Feasibility and Clinically Application of Magnetic Resonance Fingerprinting
Phase: NA
Conditions: Neurofibromatosis Type 1, Brain Tumor, Glioma
Sponsor: Case Comprehensive Cancer Center

 
 
CASE 7314, Version 11, 11/21/2016 
CASE COMPREHENSIVE CANCER CENTER 
__________________________________________________________________ 
 
STUDY NUMBER:  
 
CASE 7314 
 
STUDY TITLE:  
 
Feasibility and Clinical Application of Magnetic Resonance 
Fingerprinting 
  
PRINCIPAL INVESTIGATORS: 
Deborah Rukin Gold, MD 
 
 
Department of  Pediatrics 
 
 
University Hospitals Cleveland Medical Center 
 
Case Western Reserve University 
 
 
 
 
11100 Euclid Avenue  
 
 
 
 
Cleveland, OH   44106 
 
 
 
 
 
(216) 844-3691 
 
 
CO- INVESTIGATORS: 
 
Mark Griswold, PhD 
 
 
Department of  Radiology 
 
 
 
University Hospitals Case Medical Center 
 
Case Western Reserve University 
 
 
 
 
11100 Euclid Avenue  
 
 
 
Cleveland, OH   44106 
 
 
 
 
 
 
 
Andrew Sloan, MD 
 
 
 
 
Director, Brain Tumor & Neuro-Oncology Center 
 
 
Vice Chair, Department of Neurological Surgery 
 
Case Western Reserve University 
 
 
 
 
11100 Euclid Avenue  
 
 
 
 
 
Cleveland, OH   44106 
 
 
 
 
Vikas Gulani, MD, PhD 
 
 
 
 
Department of  Radiology 
 
 
 
 
University Hospitals Case Medical Center 
 
Case Western Reserve University 
 
 
 
 
11100 Euclid Avenue  
 
 
 
 
 
Cleveland, OH   44106 
 
 
 
 
Chaitra Badve, MD 
 
 
 
 
Department of  Radiology 
 
 
 
 
University Hospitals Case Medical Center 
 
Case Western Reserve University 
 
 
 
 
11100 Euclid Avenue  
 
 
 
 
 
Cleveland, OH   44106 
 
 
 
 
Jeffrey Sunshine, MD  
 
 
 
Department of  Radiology 
 
 
 
 
University Hospitals Case Medical Center 
 
Case Western Reserve University 
 
 
 

 
 
 
 
 
CASE 7314, Version 11, 11/21/2016 
 
2 
 
11100 Euclid Avenue  
 
 
 
 
Cleveland, OH   44106 
 
 
 
                                                            Barbara Bangert, MD 
 
 
Department of Radiology 
 
 
University Hospitals Case Medical Center 
 
 
Case Western Reserve University 
 
 
11100 Euclid Avenue 
 
 
Cleveland, OH 44106 
                                                                          
                                                                         Sara Dastmalchian, MD 
                                                             Department of Radiology 
 
 
 University Hospitals Case Medical Center 
                                                             Case Western Reserve University 
 
 
 11100 Euclid Avenue 
 
 
 Cleveland, OH 44106 
                                                                        
                                                                        Duncan Stearns, MD  
 
 
 
Department of Pediatrics 
 
 
Division of Hematology & Oncology 
 
 
 
University Hospitals Case Medical Center 
 
Case Western Reserve University 
 
 
 
 
11100 Euclid Avenue  
 
 
 
 
 
Cleveland, OH   44106 
 
 
 
 
Deborah Rukin Gold, MD 
 
 
 
 
Department of Pediatrics 
 
 
Division of Neurology 
 
 
 
 
University Hospitals Case Medical Center 
 
Case Western Reserve University 
 
 
 
 
11100 Euclid Avenue  
 
 
 
 
 
Cleveland, OH   44106 
 
 
 
 
Joshua Batesole 
 
 
Department of Radiology 
 
 
University Hospitals Case Medical Center 
 
 
Case Western Reserve University 
 
 
11100 Euclid Ave.  
 
       Cleveland, OH 44106  
  
 
 
 
 
 
Krystal Tomei, MD 
 
 
 
 
Department of Surgery 
 
 
Division of Neurosurgery 
 
 
University Hospitals Case Medical Center 
 
Case Western Reserve University 
 
 
 
 
11100 Euclid Avenue  
 
 
 
 
 
Cleveland, OH   44106 
 

 
 
 
 
 
CASE 7314, Version 11, 11/21/2016 
 
3 
STATISTICIAN: 
 
Jill Barnholtz-Sloan, PhD 
 
 
Case Comprehensive Cancer Center 
 
 
CWRU School of Medicine 
 
 
2-525 Wolstein Research Bldg,  
 
 
2103 Cornell Rd. 
 
 
Cleveland, OH 44106 
 
STATISTICIAN: 
 
Mark Schluchter, PhD 
 
 
Biostatistics Core Facility 
 
 
Case Comprehensive Cancer Center 
 
11100 Euclid Avenue  
 
 
 
 
 
Cleveland, OH   44106 
 
 
 
Tel: (216) 368-2651 
 
 
CLINICAL FACILITY: 
 
Seidman Cancer Center, 
 
 
University Hospitals Case Medical Center 
 
 
 
STUDY COORDINATOR: 
 
Sally Muehle 
 
 
University Hospitals Case Medical Center 
 
 
Case Western Reserve University 
 
 
11100 Euclid Avenue 
 
 
Cleveland, OH 44106-5065 
 
RESEARCH NURSE: 
 
Kelly Laschinger, CNP 
 
 
University Hospitals Case Medical Center 
 
 
Case Western Reserve University 
 
 
11100 Euclid Avenue 
 
 
Cleveland, OH 44106-5065 
                                                                         
                                                                         Jordonna Fulop, RN 
                                                             Department of Neurology 
                                                                         Case Comprehensive Cancer Center 
                                                                         Wolstein Research Bldg. 
                                                             2103 Cornell Rd. Rm. 1200 
                                                             Cleveland, OH 44106 
 
                                                             Karen Devine, RN  
                                                             Department of Neurology 
                                                                         Case Comprehensive Cancer Center 
                                                             W.O. Walker Bldg., Floor 3, WG72 
                                                             10524 Euclid Avenue 
                                                             Cleveland, OH 44106 
 
APPROVALS: 
 
Protocol Review and Monitoring Committee: 10/31/14 
 
 
UHCMC IRB: 2/2/2015 
 

 
 
 
 
 
CASE 7314, Version 11, 11/21/2016 
 
4 
TABLE OF CONTENTS 
 
 
 
        
 
1.0 
Background and Rationale 
 
 
 
 
 
 
 
5 
 
 
 
2.0 
Objectives 
 
 
 
 
 
 
 
 
 
7 
 
3.0  
Selection of Patients/Samples  
 
 
 
 
 
 
7 
 
4.0 
Registration Procedures and Consent  
 
 
 
 
 
8 
 
5.0 
Research Plan  
 
 
 
 
 
 
 
 
8 
 
6.0  
Study Parameters 
 
 
 
 
 
 
 
 
11 
 
7.0 
Statistical Considerations 
 
 
 
 
 
 
 
12 
 
8.0 
Data/Records to be kept 
 
 
 
 
 
 
 
13 
 
 
 
 
 
 
9.0 
Financial Considerations 
 
 
 
 
 
 
 
15 
 
10.0 
References 
 
 
 
 
 
 
 
 
 
15 
 
 
 
Table of Acronyms 
MRF 
Magnetic Resonance Fingerprinting 
DTI 
Diffusion Tensor Imaging 
MRI 
Magnetic Resonance Imaging 
OPG 
Optic Pathway Glioma 
NF1 
Neurofibromatosis type 1 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
CASE 7314, Version 11, 11/21/2016 
 
5 
1.0 BACKGROUND AND RATIONALE  
 
Although recent therapeutic advances have improved survival in pediatric neuro-oncology, 
surveillance of brain tumors remains problematic.  In young children, imaging surveillance often 
requires sedation which exposes children to additional risk.  In addition, the interpretation of 
traditional MR imaging involves the qualitative comparison of relative T1 and T2 signal 
intensity which can lead to uncertainty, misdiagnosis and additional surveillance imaging.  New 
imaging techniques that decrease scan time and allow for quantitative measurement of MR 
parameters could minimize the need for sedation and improve diagnostic certainty in children 
with brain tumors. 
 
Our group (Griswold lab, co-investigator) 
has pioneered a novel magnetic resonance 
technique that may be an important 
noninvasive biomarker for pediatric brain 
tumors.1 Magnetic resonance fingerprinting 
(MRF) uses pseudo-randomized variation 
in acquisition parameters to generate a 
multi-parametric data signal that can be 
compared to signal patterns calculated 
from all possible combinations of 
parameters of interest. The closest match in 
signal patterns yields the parameters used 
to calculate the theoretical signal, in each 
voxel, and thus a map of all parameters of 
interest for that tissue. This process is 
analogous to matching fingerprints to a 
database where a match in fingerprint to 
one from a database yields a vast array of 
uncollected information (for example 
name, address, date of birth, etc.) stored in 
the database. MRF data can be used to 
reconstruct high resolution parameter maps in a fraction of the time required by traditional MRI 
(Figure 1).  Significantly, these images are robust to movement artifact1, making them an ideal 
choice in children with minimal or no sedation.   
 
In addition, MRF may have unique clinical applications in pediatric and AYA brain tumors.  
While the interpretation of traditional MR sequences depends on the qualitative comparison of 
relative T1 and T2 signal intensity, MRF can rapidly quantify T1, T2, M0 and off-resonance 
signals to aid in tissue identification and delineation. Preliminary work from our group 
demonstrates that MRF can identify brain tumors and brain metastases from surrounding healthy 
tissue and differentiate these pathologies from one another, and has been useful in a series of 
brain tumors in suggesting a diagnosis prior to resection. 
 
MRF is able to distinguish tumor from surrounding healthy tissue. In a series of fourteen subjects 
with pathologically defined intra-axial brain tumors (7 high grade gliomas, 4 low grade gliomas 
Figure  1:  Traditional MRI sequences show a 
solid, centrally necrotic mass in medial right 
occipital lobe later shown to be metastatic 
adenocarcinoma. MRF-derived T1 map 
describes a similar lesion with greater extent of 
signal abnormality. 

 
 
 
 
 
CASE 7314, Version 11, 11/21/2016 
 
6 
and 3 metastases), mean T1 and T2 values derived from MRF were significantly different from 
contralateral white matter (1890 ± 170 ms and 219 ± 60 ms vs. 982 ± 77 ms and 77 ±13 ms, 
p=3.8x10-7 and p=2.4x10-7, respectively, Figure 2A). While traditional MR sequences are 
limited in their quantitation of tumor involvement, MRF has the potential to distinguish tumor 
grade and identify tumor infiltration more sensitively. MRF quantitation of T2 values was 
significantly different between low grade and high grade gliomas (p<0.04, Figure 2B). In 
addition, perilesional white matter T1 and T2 values were compared to contralateral white matter 
and lesional values. T1 and T2 values of perilesional white matter were less than those of 
lesional tissue, but more than contralateral white matter. The difference in T1 and T2 values 
between peritumoral white matter and contralateral white matter was statistically significant 
(p<0.0002, p<0.0001, respectively), suggesting that tumor infiltration or peritumoral edema may 
be easily quantified with this method.  
 
 
Prior experience with MR relaxometry.  MRF represents a novel technique to simultaneously 
acquire relaxation times (T1 and T2) as well as other MR parameters (such as M0 and off 
resonance) in a fast MR sequence.  Unlike traditional methods of measuring T1 and T2 
relaxation times, MRF is a fully quantitative method that is consistent across a range of 
instruments, personnel and scan dates.  Prior studies have demonstrated the utility of T1 and T2 
values in the diagnosis and surveillance of brain tumors.  In vitro experiments on excised brain 
tissue have demonstrated that T1 and T2 relaxation times can distinguish tumor histology 
(astrocytoma, oligodendroglioma, meningioma, acoustic neuroma and metastasis) and grade of 
astrocytoma.2 Subsequent in vivo experiments have shown that T2 relaxation time can 
differentiate glioma, meningioma and metastasis in human subjects,3 and may differentiate 
edema from invasive tumor.4 Combination of T1 and T2 relaxation times with other MR 
measured parameters has also been used to distinguish tumor (glioma and lymphoma) from 
edema, and distinguish tumor grade.5 In longitudinal experiments in human subjects with high 
grade glioma, T1 and T2 relaxation times have also been able to identify tumors that respond to 
anti-inflammatory therapies (steroids and bevacizumab) and correlate treatment effect with 
progression free and overall survival.6,7 Finally, the quantitative relaxometry has been used to 
identify lesions in multiple sclerosis, epilepsy and cerebrovascular infarct.8  
Figure 2: T1 and T2 values in adult intra-axial brain tumors, associated perilesional white matter (PWM) and 
contralateral white matter (CWM). (A) T1 and T2 values for lesional tissue and PWM are significantly different 
from contralateral white matter. (B) T2 values of low- (oligo) and high- (GBM) grade glioma are significantly 
different from each other and CWM. 

 
 
 
 
 
CASE 7314, Version 11, 11/21/2016 
 
7 
 
This study will investigate the feasibility of using MRF in children, adolescents and young adults 
(AYA) with and without brain tumors, in subjects with and without neurofibromatosis type 1 
(NF1). Further, it will explore potential applications of MRF signal quantification in pediatric 
and AYA brain tumors, including tissue characterization, determination of treatment effect and 
identification of occult tumor.  To explore the feasibility and potential applications of MRF, this 
study will recruit up to 80 subjects but will stop once 10 evaluable subjects are achieved in each 
of six groups. Subjects will be recruited to the study according to the following criteria: 
 
a)  10 subjects with NF1-associated OPG 
b)  10 subjects with NF1 without brain tumor 
c)  10 subjects without NF1 and with low grade gliomas exposed to therapy 
d)  10 subjects without NF1 and with untreated low grade gliomas 
e)  10 subjects without NF1 and without brain tumor 
f)  10 subjects with brain tumors of assorted pathology 
 
 
2.0 OBJECTIVES 
 
2.1 Primary Objective 
 
Specific Aim 1:  Demonstrate the feasibility of MRF in children and AYA with and 
without brain tumors. 
2.2 Secondary Objectives 
Specific Aim 2:  Characterize the MRF signature of low-grade gliomas 
Specific Aim 3:  Determine whether MRF can identify occult tumor in subjects with low-
grade glioma.   
Specific Aim 4:  Determine whether MRF can identify treatment effects in low-grade 
gliomas.   
Specific Aim 5: Explore whether common brain tumors can be differentiated by comparing 
pre-operative MRF signature with pathologic diagnosis. 
 
3.0 PATIENT/SAMPLE SELECTION 
 
We expect to approach up to 80 patients at Rainbow Babies & Children’s Hospital (Rainbow) in 
order to enroll 10 evaluable subjects in each of 6 categories (see above).  Sufficient statistical 
power would be achieved with 60 subjects. Recruitment will end once 10 evaluable subjects are 
accrued to each category or 80 subjects are recruited, whichever is first. 
 
3.1  Inclusion Criteria 
3.1.1  Age <35 years 
3.1.2  Subjects undergoing MRI evaluation of the brain  

 
 
 
 
 
CASE 7314, Version 11, 11/21/2016 
 
8 
 
3.1.3  NF1 status will be determined by clinical exam or genetic testing 
3.1.4  NF1-associated OPG will be defined as radiographic evidence of glioma along the 
optic nerve, chiasm, tract or radiation in a child with NF1. 
3.1.5  Untreated low grade gliomas will be imaging-defined gliomas that have not yet 
been exposed to radiation or systemic chemotherapy.  Those exposed to therapy will have 
had radiation and/or systemic chemotherapy more than 1 month prior to scans. 
 
3.2   Exclusion Criteria 
3.2.1  History of mental retardation unrelated to brain tumor 
3.2.2  Presence of a genetic disorder other than NF1 that effects cognition or is associated 
with MR imaging abnormalities (e.g. tuberous sclerosis) 
3.2.3  History of cerebrovascular accident (stroke) 
3.2.4  Birth weight below five pounds, premature birth prior to 36 weeks of gestation, or 
ischemic episode at birth 
3.2.5  Major psychiatric diagnosis prior to neuro-oncological diagnosis  
 
4.0 REGISTRATION PROCEDURES AND CONSENT  
 
Participants will be recruited from the neurology, oncology and neurosurgery clinics.  The 
oncology, neuro-oncology and neurocutaneous syndromes clinics have a focus on both pediatric 
as well as adolescent and young adult patients which includes patients up to 35 years of age.  
Healthy controls will be recruited from children and AYA undergoing brain MRI for other 
indications (new seizure, headache, etc) in which brain tumor may be a reasonable cause of 
symptoms. Study personnel including the study PI, co-investigators, and research nurses may 
consent eligible subjects.  All subjects enrolled on study will be entered into a single secure 
database (OnCore) (please see section 8.1, below).  
 
When appropriate, subjects ages 7-17 will be included in all discussions in order to obtain assent; 
they will be offered the opportunity sign the assent form. Assent will be waived for subjects under 
age 7, and parents/guardians will sign the consent form. 
 
5.0 RESEARCH PLAN  
 
This study will examine the feasibility of MRF in children and AYA and determine whether 
quantitative measures of T1 and T2 can be derived in subjects <35 years of age.  Approximately 
80 subjects will be evaluated and include subgroups where MRF may be of particular utility, 
including children and AYA subjects with brain tumors and subjects with NF1.  Additional aims 
will investigate the utility of MRF in these groups. 
SA1:  Subjects undergoing MRI for other clinical indications will be included in the study.  MRF 
will be included in the imaging sequences prior to contrast infusion (in those receiving contrast) 

 
 
 
 
 
CASE 7314, Version 11, 11/21/2016 
 
9 
or at the end of the sequence (for those not receiving contrast) and will add approximately 10-15 
additional minutes of scan time.  Duration of MRF sequences will be recorded as a measure of 
feasibility.  The ability to reliably extract values for T1 and T2 will also be assessed.  Duration of 
scan and the proportion of evaluable scans will be described.  Unevaluable scans will be those 
from which T1 and T2 values cannot be assessed.  Scans may be unevaluable if no one is available 
to conduct the MRF sequence, the MRF sequence is temporarily unavailable on the scanner being 
used, or in the case of patient or clinician refusal.  
Secondary aims will investigate the application of MRF to pediatric and AYA brain tumors. 
 
SA2:  Characterize the MRF signature of low-grade gliomas.   
In order to define the multiparametric signature of low-grade gliomas, we will use MRF to 
measure T1, T2, M0 and off-resonance values of regions of interest within subjects with these 
tumors.  In 20 subjects with NF1 (10 with NF1-OPG and 10 without), regions of interest within 
the optic nerve will be defined on T1 and T2 sequences and compared to analogous regions in 
NF1 subjects without OPG.  Additional analyses will compare tumor involved optic chiasm, 
tracts and radiations with similar regions in NF1 subjects without OPG.   
In 20 subjects without NF1 (10 with low-grade glioma and 10 without), regions of interest will 
be compared between tumor infiltrated regions and analogous areas from representative axial 
images through the brain.  Tumor infiltrated regions of interest will be defined on traditional MR 
sequences by contrast enhancement or T2 abnormality if no enhancement is present. Unilateral 
tumors will also be compared to non-infiltrated tissue on the contralateral side.  
To compare NF1 and non-NF1 low-grade gliomas, all four measured MRF parameters will be 
compared between 10 subjects with NF1-OPG and 10 subjects with low grade gliomas not 
associated with NF1.  Regions of interest will be defined as above.  A similar analysis will 
compare subjects without tumor in representative regions.  If no significant difference is found 
between NF1 and non-NF1 MRF signatures, further analyses will combine these groups. 
To distinguish NF1-OPG from spongiotic change from common radiographic mimics, the MRF 
signature of NF1-OPG will be compared to regions of NF1 spongiotic change.  Spongiotic 
change will be defined as areas of T2 signal abnormality without contrast enhancement or mass 
effect, in NF1 subjects with and without OPG. Spongiotic change is common in children with 
NF1 and may appear in children unaffected by tumor, and so we reasonably expect at least 10 
areas of spongiotic change to be defined outside the visual pathway.   
In each analysis, all four measured parameters will be compared using Student’s t-test and 
parameters with significant differences between tumor-involved and non-involved tissue will be 
used to define the OPG signature. Parameters with significant differences between tumoral and 
non-tumoral tissue will be used to define the tumor signature.  Based on histograms of T1, T2, 
M0 and off-resonance parameters found in either group, cut-off values may be defined for 
measures that show a statistically significant difference between groups.  Receiver operator 
curves may be constructed to identify maximum sensitivity and specificity of these measures to 
the presence of tumor.  In an exploratory analysis, the effect of each quantitative MRF parameter 
on tissue type (tumor vs. healthy tissue) will be further evaluated using multiple logistic 
regression with an elastic net penalty, and this model will be used to define a multiparametric 
equation to define the probability of tumor presence based on the MRF signature.9 

 
 
 
 
 
CASE 7314, Version 11, 11/21/2016 
 
10 
 
SA3:  Determine whether MRF can identify occult tumor in subjects with low-grade glioma. 
To investigate whether MRF is more sensitive than traditional MRI sequences to tumor 
involvement, we will use MRF to evaluate the optic radiations of children and AYA subjects 
with OPG who have already had DTI analysis. Data from our group suggests that tumor may 
affect white matter tracts (measured by DTI) that do not appear to be infiltrated on traditional 
MRI sequences.10  However, quantitative MRF measures may identify peri-tumoral areas that 
appear to have similar T1 and T2 signal to tumor regions.  These areas may represent tumor 
infiltration or peri-tumoral edema that may damage white matter tracts and affect visual acuity. 
To determine whether tumor identified by MRF correlates with white matter integrity, subjects 
with OPG who have been evaluated by DTI will undergo MRF.  Optic radiations will be 
evaluated for evidence of tumor on MRF, defined by parameter limits that distinguish OPG from 
normal tissue determined in SA2 above.  The proportion of voxels with a signature consistent 
with OPG will be compared to the FA of the optic radiations measured by DTI.  Preliminary data 
demonstrate that approximately 80% of radiations with decreased white matter integrity 
(FA<0.51) have no evidence of tumor involvement on traditional MRI.10 A significant 
correlation between tumor involvement (defined by MRF) and white matter integrity may 
suggest that occult tumor was responsible for white matter tract damage.   
To determine whether MRF abnormalities can predict tumor recurrence due to residual tumor, 
post-operative peritumoral areas in resected tumors will be examined.  Scans performed in the 
immediate post-operative period (24-48 hours after surgery), will be used to acquire axial images 
of the region of interest.  Peritumoral area will be defined as the area within 1cm of the resection 
cavity on the axial image.  MRF signature will be measured in each voxel of these areas, and 
histograms of MRF parameters will be constructed in addition to a description of means or 
medians, as appropriate.  In a further exploratory analysis, the MRF signature in each voxel will 
be converted into a probability of occult tumor (a heat map), using the multiparametric cut-off 
values derived in SA2.  Histogram of probabilities will be examined for skewness and kurtosis.  
To evaluate test characteristics of MRF, a receiver-operator curve (ROC) will be created varying 
the probability cut-off of occult tumor to predict tumor recurrence.   
 
SA4:  Determine whether MRF can identify treatment effects in low-grade gliomas.   
To determine the effect of treatment on low-grade gliomas, we will follow serial MRF scans in 
children and AYA subjects with low-grade gliomas that do and do not require treatment. In 
subjects that do not require treatment, serial MRF scans will be conducted along with 
surveillance imaging over the course of one year.  In subjects that require treatment with 
radiation or chemotherapy, MRF signature of tumor tissue will be compared in pre- and post-
therapy scans, performed approximately 3 months apart in accordance with their surveillance 
imaging schedule.  Change in MRF signature between serial evaluations will be compared in 
treatment exposed and non-exposed groups.   
Student’s paired t-test or non-parametric Wilcoxon signed rank test will be used to compare each 
MRF parameter to determine statistically significant differences.  Radiation and chemotherapy 
will also be considered in separate analyses as these therapies likely have varied effects on tissue 

 
 
 
 
 
CASE 7314, Version 11, 11/21/2016 
 
11 
microstructure.  Significant differences in pre- and post-therapy MRF signatures will define cut-
off values demonstrating likely treatment effect.  
 
SA 5: Explore whether common brain tumors can be differentiated by comparing pre-operative 
MRF signature with pathologic diagnosis. 
In this exploratory aim, pre-operative MRF signature of low-grade gliomas and tumors of 
assorted pathology will be compared to post-operative pathologic diagnosis to determine the 
multiparametric signature that best defines tumor pathology. This aim remains exploratory and 
methods of analysis depend on the number of different pathologies investigated. Histograms may 
be constructed to determine cut-off values that best define each pathology.  
6.0 STUDY PARAMETERS 
 
 
6.1 Study Calendar 
 
Parameters 
 
 
Baseline  
 
 
Surveillance 
Imaging 
(within 1 yr) 
 
Further Observation 
(within 5 yrs) 
All Subjects 
Age (years) 
X 
 
 
Gender 
X 
 
 
NF1 status 
X 
 
 
MRI evaluation 
X 
X 
 
MRF evaluation 
X 
X 
 
Duration of MRF scan 
X 
X 
 
Treatment history 
X 
X 
X 
Ophthalmology exam1 
X 
X 
 
Post-Therapy MRF2 
 
X 
 
Pathology report3 
 
X 
X 
1 Opthalmology exams will be recorded for subjects with OPG 
2 For those requiring therapy.  Post-surgical MRF will occur 24-48 hours after surgery with 
clinical scan.  Post-chemotherapy or post-radiation MRF will occur at next surveillance 
imaging approximately 3 months from therapy. 
3 For those who have a surgical biopsy or resection. 
 
6.2 Case Ascertainment: Potential subjects will be identified from active patient lists and 
upcoming clinic appointments with the Divisions of Pediatric Oncology, Pediatric Neurology, 
and NeuroSurgery.  Potential subjects who are scheduled for a scan prior to their next 
appointment will be contacted by telephone (see telephone script). 
 
 

 
 
 
 
 
CASE 7314, Version 11, 11/21/2016 
 
12 
6.3 Data Elements and Sources 
6.3.1  Chart review: A review of the medical chart will provide demographic information 
(date of birth, gender, NF1 status), ophthalmology records (visual acuity, visual field 
testing, optic disc evaluation), and pathologic diagnosis.  Subsequent reviews determine 
any new treatments or pathology. 
6.3.2  Imaging data:  DTI, MRI and MRF imaging will be acquired from the PACS system 
for post-processing. DTI is currently standard of care for children with brain tumors and 
will not require additional sedated time. 
 
 
7.0 
SAMPLE SIZE and POWER CALCULATIONS 
 
SA1: To determine whether MRF is feasible in subjects with and without brain tumors, we will 
record duration of MRF sequences in subjects’ baseline scan and subsequent MRF scans over the 
course of one year.  A separate analysis will investigate children <18 years old.  Image quality and 
the ability to adequately extract T1 and T2 signal parameters will also be evaluated.  Data will be 
evaluated descriptively both among all subjects, and among subpopulations (healthy control 
subjects, subjects with tumor, and subjects with NF1).  
In this descriptive aim, we will measure the time required for MRF and the proportion of potential 
MRF scans that are evaluable.  We will consider MRF feasible if scans are completed in less than 
15 minutes and that greater than 75% are evaluable. 
SA2:  We hypothesize that MRF will distinguish brain tumor from matching healthy tissue. 
Assuming a standard deviation of 170ms for T1 values for tumor and normal tissue (found in 
preliminary studies), 20 subjects (10 with tumors and 10 without) would be able to detect a 
difference of 225ms in T1 with 80% power and alpha of 0.05 using a two-sided T test.  If MRF 
signature is found to be unaffected by NF1 status, combined groups would yield 40 subjects (20 
with low-grade glioma and 20 without), which would be able to detect a difference of 155ms in 
T1 with 80% power and alpha of 0.05 using a two-sided T test. A difference of 908ms was found 
in T1 between tumor and contralateral white matter in preliminary data.  Because T2, M0 and 
off-resonance values will be interrogated in addition to T1, we expect to have increased power to 
find a distinct tumor signature. 
SA3:  We hypothesize that MRF signature will be able to distinguish occult low-grade glioma. 
We will compare fractional anisotropy (measured by DTI) in the optic radiations with MRF 
signature in correlate with OPG.  With an alpha of 0.05, 10 subjects would provide 80% power 
to identify a correlation of 0.71 or greater between T1 or T2 and fractional anisotropy on a one-
sided test, which would provide a clinically meaningful result. 
We will create a receiver-operator curve comparing the likelihood of occult tumor with tumor 
recurrence.  Six tumor recurrences among 20 subjects with NF1 or non-NF1 low-grade glioma 
(30% relapse rate) would achieve 80% power to detect an area under the ROC ≥0.83 compared to 
the null hypothesis area of 0.5 with an alpha of 0.05.  Secondary analysis will also analyze whether 
peak tumor probability, rather than mean probability, is predictive of tumor recurrence.  To explore 

 
 
 
 
 
CASE 7314, Version 11, 11/21/2016 
 
13 
whether MRF can predict the site of recurrence, heat maps derived in SA3 will be qualitatively 
compared to sites of recurrence. 
SA4:  We hypothesize that MRF signature will be able to distinguish treatment effects in low-
grade gliomas. 
The effect of treatment on low-grade gliomas has not yet been interrogated by MRF and standard 
deviations of T1 and T2 parameters are still unknown. Using standard deviations of high-grade 
gliomas as conservative estimates of T2 standard deviation in low-grade glioma (standard 
deviation of 60ms) and assuming a correlation of 0.5 between paired time points, a difference in 
T2 of 60ms could be detected with 80% power and an alpha of 0.05 when 10 low-grade gliomas 
are compared serially using a two-sided t test.  As in SA2, we expect to have greater than 
estimated power because we will be comparing multiple parameters (T1, T2, M0 and off-
resonance) to define the change in MRF signature over time. 
SA5:  We hypothesize that MRF signature will be able to distinguish common tumor pathologies. 
This remains an exploratory aim that will provide preliminary data for future investigations. For 
each pathology, histograms of MRF parameters will be created and mean MRF parameter values 
will be described.  Power will be inadequate for statistical comparison in this aim.  However, this 
data will provide essential information for future work defining the MRF signature of multiple 
pathologies. 
 
8.0 RECORDS/DATA TO BE KEPT 
 
 
8.1  Data Collection and Management 
Non-imaging data (demographics, pathology, ophthalmology, medical history) will be 
maintained on a secure, password-protected database (OnCore).  Only those persons selected 
to perform data entry will have access to secure databases.  Data will be reviewed for face 
validity and completeness before being accepted into the database. Raw images will be 
coded with a unique identification number to protect PHI.  The master key will be kept in 
OnCore.  Data from this study will also be sent to an outside institution for review by Peter 
de Blank, MD.  
 
The OnCore Database will be utilized, as required by the Case Comprehensive Cancer 
Center, to provide data collection for both accrual entry and trial data management at 
Rainbow. OnCore is a Clinical Trials Management System housed on secure servers 
maintained at Case Western Reserve University.  OnCore properly used is compliant with 
title 21 CFR Part 11.  Access to data through OnCore is restricted by user accounts and 
assigned roles.  Once logged into the OnCore system with a user ID and password, OnCore 
defines roles for each user which limits access to appropriate data.  User information and 
password can be obtained by contacting the Oncore Administrator at oncore-
registration@case.edu. 
 

 
 
 
 
 
CASE 7314, Version 11, 11/21/2016 
 
14 
 
8.2  Regulatory and Ethical Considerations 
8.2.1  Risk Assessment 
Subjects enrolling on study will be exposed to risks of privacy regarding protected health 
information.  Diffusion Tensor Imaging is now standard of care at Rainbow and will not 
provide any additional sedated time for study subjects.  MRF will be performed during the 
clinical MRI scans and will require approximately 10-15 additional minutes of scan time. 
Risk to privacy will be minimized by password protection of the database, and use of coded 
identifiers for raw imaging data. We consider these potential risks to be a minor increase 
over minimal risk in subjects undergoing sedation and no more than minimal risk in 
subjects who do not need sedation. 
 
8.2.2  Potential Benefits of Study Participation 
MRF provides additional data parameter maps that may be useful in identifying and 
interpreting pathology.  Preliminary data (section 1.0) demonstrates that MRF has been 
able to differentiate tumor and normal tissue, and may help identify tumor type and 
location.  MRF parameter maps may help radiologists refine the differential diagnosis of 
abnormal tissue and may contribute to patient care. The proposed study may also generate 
generalizable and important knowledge about the feasibility of MRF in children and AYA 
subjects, as well as determine whether MRF is more sensitive to occult tumor invasion than 
traditional MRI.  Since all subjects recruited for this study will undergo clinical MRI for 
potential pathologic diagnosis, we consider this a potential benefit to subjects. 
 
8.2.3  Risk-Benefit Assessment 
This study involves a minor increase over minimal risk to subjects undergoing sedation or 
anesthesia, and presents the potential for benefit for all subjects.  This study represents no 
more than minimal risk in subject who do not require sedation.  This study will develop 
new knowledge about the feasibility and utility of MRF and has the additional potential to 
develop a new-non-invasive method to better evaluate residual tumor burden in children 
and AYA subjects with brain tumors.   
 
 
 8.3  Safety Management 
 
 8.3.1  Clinical Adverse Events 
Unanticipated problems involving risks to subjects and others will be monitored 
throughout the study by the PI.  
 
 
  8.3.2   Adverse Event Reporting 
Adverse events (AEs) will be graded according to the categorization below: 
Grade 1 (MILD):  Sign or symptom is noticeable, but does not interfere with normal 
daily activities. 
Grade 2 (MODERATE):  Sign or symptom interferes with normal daily activities. 

 
 
 
 
 
CASE 7314, Version 11, 11/21/2016 
 
15 
Grade 3 (SEVERE):  Sign or symptom is incapacitating, with inability to perform 
daily activities. 
Grade 4 (LIFE-THREATENING):  Sign or symptom poses immediate risk of death 
to this patient.   
Serious adverse events (SAEs) will be grade 3 or 4 AE or any AE requiring 
hospitalization.   
SAEs are not expected. If any unanticipated problems related to the research involving risks 
to subjects or others happen during the course of this study (including SAEs) these will be 
entered into the local database and transferred to the OnCore database (if necessary).  
Unanticipated problems will be reported to the IRB according to current IRB guidelines. 
AEs that are not serious but that are notable and could involve risks to subjects will be 
summarized in narrative or other format entered into the OnCore database and submitted to 
the IRB at the time of continuing review per current IRB guidelines.  
 
8.3.3 Data Safety and Monitoring Plan 
 
This protocol will adhere to the policies of the current Case Comprehensive Cancer Center 
Data and Safety Monitoring Plan, in accordance with NCI regulations. The Data and Safety 
Toxicity Committee will review all serious adverse events and toxicity reports as well as 
protocol deviations. 
 
9.0 
FINANCIAL CONSIDERATIONS 
There will be no cost to subjects for the additional experimental tests (MRF) on this study.  
Subjects and their insurance providers will be responsible for the payment of procedures that 
are considered standard of care, including doctor visits, MRIs (including DTI, which is part of 
the standard clinical MRI), medications and all other medical care.  
 
 
10.0 
REFERENCES 
 
1. 
Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature. Mar 
14 2013;495(7440):187-192. 
2. 
Englund E, Brun A, Larsson EM, Gyorffy-Wagner Z, Persson B. Tumours of the 
central nervous system. Proton magnetic resonance relaxation times T1 and T2 and 
histopathologic correlates. Acta radiologica: diagnosis. Nov-Dec 1986;27(6):653-659. 
3. 
Chen XZ, Yin XM, Ai L, Chen Q, Li SW, Dai JP. Differentiation between brain 
glioblastoma multiforme and solitary metastasis: qualitative and quantitative analysis 
based on routine MR imaging. AJNR Am J Neuroradiol. Nov 2012;33(10):1907-1912. 
4. 
Oh J, Cha S, Aiken AH, et al. Quantitative apparent diffusion coefficients and T2 
relaxation times in characterizing contrast enhancing brain tumors and regions of 
peritumoral edema. J Magn Reson Imaging. Jun 2005;21(6):701-708. 
5. 
Wagnerova D, Herynek V, Malucelli A, et al. Quantitative MR imaging and 
spectroscopy of brain tumours: a step forward? European radiology. Nov 
2012;22(11):2307-2318. 

 
 
 
 
 
CASE 7314, Version 11, 11/21/2016 
 
16 
6. 
Ellingson BM, Cloughesy TF, Lai A, et al. Quantification of edema reduction using 
differential quantitative T2 (DQT2) relaxometry mapping in recurrent glioblastoma 
treated with bevacizumab. J Neurooncol. Jan 2012;106(1):111-119. 
7. 
Sinha S, Bastin ME, Whittle IR. Rapid clinical deterioration in a patient with multi-
focal glioma despite corticosteroid therapy: a quantitative MRI study. British journal 
of neurosurgery. Dec 2003;17(6):537-540; discussion 540. 
8. 
Cheng HL, Stikov N, Ghugre NR, Wright GA. Practical medical applications of 
quantitative MR relaxometry. J Magn Reson Imaging. Oct 2012;36(4):805-824. 
9. 
Grobman WA, Stamilio DM. Methods of clinical prediction. American journal of 
obstetrics and gynecology. Mar 2006;194(3):888-894. 
10. 
de Blank PM, Berman JI, Liu GT, Roberts TP, Fisher MJ. Fractional anisotropy of the 
optic radiations is associated with visual acuity loss in optic pathway gliomas of 
neurofibromatosis type 1. Neuro-oncology. Aug 2013;15(8):1088-1095.