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International Journal of
Radiation Oncology
biology
physics
www.redjournal.org
Imaging in Radiation Oncology
Clinical Applications of Quantitative
3-Dimensional MRI Analysis for
Pediatric Embryonal Brain Tumors
Jared H. Hara, BS,* Ashley Wu, BS,* Javier E. Villanueva-Meyer, MD,y
Gilmer Valdes, PhD,* Vikas Daggubati, BS,* Sabine Mueller, MD, PhD,z
Timothy D. Solberg, PhD,* Steve E. Braunstein, MD, PhD,*
Olivier Morin, PhD,* and David R. Raleigh, MD, PhD*,z
Departments of *Radiation Oncology, yRadiology, and zNeurological Surgery, University of
California, San Francisco, San Francisco, California
Received Dec 19, 2017, and in revised form May 22, 2018. Accepted for publication May 28, 2018.
Summary
The relationship between
clinical outcomes and imaging characteristics of pediatric embryonal brain tumors
is poorly understood. We
analyzed embryonal brain
tumor magnetic resonance
images to identify quantitative radiomic features associated with clinical
outcomes. Our results
demonstrate that features
quantifying primary tumor
size and heterogeneity are
associated with patient age,
neuraxis metastases, histology, and recurrence. These
data suggest that 3dimensional magnetic
Purpose: To investigate the prognostic utility of quantitative 3-dimensional magnetic
resonance imaging radiomic analysis for primary pediatric embryonal brain tumors.
Methods and Materials: Thirty-four pediatric patients with embryonal brain tumor
with concurrent preoperative T1-weighted postcontrast (T1PG) and T2-weighted
fluid-attenuated inversion recovery (FLAIR) magnetic resonance images were identified from an institutional database. The median follow-up period was 5.2 years. Radiomic features were extracted from axial T1PG and FLAIR contours using MATLAB,
and 15 features were selected for analysis based on qualitative radiographic features
with prognostic significance for pediatric embryonal brain tumors. Logistic regression,
linear regression, receiver operating characteristic curves, the Harrell C index, and the
Somer D index were used to test the relationships between radiomic features and demographic variables, as well as clinical outcomes.
Results: Pediatric embryonal brain tumors in older patients had an increased normalized mean tumor intensity (P Z .05, T1PG), decreased tumor volume (P Z .02,
T1PG), and increased markers of heterogeneity (P .01, T1PG and FLAIR) relative
to those in younger patients. We identified 10 quantitative radiomic features that delineated medulloblastoma, pineoblastoma, and supratentorial primitive neuroectodermal
tumor, including size and heterogeneity (P .05, T1PG and FLAIR). Decreased
markers of tumor heterogeneity were predictive of neuraxis metastases and trended toward significance (P Z .1, FLAIR). Tumors with an increased size (area under the
Reprint requests to: David R. Raleigh, MD, PhD, Departments of Radiation Oncology and Neurological Surgery, University of California, San
Francisco, 505 Parnassus Ave, L08/L75, Box 0226, San Francisco, CA
94143. Tel: (415) 353-3675; E-mail: david.raleigh@ucsf.edu
Int J Radiation Oncol Biol Phys, Vol. -, No. -, pp. 1e13, 2018
0360-3016/$ - see front matter Published by Elsevier Inc.
https://doi.org/10.1016/j.ijrobp.2018.05.077
O.M. and D.R.R. contributed equally to this work.
Conflict of interest: none.
Supplementary material for this article can
www.redjournal.org.
be
found
at
2
International Journal of Radiation Oncology Biology Physics
Hara et al.
resonance imaging analysis
has the potential to identify
radiomic risk features for
pediatric patients with
embryonal brain tumors.
curve Z 0.7, FLAIR) and decreased heterogeneity (area under the curve Z 0.7,
FLAIR) at diagnosis were more likely to recur.
Conclusions: Quantitative radiomic features are associated with pediatric embryonal
brain tumor patient age, histology, neuraxis metastases, and recurrence. These data
suggest that quantitative 3-dimensional magnetic resonance imaging radiomic analysis
has the potential to identify radiomic risk features for pediatric patients with embryonal brain tumors. Published by Elsevier Inc.
Introduction
Embryonal brain tumors, such as medulloblastoma, pineoblastoma, and supratentorial primitive neuroectodermal
tumor (sPNET), are the most common pediatric brain tumors. These highly malignant neoplasms are often diagnosed in young children, in whom curative treatments
involving surgery, radiation therapy, and chemotherapy are
often associated with significant long-term side effects
(1-3). Thus, pediatric embryonal brain tumor treatments are
frequently individualized according to clinical prognostic
features to maximize tumor control and minimize toxicity
(4-8). In that regard, molecular subgrouping has provided
key clinical prognostic information that has guided several
prospective de-escalation trials for Wnt-associated medulloblastoma (6-8). However, medulloblastoma and other
pediatric embryonal brain tumors are heterogeneous, and
there is an unmet need for novel strategies to guide adjuvant therapy (9-11). Recently, several studies have shown
that physician-defined, qualitative radiographic features are
associated with molecular subgroups of pediatric embryonal brain tumors and may provide useful information to
adapt adjuvant therapy (12-16). Building on those successes, the purpose of this study was to evaluate the
prognostic utility of quantitative radiomic features for pediatric embryonal brain tumors.
Many studies have identified correlations between imaging characteristics and clinical or molecular prognostic
features in diverse malignancies such as lung cancer, head
and neck cancer, and glioblastoma (17-22). In embryonal
brain tumors, size and qualitative imaging features such as
tumor location, cystic tumor morphology, hemorrhage, and
intratumoral necrosis are associated with distinct molecular
features (13, 23-25). Magnetic resonance imaging (MRI)
with gadolinium enhancement differs between adult and
pediatric medulloblastoma patients (26-34), in whom there
are also strong associations between MRI characteristics,
overall survival (12, 16), and neuraxis metastasis (35, 36).
More recently, studies have revealed similar correlations
among qualitative magnetic resonance spectroscopy features, medulloblastoma location, and molecular subgroup
(14-16, 37, 38).
By using quantitative imaging features, radiomics can
function as a guide that may provide novel information
beyond what a physician can consciously observe (39). For
pediatric embryonal brain tumors, physician-defined
imaging features have been discovered to correlate with
some but not all important genetic and prognostic characteristics. Thus, we hypothesized that quantitative radiomic
analysis of features derived from unbiased tumor masks
might enhance prognostication for pediatric embryonal
brain tumors. In support of this hypothesis, quantitative
textural analysis can be used to discriminate between
different pediatric brain tumor histologies (40-43), but
these approaches have not been previously reported for
pediatric embryonal brain tumors.
We explored the prognostic value and complementary
role of morphologic, normalized intensity and textural
radiomic features to (1) differentiate between pediatric
embryonal brain tumor histologies preoperatively, (2)
identify tumors with neuraxis metastases at presentation,
and (3) identify patients who are at risk of recurrence. The
specific objectives of this study were to (1) extract
3-dimensional (3D) radiomic features from common MRI
sequences (T1-weighted postcontrast [T1PG] and T2weighted fluid-attenuated inversion recovery [FLAIR])
and (2) define a framework for radiomics in pediatric
embryonal brain tumors by identifying relationships among
individual 3D MRI features, patient demographic characteristics, and clinical outcomes. Our results reveal associations between quantitative imaging features of pediatric
embryonal brain tumors and patient age, tumor histology,
neuraxis metastasis, and recurrence, suggesting that 3D
radiomic analysis could be used as a noninvasive method
for risk stratification in pediatric patients with embryonal
brain tumor.
Methods and Materials
Study design and patient population
We performed a retrospective institutional database search
for embryonal brain tumors diagnosed between June 1988
and October 2014 with institutional review board approval
(protocol 14-15432) and a waiver of consent. The study
population consisted of diverse patients treated over several
therapeutic eras; some patients were enrolled on clinical
trials, but the majority were treated according to eraspecific trial parameters. Only patients with concurrent
preoperative T1PG and FLAIR MRI sequences from a 1.5or 3-T scanner were included (Tables E1 and E2, available
Volume - Number - 2018
Table 1
Pediatric embryonal brain tumor radiomics
Patient, tumor, and treatment characteristics
Data
Patients and tumors
Total patients
Age at diagnosis (range), y
Sex
Male
Female
M status
M0*
Mþ*
Histology
Medulloblastoma
Morphology
Desmoplastic
Nodular or desmoplastic
Classic
Anaplastic
Not otherwise specified
Molecular mutation
Wnt
Sonic hedgehog
Myc
17pq deletion
Unknown
Pineoblastoma
sPNET
Treatment
Gross total resection
Subtotal resection
Radiation therapy
CSI
Local dose, Gy
CSI dose, Gy
Upfront radiation therapy
Age at diagnosis, y
Time to radiation therapy, d
Delayed radiation therapy
Age at diagnosis, y
Received delayed radiation therapy
Time to radiation therapy, d
Chemotherapy
Total no. of cycles
Vincristine
Vincristine and carboplatin
CCNU, vincristine, and cisplatin
Thiotepa, cyclophosphamide, etoposide, and vincristine
Vincristine, cisplatin, and cyclophosphamide
Carboplatin and etoposide
Carboplatin and etoposide alternating with ifosfamide and etoposide
Cisplatin and cyclophosphamide
Etoposide, cyclophosphamide, and vincristine
Vincristine, cisplatin, cyclophosphamide, and cis-retinoic acid
Cisplatin, cyclophosphamide, vincristine, and etoposide
Vincristine, cisplatin, cyclophosphamide or mesna, etoposide, and methotrexate
Cisplatin, vincristine, CCNU, and cyclophosphamide
Stem cell transplant
Shunt placement
Salvage therapy after recurrence
34
6.9 (IQR, 4.1-13.0)
13 (38.2%)
21 (61.7%)
30 (88.2%)
4 (11.8%)
22 (64.7%)
3
1
2
7
21
(8.8%)
(2.9%)
(5.9%)
(20.6%)
(61.8%)
1
1
1
3
28
6 (17.6%)
6 (17.6%)
27
7
29
27
55.8
23.4
27
7.1
26
7
2.7
2
172
32
5
(79.4%)
(20.6%)
(85.2%)
(79.4%)
(IQR, 54.0-55.8)
(IQR, 23.4-36.0)
(79.4%)
(IQR, 5.1-13.6)
(IQR, 22-33)
(20.6%)
(IQR, 2.5-5.4)
(5.9%)
(IQR, 161-183)
(94.1%)
(IQR, 4-8)
3
5
5
3
6
1
1
1
1
1
2
2
1
6 (17.6%)
2 (6%)
9 (26.5%)
(continued on next page)
3
4
Hara et al.
International Journal of Radiation Oncology Biology Physics
Table 1 (continued )
Data
Surgery
Radiation therapy
Chemotherapy
Stem cell transplant
Outcome
Median follow-up, y
Recurrencey
Local failure
Regional and distant failure (Mþ)
Recurrent regional or distant failure
New area of failure
Median time to recurrence, y
Death
Median time to death, y
2
6
7
4
5.15
9
4
5
2
3
2.2
6
2.31
(5.9%)
(17.6%)
(20.6%)
(11.8%)
(IQR, 2.69-7.99)
(26.5%)
(44.4%)
(55.5%)
(40.0%)
(60.0%)
(95% CI, 1.5-3.0)
(17.6%)
(95% CI, 1.4-5.6)
Abbreviations: CCNU Z lomustine; CI Z confidence interval; CSI Z craniospinal irradiation; IQR Z interquartile range; sPNET Z supratentorial
primitive neuroectodermal tumor.
* The extent of metastatic spread was defined as Mþ for M1 to M4 disease after cerebrospinal fluid cytologic analysis and spinal magnetic resonance
imaging for staging, consistent with the Chang criteria (45).
y
Recurrence was defined as any local (resection cavity), regional, or distant metastatic failure using the Chang criteria (45). M0 was defined as
localized disease at initial therapy; Mþ, disseminated disease at initial therapy; local failure, resection cavity failure; and regional and distant failure,
neuraxial or distant visceral organ metastases after therapy.
online at www.redjournal.org) (44). The oldest scan year
included in the study was 2001. Patient demographic,
pathologic, and clinical risk factors are summarized in
Table 1. Patient age was defined as the age at initial diagnosis. The extent of metastatic spread was defined as Mþ
for M1 to M4 disease after cerebrospinal fluid (CSF)
cytologic analysis and spinal MRI for staging, consistent
with the criteria of Chang et al (45). The extent of resection
was based on postoperative MRI and defined as gross total
resection (GTR) or subtotal resection (1.5 cm2). For the
purposes of this study, patients with near-total resection
were grouped with GTR patients (46, 47). Histopathologic
re-review was performed by neuropathologists according to
current diagnostic criteria (48, 49). Recurrence was defined
as any local (resection cavity), regional, or distant metastatic failure using the Chang criteria (45); patients with
regional or distant metastatic failure were further subclassified according to growth of neuraxial metastases that
were present at initial presentation versus emergence of
new areas of disease. Survival was defined as the length of
time from diagnosis to the date of last follow-up, to the date
of radiographic progression for progression-free survival,
or to the date of death for overall survival (Fig. 1).
Radiomic feature selection and extraction
Preoperative images were exported, and regions of interest
(ROIs) were delineated on both T1PG and FLAIR axial
imaging sequences using MIM Vista (version 6.4.9; MIM
Software, Cleveland, OH) by a single radiation oncologist
(D.R.R) to limit interreviewer variation. Both tumor and
control ROIs were contoured (Fig. 2A), the latter of which
were delineated at gray-matter junctions using a 5-mm
brush on 3 consecutive slices from both T1PG and
FLAIR axial images for low-intensity normalization. ROIs
were then expanded and contracted serially by 1 mm using
an automated MIM workflow (Fig. 2B), anonymized, and
exported to MATLAB 2017a (The MathWorks, Natick,
MA) for feature extraction via an open-source development
radiomic package called the Quantitative Imaging Prediction Modeling platform (https://github.com/qipm/QIPM)
(50, 51). This radiomic package has been validated by the
Image Biomarker Standardization Initiative. After image
quality analysis, 4 FLAIR sequences were excluded
because of software incompatibility.
Although the Quantitative Imaging Prediction Modeling
platform is capable of computing thousands of features, the
number of features was kept close to the number of patients
to specifically limit the chance of false-positive discoveries
(52). Accordingly, quantitative imaging features were
selected for further analysis after physicist (O.M.) and
neuroradiologist (J.E.V.) review of radiomic parameters that
tractably quantify qualitative radiographic features with
known prognostic value for pediatric embryonal brain tumors (as discussed in the “Introduction” section; Table 2).
Features selected included (1) morphologic features, which
capture tumor size and shape; (2) statistical features, which
calculate nondiscretized intensity values within tumors; and
(3) textural features, which express how combinations of
gray levels are spatially distributed, connected, or summed
(Table E3, available online at www.redjournal.org). The
gray-level co-occurrence and size zone matrix features were
selected to quantify heterogeneity and the amount of
Volume - Number - 2018
B
100
75
Overall survival (%)
Progression free survival (%)
A
Pediatric embryonal brain tumor radiomics
50
25
0
5
10
Time (years)
15
75
50
25
20
0
5
10
Time (years)
15
20
Progression-free survival (A) and overall survival (B) for patients with embryonal brain tumor included in the study.
necrotic tissue and/or brain invasion. Normalized intensity in
statistical features was selected with the intention to quantify
hypointense, isointense, and hyperintense radiographic presentation. This approach generated a total of 35 radiomic
A
100
0
0
Fig. 1.
5
Medulloblastoma
Pineoblastoma
sPNET
features per imaging study that were computed to quantify
size, shape, normalized intensity, and texture of each tumor.
Normalized intensity was defined as the ROI mean signal of
the tumor divided by the ROI mean signal of a gray-matter
Control
B
T2-Fluid Attenuated
Inversion Recovery
Merge
T2-Fluid Attenuated
Inversion Recovery
T1-Post Contrast
T1-Post Contrast
Fig. 2. Representative magnetic resonance imaging scans of pediatric embryonal brain tumors in patients. The 3 histologic
tumor types (axial T1-weighted postcontrast [T1PG] [top and bottom rows] and axial T2-weighted fluid-attenuated inversion
recovery [FLAIR] [middle row] magnetic resonance images) (A) and contour expansions for image feature extractions (B)
are shown. (A) Supratentorial primitive neuroectodermal tumors (sPNETs) were noted to have markedly larger tumor volumes, whereas pineoblastomas were associated with the smallest maximal 3-dimensional tumor diameter. Pineoblastomas
were noted to have increased markers of heterogeneity relative to medulloblastomas and sPNETs. Tumors contoured from
T1PG images are shown in magenta, and tumors contoured from T2-weighted FLAIR images are shown in cyan. (B)
Representative axial T1PG (left) and axial T2-weighted FLAIR (right) images are shown for radiomic feature computation on
1-mm expansions and contractions of the original regions of interest to add robustness in feature selection.
6
International Journal of Radiation Oncology Biology Physics
Hara et al.
Table 2
Associations between radiographic and radiomic features with relevance to pediatric embryonal brain tumors
Radiographic
feature
Clinical association
Radiomic feature
Description
Size
sPNET is often very large (62)
Fmorph_volume
Fmorph_diam
Enhancement
Primary pediatric embryonal brain tumors that
are hypointense on T1 (22, 27), are
hyperintense on T2 (23, 24, 45), and show
T2 contrast enhancement are often
associated with leptomeningeal spread (26)
Associated with medulloblastoma (21, 24, 29)
Medulloblastoma has high cellular density;
association of high density with low signal
on T1-weighted imaging (22, 47)
Heterogeneous appearing medulloblastomas
are associated with an improved prognosis
(63), and sPNET is heterogeneous
appearing (62); medulloblastoma is
associated with necrosis and calcifications
(21, 24), whereas both sPNET and
medulloblastoma are associated with
hemorrhage (24, 27, 30)
Fstat_mean_norm
Fstat_var
Morphologic volume
Morphologic maximum 3-dimensional
diameter
Nondiscretized normalized mean intensity
Nondiscretized intensity variance
Fmorph_sphericity
Fmorph_comp1
Morphologic sphericity
Morphologic compactness
Fcm_contrast
Fcm_dissimilarity
Fszm_zs_var
Fszm_zs_entr
Fngt_contrast
Fngt_busyness
Fngt_complexity
Fngt_strength
Gray-level co-occurrence matrix contrast
Gray-level co-occurrence matrix dissimilarity
Gray-level size zone matrix variance
Gray-level size zone matrix entropy
Gray-level co-occurrence matrix contrast
Neighborhood gray tone busyness
Neighborhood gray tone complexity
Neighborhood gray tone strength
Cystic change
Cell density
Heterogeneity
Necrosis
Hemorrhage
Calcification
Abbreviation: sPNET Z supratentorial primitive neuroectodermal tumor.
control region. Radiomic features were also computed on the
1-mm expansion and contraction of the original ROI to add
robustness in feature selection. Features were calculated
using a 3-mm isotropic interpolation for nontextural and
textural features, linear interpolation for ROI mask, and a
fixed bin number of 32 for discretization of the primary
image intensity. Among the initial 35 features calculated, we
selected the features with the largest observed variance in the
cohort. This feature selection eliminated features that were
nearly constant for the cohort studied. Ultimately, 15
radiomic features were selected for further analyses based on
correlations with physician-defined, qualitative radiographic
characteristics with known prognostic value for pediatric
embryonal tumors. The radiomic features selected encompassed primary tumor size, enhancement, heterogeneity
(hemorrhagic, calcifications, necrosis), and cystic components (Table 2), and textural features were used as markers to
quantify heterogeneity.
Statistical analysis
All statistical analyses were performed using Stata Statistical
Software (version 15; StataCorp, College Station, TX) and
JMP (version 13.0; SAS Institute, Cary, NC). Progression-free
survival and overall survival were analyzed by the KaplanMeier method. We computed z scores for each radiomic
feature to transform the data to comparable scales for
regression analyses. The ability of individual radiomic
features to distinguish between patients by clinical outcomes
was assessed by performing logistic regression for sex and M
status, multinomial logistic regression for histology, linear
regression for age, and Cox regression for recurrence and
survival outcomes. P .05 was considered statistically significant (Table 3; Table E4, available online at www.
redjournal.org). Receiver operating characteristic curves
were built to evaluate the adequacy of fitted logistic regression models, as conveyed by area under the curve (AUC)
values. Asymptotic confidence intervals (CIs) were constructed to provide measures of uncertainty around the curves
for out-of-sample predictions, and AUC values 0.7 were
considered statistically significant (Table 3; Table E4, available online at www.redjournal.org). Harrell C indexes were
computed to evaluate the predictive power of radiomic features in fitted Cox regression models, whereas Somer D indexes were computed to estimate rank parameters with
respect to censored survival time. Indexes and respective
95% CIs were reported (Table 3; Table E4, available online at
www.redjournal.org) (53-56). Individual radiomic features
and clinically relevant predictor variables of age, GTR, and M
status were included in multivariate Cox regression analyses
for recurrence and survival outcomes. Hazard ratios were
reported to reveal each covariate’s predictive power (Tables
E6-E18, available online at www.redjournal.org). Spearman
correlation heat maps were built to illustrate homogeneity
between selected radiomic features (Figs. E1 and E2, available online at www.redjournal.org).
Volume - Number - 2018
Results and Discussion
Patient characteristics, treatments, and clinical
outcomes
Thirty-four pediatric embryonal brain tumor patients with
both T1PG and FLAIR preoperative imaging were identified (Table 1). The median age at diagnosis was 6.9 years
(interquartile range [IQR], 4.1-13.0 years). The median
follow-up period was 5.2 years (IQR, 2.7-8.0 years), and
the median overall survival period was 2.3 years (95% CI,
1.4-5.6 years). With respect to tumor histology, 22 patients
(64.7%) had medulloblastoma, 6 (17.6%) had pineoblastoma, and 6 (17.6%) had sPNET. Four patients
(11.8%) had neuraxis or distant metastasis at presentation.
Twenty-seven patients (79.4%) underwent GTR and 7
(20.6%) underwent subtotal resection. Before preoperative
imaging, 2 patients (6%) underwent shunt placement.
Adjuvant radiation therapy was administered in 29 patients
(85.2%), and 27 patients received craniospinal irradiation
(79.4%), with a median tumor bed dose of 55.8 Gy (IQR,
54.0-55.8 Gy) and median craniospinal dose of 23.4 Gy
(IQR, 23.4-36.0 Gy). Upfront radiation therapy was
administered in 27 patients (79.4%), and 2 patients (2.9%)
were treated with delayed radiation therapy. Adjuvant
chemotherapy was used in 32 patients (94.1%), with a
median of 5 cycles (IQR, 4-8 cycles), and 6 patients
(17.6%) underwent stem cell transplant. During the followup period, 9 tumors recurred (26.5%), with a median time
to recurrence of 2.2 years (95% CI, 1.5-3.0 years), and 6
patients died (17.6%) (Fig. 1). After recurrence, 9 patients
(26.5%) received salvage treatment (Table 1).
Patient age
Tumor size and qualitative radiographic metrics of
enhancement and heterogeneity are associated with pediatric embryonal brain tumor patient age (26, 30, 57). Our
morphologic quantitative findings similarly revealed that
tumor volume (P Z .03, T1PG) and maximum 3D diameter
(P Z .02, T1PG) were distinctly smaller with increased age
(Table 3, Fig. 3A). Likewise, we observed that tumor
enhancement intensity, as measured by normalized mean
intensity, increased with increasing patient age on T1PG
(P Z .05), which was again consistent with qualitative
evidence (57). We observed that 4 markers of heterogeneity
of embryonal brain tumors increased in older patients, as
evidenced by increased gray-level co-occurrence matrix
contrast (P Z .003, T1PG; P Z .01, FLAIR) and dissimilarity (P Z .002, T1PG), as well as neighboring gray tone
contrast (P Z .003, T1PG) and complexity (P Z .001,
T1PG). Of note, 2 of the markers of heterogeneity
decreased with age as shown by neighboring gray tone
busyness (P Z .03, T1PG) and gray-level size zone matrix
variance (P Z .02, T1PG). In addition, markers of heterogeneity were highly correlated with one another
Pediatric embryonal brain tumor radiomics
7
(Figs. E1 and E2, available online at www.redjournal.org).
Generally, age prediction on radiomic analysis favored
T1PG (Table 3). Overall, these findings support qualitative
assessments indicating that embryonal brain tumors are
usually more heterogeneous in older patients (57). Moreover, the radiomic differences we observed are consistent
with the conception that adult and pediatric embryonal
brain tumors have distinct biological characteristics (58).
Histology
Qualitative radiographic studies have identified distinct
characteristics for medulloblastoma, pineoblastoma, and
sPNET histologies (6, 16, 59-61). Broadly, medulloblastomas are typically hypointense on T1 with heterogeneous
contrast enhancement, isointense to hypointense on FLAIR
with surrounding edema, and hyperintense with reduced
diffusion on diffusion-weighted imaging (DWI) (6, 16, 59).
Pineoblastomas tend to appear as large irregular masses
with a cystic or necrotic component that are isointense to
hypointense on T1 with heterogeneous enhancement and
isointense on T2 with restricted diffusion owing to high
cellular packing on DWI (61). Finally, sPNETs are variable
on T1 with heterogeneous contrast enhancement, a high
solid signal on T2 with cystic components, and a markedly
low level of peritumoral vasogenic edema (60). Preliminary
data have suggested that multimodal MRI with DWI and
magnetic resonance spectroscopy can be used to identify
pediatric central nervous system tumor type at diagnosis
and better detect recurrence (62, 63), but with respect to
quantitative radiomic features, there are no prior reports of
textural analysis among pediatric embryonal brain tumors.
Our quantitative radiomic analyses demonstrated that size
is a key feature for differentiating pediatric embryonal brain
tumors. Not surprisingly, 2 separate measures of tumor size,
including volume (P < .03, T1PG; P Z .05, FLAIR) and
maximum 3D diameter (P < .03, T1PG; P Z .04, FLAIR)
were associated with specific embryonal brain tumor histologies (Table 3; Fig. 2A; Fig. E3, available online at www
.redjournal.org). In that regard, we observed that sPNETs
(126.3 cm3; IQR, 33.7-167.4 cm3) were significantly larger
than either medulloblastomas (34.6 cm3; IQR, 21.2-39.0
cm3) or pineoblastomas (3.8 cm3; IQR, 3.1-4.5 cm3) (Tables
E19 and E20, available online at www.redjournal.org).
Similarly, pineoblastomas (2.94 cm; IQR, 2.59-3.34 cm) had
a significantly smaller maximum 3D diameter than sPNETs
(8.92 cm; IQR, 5.70-10.70 cm) or medulloblastomas (6.00
cm; IQR, 5.50-6.33 cm). Clinically, sPNETs are often larger
at the time of detection because of their location and lower
likelihood of causing hydrocephalus compared with pineoblastomas or medulloblastomas. Of note, tumors were
larger on FLAIR than on T1PG for volume (P < .05)
(Table 3). For example, medulloblastomas had a higher
volume on FLAIR (44.8 cm3; IQR, 30.0-57.9 cm3) than on
T1PG (34.6 cm3; IQR, 21.2-39.0 cm3), which is consistent
with the surrounding infiltrative disease commonly seen
8
International Journal of Radiation Oncology Biology Physics
Hara et al.
Table 3 Univariate analysis of imaging biomarkers from primary pediatric embryonal brain tumors for dependent variables of age,
histology, M stage, or recurrence
Age: P value
M status
T1PG
Morphologic volume
Morphologic compactness
Morphologic sphericity
Morphologic maximum 3D diameter
Nondiscretized normalized mean intensity
Nondiscretized intensity variance
Gray-level co-occurrence matrix contrast
Gray-level co-occurrence matrix dissimilarity
Gray-level size zone matrix variance
Gray-level size zone matrix entropy
Neighborhood gray tone coarseness
Neighborhood gray tone contrast
Neighborhood gray tone busyness
Neighborhood gray tone complexity
Neighborhood gray tone strength
T1PG
FLAIR
P value
.027*
.509
.523
.021*
.054*
.486
.003*
.002*
.02*
.155
.143
.007*
.03*
.001
.11
.062
.644
.656
.091
.532
.306
.013*
.022*
.098
.125
.138
.063
.067
.001
.11
.941
.64
.57
.465
.863
.799
.401
.415
.704
.198
.21
.433
.77
.591
.183
AUC (95% CI)
0.69
0.46
0.46
0.64
0.53
0.45
0.59
0.55
0.36
0.66
0.69
0.58
0.68
0.57
0.70
(0.50-0.88)*
(0.20-0.72)
(0.20-0.72)
(0.46-0.83)
(0.26-0.80)
(0.13-0.78)
(0.32-0.85)
(0.29-0.82)
(0.14-0.59)
(0.44-0.88)
(0.51-0.87)*
(0.31-0.84)
(0.48-0.89)*
(0.29-0.84)
(0.50-0.89)*
FLAIR
P value
.678
.485
.502
.196
.336
.402
.128
.155
.761
.144
.21
.132
.84
.591
.183
AUC (95% CI)
0.74
0.61
0.61
0.73
0.63
0.67
0.73
0.67
0.31
0.74
0.75
0.77
0.75
0.57
0.70
(0.56-0.92)*
(0.38-0.85)
(0.38-0.84)
(0.55-0.91)*
(0.32-0.94)
(0.43-0.91)
(0.55-0.91)*
(0.48-0.85)*
(0.12-0.50)
(0.56-0.92)*
(0.58-0.92)*
(0.58-0.97)*
(0.57-0.93)*
(0.42-0.84)
(0.56-0.92)*
Abbreviations: 3D Z 3-dimensional; AUC Z area under curve; CI Z confidence interval; FLAIR Z T2-weighted fluid-attenuated inversion recovery;
MB Z medulloblastoma; PB Z pineoblastoma; sPNET Z supratentorial primitive neuroectodermal tumor; T1PG Z T1-weighted postcontrast.
Logistic regression analysis was used for M status. Cox regression was used for recurrence. Linear regression was used to assess age. Multinomial
regression was used to assess histology. The area under the receiver operating characteristic curve was used for neuraxis metastases, and the Harrell C
statistic was used for recurrence.
* Significant P, AUC, or C-index values.
around the bulky core of these tumors (Tables E19 and E20,
available online at www.redjournal.org) (16).
Pediatric embryonal brain tumor histologies were also
markedly different with respect to 8 of the 9 textural features (Fig. 2A). Both gray-level co-occurrence features and
size zone matrix, which are markers of tumor heterogeneity
(64), demonstrated unique features among histologic subgroups. Gray-level co-occurrence matrix features, which
express how combinations of discretized gray levels of
neighboring pixels or voxels in a 3D volume are distributed
along one of the image directions, were distinct for contrast
(P Z .03, T1PG; P Z .009, FLAIR) and dissimilarity
(P Z .02, T1PG; P Z .01, FLAIR) among medulloblastomas and pineoblastomas. Similarly, size zone matrix,
which measures the number of groups of connected voxels
with a specific discretized gray level and size, was different
for variance (P Z .03, T1PG) and entropy (P Z .01,
T1PG). In addition, neighboring gray tone, another marker
of heterogeneity, differed among medulloblastomas and
pineoblastomas in terms of radiomic coarseness (P Z .005,
T1PG; P < .02, FLAIR), contrast (P Z .02, T1PG and
FLAIR), busyness (P < .05, T1PG), and strength
(P Z .006, T1PG; P Z .02, FLAIR). Pineoblastoma
radiomic features generally had higher scores for heterogeneity (contrast, 66.5 [IQR, 36.8-86.3] on T1PG; dissimilarity, 6.37 [IQR, 4.61-7.40] on T1PG) (Tables E19 and
E20, available online at www.redjournal.org), which is
consistent with the qualitative radiographic literature (65).
Conversely, medulloblastoma radiomic features had
comparatively less evidence of heterogeneity (contrast,
30.2 [IQR, 27.7-37.1] on T1PG; dissimilarity, 4.11 [IQR,
3.93-4.52] on T1PG) (10). Thus, quantitative radiomics
demonstrates that pineoblastoma is the most internally
heterogeneous pediatric embryonal brain tumor and that
medulloblastoma is the most homogeneous.
Metastases
Spinal MRI and lumbar puncture for CSF analysis are
standard of care for metastatic staging in pediatric patients
with embryonal brain tumor (66). However, CSF analysis is
associated with a high false-negative rate, and postoperative
or postelumbar puncture spinal imaging is associated with
a high false-positive rate because of surgical and hematologic debris and reactive change (67, 68). Moreover, up to
50% of patients with pediatric embryonal brain tumor have
equivocal spinal MRI studies (69). As a result, CSF analysis and spinal MRI results are often discordant (66). To
address this unmet need, qualitative radiographic studies
have suggested that neuroimaging may provide an additional source of evidence of neuraxis metastasis (35, 36).
Indeed, we observed that T1PG and FLAIR 3D radiomic
features of primary pediatric embryonal brain tumors
trended toward discriminating between localized (M0) and
disseminated (Mþ) disease. For patients with metastases,
those with a larger primary tumor size were observed to
have a higher likelihood of neuraxis metastases (maximal
tumor diameter AUC Z 0.74, FLAIR). In addition, we
observed a decrease in primary tumor heterogeneity, as
Volume - Number - 2018
Pediatric embryonal brain tumor radiomics
9
Table 3 Univariate analysis of imaging biomarkers from primary pediatric embryonal brain tumors for dependent variables of age,
histology, M stage, or recurrence (continued)
Histology: P value
Recurrence
PB vs MB
sPNET vs MB
T1PG
T1PG
FLAIR
T1PG
FLAIR
P value
.063
.161
.167
.029*
.054*
.189
.031*
.021*
.032*
.013*
.005*
.023*
.045*
.141
.006*
.111
.391
.379
.142
.709
.307
.009*
.012*
.158
.996
.017*
.017*
.103
.051*
.02*
.026*
.909
.92
.029*
.145
.645
.902
.508
.042*
.993
.739
.991
.037*
.541
.768
.050*
.535
.542
.043*
.233
.864
.688
.38
.129
.322
.49
.706
.087
.411
.808
.656
.591
.623
.323
.221
.888
.3
.231
.394
.173
.297
.351
.862
.357
.34
FLAIR
C index (95% CI)
0.63
0.53
0.53
0.62
0.63
0.52
0.58
0.60
0.59
0.67
0.63
0.59
0.62
0.60
0.62
(0.42-0.84)
(0.31-0.74)
(0.31-0.74)
(0.41-0.82)
(0.43-0.83)
(0.29-0.74)
(0.39-0.78)
(0.39-0.82)
(0.37-0.81)
(0.45-0.89)
(0.41-0.85)
(0.35-0.83)
(0.40-0.84)
(0.37-0.84)
(0.39-0.85)
P value
.886
.208
.205
.261
.572
.436
.186
.219
.608
.271
.225
.18
.95
.357
.34
C index (95% CI)
0.71
0.62
0.62
0.71
0.49
0.41
0.57
0.56
0.43
0.67
0.70
0.62
0.71
0.61
0.72
(0.53-0.88)*
(0.41-0.83)
(0.41-0.83)
(0.54-0.89)*
(0.25-0.73)
(0.21-0.60)
(0.37-0.78)
(0.36-0.75)
(0.2-0.65)
(0.45-0.90)*
(0.53-0.87)*
(0.40-0.84)
(0.52-0.90)*
(0.39-0.84)
(0.54-0.91)
measured by textural features such as neighborhood gray
tone coarseness (AUC Z 0.7, T1PG and FLAIR) and
strength (AUC Z 0.7, T1PG and FLAIR) (Tables 2 and 3,
Fig. 3B). Neuraxis metastasis at presentation trended toward decreased primary tumor radiomic metrics of heterogeneity in additional radiomic FLAIR features, such as
neighborhood gray-level co-occurrence matrix contrast
(P Z .1, AUC Z 0.8, FLAIR) and dissimilarity (AUC Z
0.7, FLAIR), as well as neighborhood gray tone contrast
(P Z .1, AUC Z 0.8, FLAIR), which also delineated
metastatic tumors on FLAIR but not on T1PG. Consistently, FLAIR characteristics of primary pediatric embryonal brain tumors are thought to have greater potential to
identify patients with distant neuraxis metastasis than
T1PG imaging features (31). Of note, these results were not
statistically significant on logistic regression, likely because
of the limited sample size. This limitation notwithstanding,
our data suggest that the radiomic features of primary tumors may provide an additional source of information for
differentiating between localized and disseminated pediatric embryonal brain tumors.
predict prognosis of pediatric embryonal brain tumors (70).
From the standpoint of quantitative radiomics, we discovered that patients who have recurrence trended toward
having larger tumors at the time of diagnosis (Fig. 3C, left),
as denoted by maximum 3D diameter (AUC Z 0.7,
FLAIR) and volume (AUC Z 0.7, FLAIR), and were more
likely to ultimately develop recurrent disease (Fig. 3C,
right). In addition, we observed that decreased heterogeneity, as measured by neighborhood gray tone coarseness
(AUC Z 0.7), contrast (AUC Z 0.7), and busyness (AUC
Z 0.7), was associated with disease recurrence on FLAIR.
Of note, recurrence was also positively associated with size
zone matrix entropy (AUC Z 0.7, T1PG), as observed for
metastases at presentation. Unfortunately, these patterns
were not significant on univariate analysis, likely again
because of the limited sample size (Table 3). Nevertheless,
our findings are clinically important because 80% of deaths
in our patients resulted from progressive disease (Table 1,
Fig. 1). Thus, our data suggest that patients with decreased
heterogeneity and/or larger tumors on MRI may benefit
from more aggressive therapy.
Recurrence
Limitations and future directions
Beyond patient demographic characteristics and clinical
features at diagnosis, we explored the role of quantitative
3D MRI analysis to predict clinical outcomes (Tables 2
and 3). Previous groups have demonstrated the value of
qualitative MRI features to screen for recurrence and
Aside from the retrospective nature of our study, we were
principally limited by a small data set, which may have
contributed to nonsignificant findings on univariate analysis
(Table 3). Our strict inclusion criteria of only pediatric
patients with embryonal brain tumor who had preoperative
10
International Journal of Radiation Oncology Biology Physics
Hara et al.
A
7-year-old
18-year-old
B
C
Merge
T2-Fluid Attenuated
Inversion Recovery
T1-Post
Contrast
2-year-old
Fig. 3. Representative magnetic resonance imaging scans of medulloblastoma in patients of various ages: axial
T1-weighted postcontrast (top and bottom rows) and axial T2-weighted fluid-attenuated inversion recovery (FLAIR) (middle
row) magnetic resonance images (A), neuraxis metastases at presentation (B), and tumor recurrence (C). (A) Images of a
2-year-old patient (left), 7-year-old patient (middle), and 18-year-old patient (right) with medulloblastomas, demonstrating a
less contrast-enhancing tumor component in the older patient. (B) Images of a 6-year-old patient with medulloblastoma
presenting with neuraxis metastases associated with an increased maximal 3-dimensional primary tumor diameter and
decreased markers of primary tumor heterogeneity. (C) Axial T2-weighted FLAIR magnetic resonance images of a 4-yearold patient who initially presented with a right frontal supratentorial primitive neuroectodermal tumor (left) and who underwent a subtotal resection and subsequently received high-dose adjuvant chemotherapy (thiotepa and carboplatin) with
stem cell transplant (middle); the patient had a local recurrence 26 months after surgery (right).
3D MRI certainly contributed to this limitation. Thus,
because of the small sample size and exploratory nature of
our study, we did not correct for multiple comparisons and
we acknowledge that these findings cannot directly show
statistical significance. In addition, given the large number
of patients with medulloblastoma in comparison with
pineoblastoma or sPNET, we recognize that our results
favor findings for medulloblastoma, with a decreased
sensitivity for pineoblastoma or sPNET. Moreover, we were
unable to use assigned costs to address class imbalance
because of the limited sample size, but we recognize this as
a potential avenue of future investigation. However, we
observed several radiomic features that were associated
with key clinical parameters on both T1PG and FLAIR
sequences for each of these tumors; thus, we are optimistic
that our findings will be generalizable to the larger population of pediatric patients with embryonal brain tumor.
The patients included in this study were referred from
multiple centers with different MRI scanners, which have
inherent differences in image characteristics. To internally
account for this variation, the normal brain contour was
used to first normalize the brain and then discretize the gray
level to account for magnetic resonance scan signal variation. Moreover, we observed no systematic differences in
MRI radiomic characteristics when patients were dichotomized according to magnet strength (Tables E1 and E2,
available online at www.redjournal.org). However, we
observed a difference in radiomic characteristics, specifically for gray-level co-occurrence size zone matrix between magnet strengths, but there was no change in the
results after standardizing the gray-level co-occurrence size
zone matrix features internally with respect to magnet
strength. Recent studies have suggested that interscanner
variability does not prohibit the use of a quantitative
radiomic analysis but have noted that single-scanner studies
may improve predictive quantitative radiomic analysis
outcomes. Moreover, these results indicate that similar
textural features are enhanced by magnetic resonance images obtained at different hospitals (71). However, more
work must be done to further explore the technique to
implement multi-institutional transferability of these
images.
Volume - Number - 2018
We identified significant correlation between tumor size
and features for heterogeneity (Figs. E1 and E2, available
online at www.redjournal.org). These results suggest that
even stronger associations may be identified with a larger
data set. With the high degree of correlation within feature
classes and between feature size and heterogeneity, a larger
data set is needed to assess intraclass correlation and
identify underlying components that better characterize
correlated features. Because there is currently no established method to select textural features that correlate with
specific histopathologic or clinical correlates such as heterogeneity, the results of our study should be taken with
caution. However, the features selected were supported
mathematically, conceptually, and biologically based on the
current understanding of quantitative radiomic features, as
well as radiographic correlates, which were consistent with
our quantitative radiomic feature analysis. These results
support the notion that there is a clinical need and potential
for further establishment of radiomic textural features with
their clinical correlate.
We observed that medulloblastoma, pineoblastoma, and
sPNET are associated with markedly different radiomic
features, and we cannot rule out the possibility that associations stemmed from histologic differences as opposed to
clinical outcomes. We also recognize that genetic subgroups have recently played a role in diagnostic, prognostic, and predictive outcomes of these patients (48).
Because there are currently no studies describing the
impact of quantitative imaging features on prognosis in the
context of multiple histologies and varied risk categories,
these exploratory results support the potential for future
multicenter studies to further quantify and validate the
relationship between clinical outcomes and 3D MRI
quantitative features.
Conclusions
Pediatric embryonal tumor risk stratification is based on
clinical and molecular factors. Previous studies have shown
that pediatric embryonal tumors present with distinct
qualitative imaging characteristics (23-25), but little is
known about the relationship between quantitative imaging
characteristics and clinical outcomes in this patient population. We performed quantitative 3D T1PG and FLAIR
MRI radiomic analyses of pediatric embryonal brain tumors in the context of patient demographic characteristics
and clinical outcomes. Our results identified radiomic features that are consistent with known qualitative radiographic features associated with patient age and tumor
histology, such as contrast enhancement and markers of
tumor heterogeneity. In addition, our results identified potential predictive radiomic features of neuraxis metastasis
and recurrence such as tumor size and decreased markers of
tumor heterogeneity. Given the limitation of sample size
and the limited molecular subgrouping data, these findings
should be taken with caution, and future multicenter studies
Pediatric embryonal brain tumor radiomics
11
are needed to validate these results. Overall, our data support a potential paradigm to use 3D textural analysis as an
objective measurement of qualitative radiographic features
and suggest that MRI radiomics has potential to identify
radiomic risk features for patients who may benefit from
aggressive adjuvant therapy.
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