Review of tracer kinetic models in evaluation of gliomas using dynamic contrast-enhanced imaging

Abstract

Glioma is the most common type of primary malignant tumor of the central nervous system (CNS), and is characterized by high malignancy, high recurrence rate and poor survival. Conventional imaging techniques only provide information regarding the anatomical location, morphological characteristics, and enhancement patterns. In contrast, advanced imaging techniques such as dynamic contrast-enhanced (DCE) MRI or DCE CT can reflect tissue microcirculation, including tumor vascular hyperplasia and vessel permeability. Although several studies have used DCE imaging to evaluate gliomas, the results of data analysis using conventional tracer kinetic models (TKMs) such as Tofts or extended-Tofts model (ETM) have been ambiguous. More advanced models such as Brix’s conventional two-compartment model (Brix), tissue homogeneity model (TH) and distributed parameter (DP) model have been developed, but their application in clinical trials has been limited. This review attempts to appraise issues on glioma studies using conventional TKMs, such as Tofts or ETM model, highlight advancement of DCE imaging techniques and provides insights on the clinical value of glioma management using more advanced TKMs.

Full text

Review of tracer kinetic
models in evaluation of
gliomas using dynamic
contrast-enhanced imaging
Jianan Zhou
1,2,3
, Zujun Hou
4
, Chuanshuai Tian
1,2,3
,
Zhengyang Zhu
2,3
, Meiping Ye
2,3
, Sixuan Chen
2,3
,
Huiquan Yang
2,3
, Xin Zhang
1,2,3
*and Bing Zhang
1,2,3
1
Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical
University, Nanjing, China,
2
Institute of Medical Imaging and Artificial Intelligence, Nanjing University,
Nanjing, China,
3
Medical Imaging Center, Department of Radiology, Nanjing Drum Tower Hospital,
Affiliated Hospital of Medical School, Nanjing University, Nanjing, China,
4
The Jiangsu Key Laboratory
of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of
Sciences, Suzhou, China
Glioma is the most common type of primary malignant tumor of the central
nervous system (CNS), and is characterized by high malignancy, high recurrence
rate and poor survival. Conventional imaging techniques only provide
information regarding the anatomical location, morphological characteristics,
and enhancement patterns. In contrast, advanced imaging techniques such as
dynamic contrast-enhanced (DCE) MRI or DCE CT can reflect tissue
microcirculation, including tumor vascular hyperplasia and vessel permeability.
Although several studies have used DCE imaging to evaluate gliomas, the results
of data analysis using conventional tracer kinetic models (TKMs) such as Tofts or
extended-Tofts model (ETM) have been ambiguous. More advanced models
such as Brix’s conventional two-compartment model (Brix), tissue homogeneity
model (TH) and distributed parameter (DP) model have been developed, but their
application in clinical trials has been limited. This review attempts to appraise
issues on glioma studies using conventional TKMs, such as Tofts or ETM model,
highlight advancement of DCE imaging techniques and provides insights on the
clinical value of glioma management using more advanced TKMs.
KEYWORDS
glioma, dynamic contrast-enhanced, tracer kinetic model, diagnosis,
treatment response
Frontiers in Oncology frontiersin.org01
OPEN ACCESS
EDITED BY
Michael Albert Thomas,
University of California, Los Angeles,
United States
REVIEWED BY
Johannes Kerschbaumer,
Innsbruck Medical University, Austria
Vera Catharina Keil,
VU Medical Center, Netherlands
*CORRESPONDENCE
Xin Zhang
neuro_zx@163.com
RECEIVED 02 February 2024
ACCEPTED 29 May 2024
PUBLISHED 14 June 2024
CITATION
Zhou J, Hou Z, Tian C, Zhu Z, Ye M,
Chen S, Yang H, Zhang X and Zhang B (2024)
Review of tracer kinetic models in
evaluation of gliomas using dynamic
contrast-enhanced imaging.
Front. Oncol. 14:1380793.
doi: 10.3389/fonc.2024.1380793
COPYRIGHT
©2024Zhou,Hou,Tian,Zhu,Ye,Chen,Yang,
Zhang and Zhang. This is an open-access
article distributed under the terms of the
Creative Commons Attribution License (CC BY).
The use, distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
TYPE Review
PUBLISHED 14 June 2024
DOI 10.3389/fonc.2024.1380793

1 Introduction
Glioma originates from the neurostromal cells and is the most
common primary tumor of the central nervous system (CNS) (1). It
is characterized by wide-spread invasion and angiogenesis, with
short median survival duration and high recurrence rate (2). First-
line therapy for gliomas consists of radiotherapy, surgery,
concomitant chemoradiotherapy and adjuvant chemotherapy with
temozolomide (3), while immunotherapies are currently in the pre-
clinical and clinical stages of testing (4). The treatment response of
glioma is primarily evaluated on the basis of contrast enhanced T1-
weighted magnetic resonance imaging (MRI). However, the
correlation between changes in enhancement and the treatment
response is often confounded by the presence of radiation necrosis,
pseudoprogression or pseudoresponse (5–7)(Figures 1 and 2for
example), thereby warranting more advanced imaging techniques
for accurate assessment.
Dynamic contrast enhancement (DCE) imaging is a non-
invasive approach that can provide in vivo physiological and
metabolic information of tissues, and assess microvascular
features such as the degree of vascularity and disruption of
vascular wall permeability (8–11). DCE imaging data can be
analyzed in terms of both semi-quantitative and quantitative
parameters. The former includes time-intensity curve (TIC)
parameters, such as initial area under the curve (IAUC) and time
to peak, which are easy to derive (12) but challenging to reproduce
across studies due to differences in data acquisition and subject
conditions. DCE imaging data can be quantitatively analyzed using
a tracer kinetic model (TKM), a mathematical description of tracer
molecular transport within the tissue microenvironment that
derives quantitative values of various model parameters
pertaining to the tissue status. Several clinical studies have tested
DCE imaging for various applications, including glioma assessment
(13–17). Most of these studies used Tofts or extended-Tofts model
(ETM), which represents early development in DCE imaging. More
advanced TKMs have been developed (18,19), but have received
less attention in clinical studies.
In this review, the evaluation of glioma using conventional
TKMs, advances in DCE imaging techniques, and the clinical
potential of advanced TKMs in glioma management have
been discussed.
2 Materials and methods
2.1 Literature search and selection strategy
We searched for candidate articles describing the different
TKMs for gliomas in PubMed and Science Direct databases
published between January 1950 and May 2024. The search
strategy of key terms used was “((tracer kinetic model OR
pharmacokinetic model) OR (Tofts OR generalized kinetic
model) OR extended Tofts model OR (two-compartment model
OR two-compartment exchange model) OR tissue homogeneity
model OR distributed parameter model)) AND (glioma OR
glioblastoma) AND (dynamic contrast-enhanced OR DCE)”. The
studies were included based on the following inclusion criteria: (1)
clinical studies that employed DCE data in patients with gliomas, or
experimental animal studies that refer to pathophysiological
mechanism of microenvironment; (2) original research published
in English with the full text available; and (3) the theoretical basis of
tracer kinetic model and its application in glioma diagnosis and
evaluation after treatment. The following types of studies were
excluded: (1) unrelated or irrelevant studies, such as those that did
not employ DCE techniques to investigated gliomas; and (2) studies
focusing on other topics that are irrelevant to our research purpose.
After a detailed evaluation and screening,105 studies that met our
criteria were included and reviewed. The article selection process is
shown as a flowchart in Figure 3.
2.2 Fundamental concepts and primary
tracer kinetic models
Tracer kinetic models describe the transport of tracer molecular
within the tissue microenvironment. The physical space of the
movement of tracer molecules in the tissue is termed as
compartment. Typically, there are two well-defined compartments
ABC
FIGURE 1
A 59-year-old female with glioma of the left parieto-occipital lobe, treated with surgery and radiation therapy. (A) MR image before surgery; (B) 10
months after surgery and completion of radiation therapy showed enhancing lesion; (C) follow-up MR showed resolution of the lesion.
Zhou et al. 10.3389/fonc.2024.1380793
Frontiers in Oncology frontiersin.org02

in the field, namely, the compartment of intravascular plasma space
(IVPS), and the compartment of extravascular extracellular space
(EES). Furthermore, based on the distribution of the tracer, a
compartment in TKMs can be categorized as homogeneous or non-
homogeneous. For a homogeneous compartment, diffusive resistances
are assumed to be zero and the tracer is assumed to move fast and
distribute instantaneously and evenly upon arrival in the
compartment, which is also termed as a well-mixed compartment.
Since the distribution of the tracer is uniform in a well-mixed or
homogeneous compartment, the tracer concentration is constant in
space and only changes with time (18). TKMs with assumptions on
homogeneous or well-mixed compartment are often named as
lumped parameter models. In contrast, the tracer concentration
varies in a non-homogeneous compartment, and is therefore a
function of both space and time. In general, a homogeneous or
well-mixed compartment would simplify the modeling process and
computation. Thus, the early TKMs were developed based on this
assumption. However, the uniform distribution of a tracer in the
compartment would depend on rapid movement of tracer molecules
or sufficiently long measurement time, neither of which is viable in
clinical practice. Therefore, subsequent TKMs introduced a
concentration gradient in space to account for the variation in
tracer distribution, leading to the assumption of non-homogeneous
compartment. Nevertheless, tracer concentration in these models is
homogeneous in the radial direction and variable in the axial direction.
The meaning of these terms has been summarized in Table 1.
Primary tracer kinetic models, including Tofts, ETM, Brix’s
conventional two-compartment model (Brix), tissue homogeneity
model (TH), and distributed parameter model (DP), have been
listed in Table 2. It is worth pointing out that the notation of a TKM
could be different in different studies, and what is introduced here
follows largely the notations in earlier review papers on technical
aspects of tracer kinetic modeling (18,19). Tofts model was also
named as generalized kinetic (GK) model in (18). The model
developed by Brix and coauthors (24,25) has been denoted as
two-compartment exchange model (2CXM) in some literature (19,
35,36). However, this notation reflects also the fundamental
features of other models such as TH and DP, likely leading to
confusion in understanding the connection and the difference
among these TKMs. To emphasize that the exchange between
two-compartments is the common feature of these models, the
notation of the model proposed by Brix and coauthors is denoted as
ABCDEF
FIGURE 2
A 67-year-old male with WHO grade 2 glioma of the left basal ganglia and posterior horn of lateral ventricle, treated with surgery, radiotherapy plus
concomitant and adjuvant temozolomide. (A) MR image before surgery; (B) MR image after surgery; (C) 1 month after surgery; (D) 10 months after
surgery; (E) 13 months after surgery; (F) 15 months after surgery. Follow-up examinations demonstrated the presence of enhancing lesion in 10
months after surgery, which expanded in 13 months and reduced in 15 months.
FIGURE 3
Flowchart of the literature screening process.
Zhou et al. 10.3389/fonc.2024.1380793
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conventional two-compartment model (CC or CC2) in (18,37), or
as Brix model in (38). Since ETM is also of two-compartment by
nature, confusion could be arisen between CC and ETM. For clarity
and simplicity, this presentation adopts the notation of Brix model.
For completeness, the equations of the models are given
as follows:
Tofts model
Ctiss(t) = AIF ⊗Ktransexp −Ktrans
ve
t

where ⊗denotes the convolution operator.
Extended-Tofts model (ETM)
Ctiss(t) = AIF ⊗Ktransexp −Ktrans
ve
t

+vp

Brix’s conventional two-compartment (Brix) model, Equations 1a–1c
Ctiss(t) = AIF ⊗Fp½Aexp(at)+(1−A)exp(bt)(1a)
where
a
b
!
=1
2−PS
vp
+PS
ve
+Fp
vp
!
±ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
PS
vp
+PS
ve
+Fp
vp
!
2
−4PS
ve
Fp
vp
v
u
u
t
2
43
5
(1b)
A=
a+PS
vp+PS
ve
a−b (1c)
Distributed parameter (DP) model, Equation 2
Ctiss(t) = AIF ⊗
Fp
u(t)−ut−vp
Fp

+
ut−vp
Fp

1−exp −PS
Fp

1+ ∫t−vp
Fp
0exp −PS
ve
t

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
PS
ve
PS
Fp
1
t
sI12ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
PS
ve
PS
Fp
t
s
!
dt
"#()
8
>
>
>
<
>
>
>
:
9
>
>
>
=
>
>
>
;
(2)
where u(t) denotes the Heaviside unit-step function and I
1
is the
modified Bessel function.
Tissue homogeneity (TH) model, Equation 3
Ctiss(t) = AIF ⊗
Fp1−exp −PS
Fp
hi
exp −Fp
ve1−exp −PS
Fp
hi
t−Fp
vp
nono
(3)
Tofts assumes that IVPS is significantly smaller than EES, and
thus only involves EES. All other models are two-compartment
models. The movement of tracer molecules in tissue generally
involves intravascular transport and exchange between
intravascular and interstitial space. The former reflects blood flow
(CBF) and the latter indicates permeability of endothelial wall (PS).
Tofts and ETM utilize one parameter (K
trans
) to describe both
movements, whereas Brix, TH and DP differentiate between the two
and model them separately (24). Tofts, ETM and Brix models
assume the compartment to be well-mixed. In the TH model, EES is
well-mixed but IVPS is non-homogenous. On the other hand, both
compartments are non-homogenous in the DP model. The
TABLE 1 The concept and clinical significance of several terms.
Concept Meaning
compartment physical distribution space of tracer in the tissue
well-mixed compartment tracer distributes evenly throughout compartment
not well-
mixed compartment
tracer concentration changes with time and space
in compartment
IVPS intravascular compartment (intravascular
plasma space)
EES interstitial compartment (extravascular
extracellular space)
AIF arterial input function
VIF vascular input function
relative or
normalized parameter
parameter is normalized with respect to
contralateral healthy tissue
TABLE 2 Summary of primary tracer kinetic models.
Model References Compartment Transport
rate
parameter
Well-
mixed
assumption
Independent
parameters
Derived
parameters
Tofts [Kety et al. (20),Tofts et al. (21)] EES K
trans
well-mixed K
trans
,V
e
K
ep
ETM [Tofts et al. (22)] EES, IVPS K
trans
well-mixed K
trans
,V
e
,V
p
K
ep
Brix [Hayton et al. (23), Brix et al. (24),
Brix et al. (25), Larsson et al. (26)]
EES, IVPS CBF, PS well-mixed CBF, PS, V
e
,V
p
MTT, E
TH [Johnson (27)], Lawrence et al.
(28), Lawrence et al. (29), Lee
et al. (30)]
EES, IVPS CBF, PS well-mixed EES, not
well-mixed IVPS
CBF, PS, V
e
,V
p
MTT, E
DP [Bassingthwaighte et al. (31),
Larson et al. (32), Koh et al. (33),
Koh et al. (34)]
EES, IVPS CBF, PS not well-mixed in
both compartments
CBF, PS, V
e
,V
p
MTT, E
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parameters derived from these models are listed in Table 2. The
technical details pertaining to these models have been
comprehensively discussed in previous reviews (18,19). The
kinetic parameters were graphically presented in Figure 4.
2.3 Comparison between DCE MRI and
DCE CT
As a well-established imaging technique, DCE can be
performed by data acquisition on either MRI or CT scanners,
followed by image analysis using TKMs (39). Each imaging
modality has its merits and demerits. CT is fast in scanning and
allows acquisition of images with high resolution in both spatial and
temporal space, but at cost of X-ray radiation. MRI is advantageous
in better contrast in soft tissue and radiation-free. To reduce
radiation in DCE CT, non-uniform acquisition strategy can be
adopted to decrease the number of X-ray exposure in the design of
DCE CT protocol, with frequent scans during artery phase and less
frequent scans during delayed phase. A key difference in terms of
DCE language between two modalities lies in the calculation of
contrast concentration. Contrast concentration in DCE CT follows
simply a linear relationship with CT image signal, whereas the
relationship between contrast concentration in DCE MRI with MRI
signal is much more complicated, which relates to changes in T1
values of tissue before and after contrast injection. A practical
approach to estimating T1 value of tissue is the method of variable
flip angles and the computation involves several MR scanning
factors such as time of repetition (TR), time of echo (TE), flip
angle, homogeneity of B1 field. After deriving contrast
concentration from either CT or MR image signals, the analytical
process of concentration-time curve will be exactly the same
between DCE CT and DCE MRI. For a good appraisal on DCE
CT and DCE MRI, interested readers can refer to the review paper
(39), where it was shown that data acquisition and analysis were
well comparable despite inherent differences in signal production
and mechanism of tissue contrast enhancement.
3 Application of conventional tracer
kinetic models in glioma evaluation
3.1 Application in glioma grading
Various studies have analyzed the relationship between DCE
imaging parameters and glioma grading (40–49). Santarosa et al.
(50) used ETM of DCE MRI in a cohort of 26 glioma patients and
demonstrated that V
p
and K
trans
differed significantly between low-
grade and high-grade gliomas. Using the same model, Zhang et al.
(51) found that K
trans
and V
e
values calculated in 28 glioma patients
based on DCE MRI increased with advanced tumor grade, and
significant differences were observed between the low (I and II) and
high (III and IV) grade gliomas, as well as between grades II and III.
Awasthi et al. (52) applied Tofts model of DCE MRI to 76 glioma
patients and showed that K
ep
and V
e
could differentiate low-grade
from high-grade tumors, although there was no significant
correlation between K
trans
and the expression of MMP-9, which
plays a key role in the disruption of the blood-brain barrier (BBB)
by degrading extracellular matrix in order to facilitate tumor cell
infiltration and metastasis. In contrast, other studies (53,54) based
on DCE MRI have shown that K
trans
is the most effective parameter
for differentiating between glioma grades. These contradictory
findings can be attributed to the differences in the extent of BBB
disruption among different tumor grades, as well as the different
mechanisms underlying BBB disruption in infectious and
neoplastic pathologies.
3.2 Correlation with
immunohistochemical markers
Since the 2021 World Health Organization (WHO) guidelines
on the histological classification of central nervous system tumors
were published (55), molecular markers have been instrumental in
the diagnosis of gliomas. For instance, IDH mutation, 1p/19q co-
deletion, TERT promoter mutation, and EGFR gene amplification
are key biomarkers used for the classification of diffuse gliomas in
FIGURE 4
The graphic illustration of various kinetic parameters in tracer kinetic models. IVPS, intravascular compartment (intravascular plasma space); EES,
interstitial compartment (extravascular extracellular space); K
trans
, transfer constant; K
ep
, washout rate; CBF, tumor blood flow; V
e
, interstitial volume;
V
p
, blood volume; PS, permeability–surface area product.
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adults, and DCE TKMs have been used for evaluating the status of
these markers in glioma patients (52,56–63).
IDH mutation is associated with a survival benefit in glioma
patients (64). Wang et al. (65) retrospectively studied the IDH
mutation status of 30 patients with low grade gliomas (LGGs) using
ETM of DCE MRI, and showed that K
trans
,V
e
and V
p
were higher
values in the IDH wild-type compared to the IDH mutant LGGs. In
contrast, Brendle et al. (66) reported that DCE MRI kinetic
parameters derived using the same model did not distinguish
between IDH mutant and wild-type astrocytomas. Ahn et al. (67)
retrospectively studied the molecular markers in 132 LGG patients
using ETM of DCE MRI, and found no significant difference in the
DCE kinetic parameters between the IDH mutant and wild-
type gliomas.
MGMT promoter methylation predicts favorable prognosis
after alkylating drug-based chemotherapy in patients with IDH-
wild-type glioblastomas (68). Several studies have evaluated the
correlation between MGMT promoter methylation and DCE
kinetic parameters in gliomas (69–71). Ahn et al. (67) showed
that K
trans
,V
e
and V
p
values derived using ETM of DCE MRI were
significantlylowerinMGMTmethylatedLGGsthaninthe
unmethylated counterparts. Another study (70) using the Tofts
model of DCE MRI showed that K
trans
values were significantly
higher in the MGMT methylated tumors, while K
ep
and V
e
showed
no significant difference between gliomas with methylated and
unmethylated MGMT. Hilario et al. (69) analyzed 49 glioma
patients using ETM of DCE MRI, and did not detect any
significant differences in the DCE kinetic parameters of the
MGMT methylated and non-methylated tumors. Zhang et al. (71)
further showed that gliomas with non-methylated MGMT had
higher V
e
and K
trans
values with ETM of DCE MRI than those
with methylated MGMT.
3.3 Differential diagnosis of glioma, PCNSL,
and metastasis
Due to differences in clinical treatment and prognosis, it is
critical to distinguish between primary central nervous system
lymphoma (PCNSL), high-grade gliomas (HGGs), and metastatic
glioma (72–79). Xi et al. (80) used Tofts model of DCE MRI to
retrospectively analyze 8 cases of PCNSL, 21 cases of HGGs and 6
cases of metastasis, and detected significantly higher K
trans
and V
e
in
the PCNSL tumors compared to HGGs and metastatic tumors.
However, Kickingereder et al. (81) did not observe any significant
difference in the V
e
values of PCNSL and GBM using ETM of DCE
MRI. Lin et al. (82) showed that PCNSL had higher values of K
trans
using ETM of DCE MRI compared to GBM, although the difference
was not statistically significant. Jin et al. (83) used ETM of DCE
MRI to show that K
trans
had the highest specificity and sensitivity in
differentiating between GBM, PCNSL, and metastasis. Kang et al.
(84) calculated the V
p
in glioblastomas and PCNSL through ETM of
DCE MRI, and found that the values were significantly higher in the
former. In contrast, Abe et al. (43) used ETM of DCE MRI to
analyze 29 lesions (including glioma, metastatic tumor and
lymphoma) and found that V
p
was not helpful in differentiating
PCNSL from GBM.
3.4 Evaluation of treatment response
All patients with glioblastoma eventually relapse. It is
challenging to differentiate relapsed tumor from other treatment-
related changes during the follow-up of glioma patients (85–92).
Thomas et al. (93) found that V
p
and K
trans
measurements were
lower in glioblastoma patients with pseudoprogression compared to
those with relapsed lesions using ETM of DCE MRI. Yun et al. (94)
also applied ETM of DCE MRI to differentiate false progression
from true progression, and found that K
trans
and V
e
were
significantly higher in the latter, whereas V
p
value was similar for
both types of lesions. Jing et al. (95) applied the Tofts model
of DCE MRI to a retrospective analysis of 51 patients with
new enhancement lesions after standard radiotherapy and
chemotherapy after surgical resection, and found no significant
difference in V
e
and K
ep
between the pseudoprogression group and
the recurrence group.
4 Assessment of gliomas using
advanced tracer kinetic models
4.1 Glioma grading
Jain et al. (96,97) investigated glioma grading using perfusion
CT (PCT) with the TH model (27) to estimate permeability and
blood volume. While the rCBV increased more than the PS in
LGGs, grade 3 and especially grade 4 gliomas showed a greater
increase in PS compared to rCBV. The rate of change in the rCBV/
PS ratio may correlate to changes occurring at the tumor
microvasculature level. Both PS and CBV were higher in the
HGGs compared to the LGGs. Furthermore, PS can also be used
to differentiate WHO grade 3 glioma from grade 4 glioma. Tietze
et al. (35) presented a Bayesian framework for parameter
optimization of tracer kinetic modeling and compared the Brix
model and ETM in the grading of 42 untreated glioma patients.
Brix-derived V
p
showed the best diagnostic performance with AUC
of 0.97, followed by ETM-derived K
trans
with an AUC of 0.92.
However, the diagnostic performance of permeability parameter
was low, with AUC of 0.57.
4.2 Monitoring of treatment response
4.2.1 In vivo human studies
Jensen et al. (98) correlated parameters derived from a gamma
capillary transit time model of DCE MRI, which is based on the
distributed capillary approximated TH model (99,100), with
molecular markers of hypoxia, vascularity, proliferation, and
progression-free and overall survival (OS) in a cohort of 18
glioma patients. The derived parameters included tumor blood
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flow (CBF), extraction fraction (E), permeability–surface area
product (PS), transfer constant (K
trans
), washout rate (K
ep
),
interstitial volume (V
e
), blood volume (V
p
), capillary transit time
(tc), and capillary heterogeneity (a−1). The study showed that a−1,
tc, K
ep
, and V
p
were correlated with HIF-1aand VEGF expression,
whereas V
e
and a−1were correlated with OS. The other imaging
markers were not helpful in predicting OS. In particular, none of the
blood flow and permeability parameters (K
trans
, CBF, E, PS) showed
any correlation with patient outcome.
Lundemann et al. (101) explored the feasibility of predicting
tumor recurrence using multi-modal imaging based on DCE MRI at
the pre-radiotherapy stage in a cohort of 16 glioblastoma patients
using Brix-derived DCE parameters. The median MTT, V
p
,V
e
and
PS derived from scans prior to chemoradiotherapy showed
differences between recurring and non-recurring voxels.
Henriksen et al. (102) investigated the diagnostic value of Brix-
derived parameters of DCE MRI for short-term disease progression
in 60 anaplastic astrocytoma and glioblastoma patients with
suspected recurrence and evaluable outcome within 6 months of
follow-up as determined by histopathology, MRI findings or clinical
decision. The blood volume and vascular permeability were
significantly higher in the progressive lesions compared to the
non-progressive lesions. ROC analysis showed that blood flow
and blood volume had AUC values of 0.76 and 0.78 respectively,
which were higher than that of vascular permeability (0.68).
Larsen et al. (103) utilized Brix of DCE MRI to differentiate tumor
recurrence from radiation necrosis in 19 glioma patients following
surgery and radiation therapy, and demonstrated that an empirical
threshold of 2 ml/100 g for blood volume allowed detection of
regressing lesions with sensitivity and specificity of 100% each. In
comparison, neither blood flow nor permeability parameter could
discriminate between regressing and progressing lesions.
4.2.2 Ex vivo animal studies
Kiser et al. (36) applied the Brix model of DCE MRI to evaluate
test-retest repeatability and tumor response of a murine
glioblastoma model at 7 T to a combination therapy of
bevacizumab and fluorouracil. Test-retest experiments
demonstrated that there was no significant difference between the
scans in terms of the median values of parameters, except for K
trans
.
The compartmental volume fractions (V
e
and V
p
) remained more
consistent between scans while the vascular functional parameters
(CBF and PS) showed noticeable increase in values, likely due to
physiological changes in the tumor between scans. The tumor
volume in the control and treated groups did not differ
significantly at any time point, which corresponded to similar
tracer kinetic parameters in both groups.
Yeung et al. (104) investigated the efficacy of CT perfusion
imaging as an early biomarker of the response to stereotactic
radiosurgery in a rat glioma model using the TH model (27). Rats
with orthotopic C6 glioma tumors received either mock irradiation
or stereotactic radiosurgery delivered by Helical Tomotherapy. The
responders to stereotactic radiosurgery showed lower relative CBV,
and PS on day 7 post-stereotactic radiosurgery when compared to
controls and non-responders. Relative CBV and PS on day 7 were
correlated with the OS, and predicted survival with 92% accuracy.
4.3 Correlation to
immunohistochemical markers
Li et al. (105) applied the DP model of DCE MRI to a dataset
consisting of 55 glioma patients to assess glioma isocitrate
dehydrogenase (IDH) mutation. The IDH-mutant gliomas
showed significantly lower CBF, PS, V
p
, E and V
e
compared to
the IDH-wildtype gliomas. V
p
exhibited the best performance in
differentiating between IDH-mutant and IDH-wildtype gliomas
(AUC=0.92), followed by PS (AUC=0.82) and E (AUC=0.8).
Furthermore, the Tofts parameters K
trans
and V
e
were lower for
the IDH-mutant gliomas, and no significant difference was
observed in K
ep
. The AUCs of K
trans
,V
e
, and K
ep
were 0.69, 0.79,
and 0.55 respectively. These findings suggested that IDH-mutant
gliomas have lower vascularity, vessel permeability and blood flow
compared to IDH-wildtype gliomas, which may explain the better
outcomes in patients with IDH-mutated versus IDH-
wildtype gliomas.
5 Discussion
5.1 Advantage of advanced TKMs over
conventional ones
The fifth edition of the WHO Classification of CNS Tumors
(WHO CNS5), published in 2021, builds on the fourth edition
updated in 2016 and the work of the Consortium to Inform
Molecular and Practical Approaches to CNS Tumor Taxonomy,
further advancing the role of molecular diagnostics in the
classification of CNS tumors, but remains rooted in other
established methods for assessing tumor characteristics, including
histology and immunohistochemistry (55). Besides, the existing
evaluation of glioma features is still based on pathological biopsy as
the gold standard. Due to the high heterogeneity of gliomas and the
influence of non-uniform sampling error of tumors, the diagnostic
accuracy is limited by the subjective judgment of pathologists. In
2021 WHO CNS5, EGFR amplification is commonly seen in IDH
wild-type gliomas. EGFR is a tyrosine kinase receptor regulating cell
proliferation and differentiation by interacting with epidermal
growth factor (EGF) and tumor growth factor-a(TGF-a), and
can reflect microvascular proliferation in tumors (106). With the
development of MRI technologies, more abundant characteristics of
tumor microenvironment have been provided for the non-invasive
diagnosis of gliomas. DCE MRI is sensitive to the destruction of the
blood-brain barrier (BBB) and is closely related to microvascular
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proliferation and vascular wall permeability, which can effectively
evaluate tumor angiogenesis and obtain various kinetic parameters
reflecting tissue microcirculation function (9).
The focus of this review was to reveal the characteristics of the
immune microenvironment of gliomas based on the conventional
and advanced TKMs of DCE imaging, and to provide a non-
invasive method for the diagnosis and treatment response
evaluation of gliomas. Most studies on glioma assessment with
DCE imaging have used Tofts or ETM for image postprocessing and
data analysis, and the results show considerable variability. For
instance, some studies (50,107) have reported that ETM-derived V
p
is a useful imaging parameter that can discriminate between LGG
and HGG, whereas one study (51) showed that V
e
and not V
p
differed significantly between LGG and HGG. Likewise, Arevalo-
Perez et al. (42) showed that V
p
had the best discriminatory power
in glioma grading, whereas Jung et al. (44) reported that K
trans
was
the most significant pharmacokinetic parameter. ETM-derived
parameters can also distinguish IDH-mutant gliomas from IDH
wildtype gliomas (65), although some studies (66,67) have reported
contradictory findings. While ETM_V
p
was reported to be
significantly higher for glioblastomas compared to PCNSL in one
study (84), another study (43) showed that it could not differentiate
between the two lesions. Furthermore, K
trans
derived from the Tofts
model was demonstrated to be a promising discriminatory
biomarker for LGG relative to HGG (41,108), although Awasthi
et al. (52) found that K
trans
was not significantly different between
LGG and HGG. The variations in the results across the different
studies using Tofts or ETM, and the inconsistent performance of
K
trans
in particular, have been reported in earlier reviews (13,14).
Quantitative imaging biomarkers alliance (QIBA) has
recommended ETM for analyzing brain tumors through DCE
imaging (109). Both imaging hardware and DCE tracer kinetic
modeling have undergone significant advances over the years,
thereby allowing acquisition of DCE images with higher temporal
resolution, better signal-to-noise ratio, wider brain coverage and
increased spatial resolution, and enabling separate quantification of
CBF and PS. The advantages of a DP model over the Tofts model
have been demonstrated in the assessment of the IDH mutation
status in glioma (105). Recent test-retest experiments using Brix
(36) showed that only the median K
trans
was significantly different
between scans, which might justify the separate modeling of
intravascular transport and exchange between intra- and extra-
vascular space in Brix, instead of mixed modeling using one
transport rate parameter (K
trans
). HIF-1aexpression is
significantly associated with HGGs (110) and is an excellent
biomarker for glioma grading (111). Furthermore, gliomas are
typically characterized by a marked increase in the formation of
blood vessels with abnormal blood flow and increased vessel
permeability. In fact, blood volume is a promising imaging
biomarker for glioma grading (42,50,107). As expected, V
p
derived using a DP model showed good correlation with HIF-1a
expression (r= 0.747, P=0.043) (108). However, low correlation was
observed between HIF-1aexpression and ETM-derived V
p
(r= 0.149, P=0.219) in another study (111).
Physiological interpretation of K
trans
has been ambiguous in
clinical trials using Tofts or ETM, and is generally described as a
volume transfer constant that reflects vessel wall permeability (13,
14). Several theoretical descriptions have been provided to interpret
K
trans
as a combination of blood flow and vessel wall permeability
(112,113). Separate measurement of CBF and PS has been well
addressed in nuclear medicine (114), which is crucial to the
understanding of tissue hemodynamics, and has spurned the
development of more advanced DCE TKMs. In Figure 5,the
parametric maps of ETM and DP for a glioblastoma (GBM)
patient after surgery have been compared. The follow-up MR
images in Row 1 demonstrated the evolution of enhancing lesion.
DP-derived parameters in Row 2 showed that the enhancing lesion
was characterized by reduced blood flow, increase in PS and
marginal increase in V
e
compared to the contralateral tissue.
ETM-derived parameters in Row 2 manifested as reduced K
trans
,
marginal increase in V
p
, and increase in V
e
. As previously reported
(24), V
e
in the brain tumor reflects the transfer of tracer molecules
between intravascular and interstitial space due to insufficient
leakage from the intravascular space and attainment of steady
levels in the interstitial space during the insufficient scanning
period. Hence, ETM and DP illustrated similar pattern in vessel
wall permeability in this case. Nonetheless, the lower value of K
trans
,
which is generally explained as reduced vessel wall permeability,
may be indicative of reduced blood flow for this case.
The MR images of WHO grade 4 glioma at the right frontal lobe
treated with surgery and chemoradiotherapy are shown in Figure 6.
Small enhancing lesions appeared in the 31
th
month of follow-up
after surgery and continued to grow. DCE scan was performed in
the 37
th
month of follow-up, and analyzed using the distributed
parameter model. The parametric maps are shown in the second
row, which indicate reduced blood flow and increased vessel
permeability in the lesion compared to the contralateral tissue.
The patient underwent a second surgery, and histopathological
analysis revealed necrotic foci with no evidence of recurrent tumor.
In the traditional WHO classification of tumors, each tumor
type has a unique WHO grading, such as anaplastic astrocytoma,
which is automatically assigned to WHO grade III. The latest 2021
WHO Classification of CNS Tumors emphasizes the combination
of histological grades and molecular markers, such as Glioblastoma,
IDH-wild type; Astrocytoma, IDH-mutant; or Oligodendroglioma,
IDH-mutant & 1p/19q codeleted. IDH mutant astrocytoma
expands from the CNS in WHO grade 2–4. However, most of the
current studies are based on the 2016 WHO Classification, lacking
specific tumor pathological classification diagnosis (Table 3).
Therefore, future studies need to use more advanced MRI
technologies for further comprehensive diagnosis and stratified
reporting of gliomas.
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5.2 Standardization of DCE data acquisition
and postprocessing
Despite limited research on advanced DCE imaging TKMs for
glioma, the differences across studies are obvious. Brix-derived
blood volume has shown high discriminatory ability for
regressing lesions, whereas blood flow and permeability
parameters showed fairly low discriminating power in the
differential diagnosis of tumor recurrence (103). In contrast, a
recent study (102) demonstrated similar ability of blood flow and
blood volume in differentiating progressive lesions from non-
progressive lesions, and significantly higher permeability in the
former. Multiple factors can contribute to these discrepancies,
such as DCE imaging protocols, data post-processing, scanners,
operators in data processing, sample size and patient characteristics,
etc. QIBA has made the following recommendations to standardize
DCE MR imaging protocol (115): (1) 3D T1-weighted GRE
sequence, (2) variable flip angle (VFA) method to estimate T1
map and contrast agent concentration, (3) same sequence for pre-
contrast VFA scan and post-contrast dynamic scan, (4) temporal
resolution not lower than 4 s in most cases, (5) sufficiently long
scanning duration of about 6 min for permeability measurement.
The DCE protocols used in earlier studies often deviated from
QIBA recommendations, whereas the more recent studies (102)
have protocols closer to QIBA guidelines. DCE imaging data is
largely analyzed using commercial software programs that differ in
algorithm implementation, which may result in significant
differences in the estimated DCE parameters (116). When
analyzing DCE images, it is critical to accurately determine the
contrast concentration from image intensities. The relationship
FIGURE 5
59-year-old male with glioblastoma of the left temporal lobe. Row 1: follow-up MR examinations after surgery showed the evolution of enhancing
lesion. Row 2: parametric maps of blood flow CBF, vessel permeability PS and fractional volume of interstitial space V
e
as derived using DP.
Parametric maps of volume transfer constant K
trans
,V
p
and V
e
as derived using ETM.
ABCDEF
FIGURE 6
35-year-old female with WHO grade 4 glioma of the right frontal lobe. Postoperative pathology confirmed the enhanced lesion as radiation necrosis
(RN). Row 1: follow-up MR examinations after surgery showed the presence and evolution of enhancing lesion. (A) MR image after the first surgery;
(B) 30 months after surgery; (C)31 months after surgery; (D)33 months after surgery; (E)35 months after surgery; (F)37 months after surgery. Row 2:
parametric maps of blood flow CBF, mean transit time MTT, fractional volume of intravascular space CBV, fractional volume of interstitial space V
e
,
vessel permeability PS, and extraction ratio E as derived using DP model.
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between image intensity and concentration can be non-linear,
particular for MRI. In some software programs, linear assumption
between change in signal intensity and gadolinium concentration is
used to directly derive the contrast concentration-time curve from
signal intensity-time curve (42). The choice of vascular input
function (VIF) also affects the estimated values of DCE
parameters. It would be ideal to select VIF from DCE images for
each patient. Some software programs use population VIF or
empirical VIF, as summarized in the QIBA profile (109).
Standardization of imaging protocol and postprocessing
procedure is crucial in making DCE reproducible and bringing
forward the technology into clinical practice.
5.3 Cross-validation in DCE studies
ROC curve analysis is a useful tool for characterizing the
differential diagnostic ability of potential biomarkers, which can be
quantified by calculating the area under the ROC curve (AUC), or by
finding an optimal threshold from the ROC curve and determining the
associated sensitivity, specificity and accuracy. Most studies on DCE
imaging-based glioma assessment directly applied the ROC curve
method to the total data, and reported the performance of DCE
parameters. In clinical practice, however, the imaging data is
invariably subjected to different sources of perturbations. Therefore,
the resulting diagnostic metrics could be biased towards best matching
pattern in the current data, and their performance could be
significantly different when applied to new data. This phenomenon is
called overfitting in machine learning (117) and is particularly acute
whenthesamplesizeissmall,whichisoftenthecaseingliomastudies.
This issue is commonly resolved by cross-validation, which is typically
performed by dividing data into the training set and validation set, and
applying the information derived from the former to the latter. K-fold
cross-validation is the most frequently used cross-validation method in
machine learning. However, the leave-one-out cross-validation method
is recommended for glioma studies since the cohort size is usually
small. Shao et al. (38) used this method to assess the parameters of DCE
imaging in a small cohort of cervical cancer patients. The following
steps were used: (1) one data was left out, and the remaining data was
used as the training set to build a model and make prediction on the
excluded data, (2) the previous step was repeated for each data, and (3)
the differential diagnostic ability was quantified by summarizing all
predicted values. In the current DCE glioma studies, performance
quantification without cross-validation has resulted in variation
between the results of different research groups.
6 Conclusion
DCE imaging is effective in glioma grading and therapeutic
effect monitoring, and its parameters are potential imaging markers
for glioma diagnosis. However, the discrepancies between the
findings in different studies warrant further improvement and
validation of this technique with standardization of protocol
design, and data post-processing in multi-center and large
cohorts. Advanced DCE imaging techniques that allow separate
modeling of blood flow and vessel permeability have advantages
over conventional counterparts, although more studies are needed
to ascertain the clinical value.
Author contributions
JZ: Conceptualization, Formal analysis, Funding acquisition,
Investigation, Writing –original draft, Writing –review & editing.
ZH: Conceptualization, Formal analysis, Methodology, Software,
Writing –original draft, Writing –review & editing. CT:
Conceptualization, Formal analysis, Writing –original draft,
Writing –review & editing. ZZ: Conceptualization, Formal
analysis, Writing –original draft, Writing –review & editing.
TABLE 3 Application of tracer kinetic models in glioma grading and molecular markers according to WHO classification.
Model References Tasks WHO Classification
Tofts
Zhang et al. (2012) (51), Awasthi et al. (2012) (52) grading 2007
Ahn et al. (2014) (70) MGMT methylation 2007
Jia et al. (2021) (41), Zhao et al. (2017) (53) grading 2016
ETM
Santarosa et al. (2016) (50) grading 2016
Wang et al. (2020) (65), Brendle et al. (2018) (66) IDH mutation 2016
Ahn et al. (2023) (67) IDH mutation, EGFR, MGMT and TERT 2016
Hilario et al. (2019) (69) IDH mutation, ATRX, and MGMT 2016
Zhang et al. (2020) (71) IDH mutation, MGMT and TERT 2016
Anzalone et al. (2018) (40) grading, IDH mutation and 1p19q codeletion 2016
TH Jain et al. (2008,2015) (96,97) grading 2007
DP Li et al. (2020) (105) IDH mutation 2016
Zhou et al. 10.3389/fonc.2024.1380793
Frontiers in Oncology frontiersin.org10

MY: Conceptualization, Formal analysis, Writing –original draft,
Writing –review & editing. SC: Conceptualization, Writing –
original draft, Writing –review & editing. HY: Conceptualization,
Writing –original draft, Writing –review & editing. XZ:
Conceptualization, Funding acquisition, Methodology,
Supervision, Writing –original draft, Writing –review & editing.
BZ: Funding acquisition, Supervision, Writing –original draft,
Writing –review & editing.
Funding
The author(s) declare financial support was received for the
research, authorship, and/or publication of this article. This
research was funded by the National Science and Technology
Innovation 2030 –Major program of “Brain Science and Brain-
Like Research”(2022ZD0211800); the National Natural Science
Foundation of China (82271965, 82302172); General Project
Supported by Medical Science and technology development
Foundation, Nanjing Department of Health (YKK22083,
YKK23103), the Jiangsu Funding Program for Excellent
Postdoctoral Talent (2022ZB694); and fundings for Clinical Trials
from the Affiliated Drum Tower Hospital, Medical School of
Nanjing University (2022-LCYJ-PY-15, 2022-LCYJ-MS-03, 2021-
LCYJ-PY-36, 2022-LCYJ-MS-25, 2021-LCYJ-PY-20); Postgraduate
Research & Practice Innovation Program of Jiangsu
Province (JX22014155).
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
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