
International Conference on Harmonisation (ICH)/Good Clinical Practice (GCP) 
guidelines, and in accordance with applicable local and legal requirements.  The 
Principal Investigator is responsible for personally overseeing the treatment of all 
study patients and ensuring that clinical data associated with biospecimen(s) 
collection should be used and disclosed only for research in compliance, as 
applicable, with HIPAA, with the U.S. Department of Health and Human Services 
and FDA human subjects protection regulations and with applicable State and 
local laws.  The Principal Investigator must assure that all study site personnel, 
including sub-investigators and other study staff members, adhere to the study 
protocol in accordance with the guidance and regulations listed above both during 
and after study completion. 
 

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The Principal Investigator will be responsible for assuring that all the required 
data will be collected and entered into the eCRFs. Periodically, monitoring visits 
will be conducted and the Principal Investigator will provide access to his/her 
original records to permit verification of proper entry of data. At the completion 
of the study, all eCRFs will be reviewed by the Principal Investigator and will 
require his/her final signature to verify the accuracy of the data. 
 
8.0 
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2. Kuzmiak CM. Breast Cancer Survivors: Does the Screening MRI Debate Continue? 
Acad Radiol. 2015;22(11):1329-1330. doi:10.1016/j.acra.2015.09.001. 
 
3. Berg WA, Zhang Z, Lehrer D, et al. Detection of breast cancer with addition of annual 
screening ultrasound or a single screening MRI to mammography in women with 
elevated breast cancer risk. Jama. 2012;307(13):1394-1404. doi:10.1001/jama.2012.388. 
 
4. Shieh Y, Eklund M, Sawaya GF, Black WC, Kramer BS, Esserman LJ. Population-
based screening for cancer: hope and hype. Nat Rev Clin Oncol. 2016;12. 
doi:10.1038/nrclinonc.2016.50. 
 
5. Barr RG, Nakashima K, Amy D, et al. WFUMB guidelines and recommendations for 
clinical use of ultrasound elastography: Part 2: breast. Ultrasound Med Biol. 
2015;41(5):1148-1160. doi:10.1016/j.ultrasmedbio.2015.03.008. 
 
6. Barr RG, Zhang Z. Shear-wave elastography of the breast: value of a quality measure 
and comparison with strain elastography. Radiology. 2015;275(1):45-53. 
doi:10.1148/radiol.14132404. 
 
7. Grajo JR, Barr RG. Strain elastography for prediction of breast cancer tumor grades. J 
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