Applicability of Radiomics for Differentiation of Pancreatic Adenocarcinoma from Healthy Tissue of Pancreas by Using Magnetic Resonance Imaging and Machine Learning
Cancers,
Journal Year:
2025,
Volume and Issue:
17(7), P. 1119 - 1119
Published: March 27, 2025
This
study
analyzed
different
classifier
models
for
differentiating
pancreatic
adenocarcinoma
from
surrounding
healthy
tissue
based
on
radiomic
analysis
of
magnetic
resonance
(MR)
images.
We
observed
T2W-FS
and
ADC
images
obtained
by
1.5T-MR
87
patients
with
histologically
proven
training
validation
purposes
then
tested
the
most
accurate
predictive
that
were
another
group
58
patients.
The
tumor
segmented
three
consecutive
slices,
largest
area
interest
(ROI)
marked
using
MaZda
v4.6
software.
resulted
in
a
total
261
ROIs
each
classes
training-validation
174
testing
group.
software
extracted
304
features
ROI,
divided
into
six
categories.
was
conducted
through
feature
reduction
methods
five-fold
subject-wise
cross-validation.
In-depth
shows
best
results
Random
Forest
(RF)
Mutual
Information
score
(all
nine
are
co-occurrence
matrix):
an
accuracy
0.94/0.98,
sensitivity
specificity
F1-score
0.94/0.98
achieved
T2W-FS/ADC
group,
retrospectively.
In
0.69/0.81,
0.86/0.82,
0.52/0.70,
0.74/0.83
images,
machine
learning
approach
radiomics
relatively
high
differentiation
tissue,
which
could
be
especially
applicable
screening
purposes.
Language: Английский
The Value of Cerebral Blood Volume Derived from Dynamic Susceptibility Contrast Perfusion MRI in Predicting IDH Mutation Status of Brain Gliomas—A Systematic Review and Meta-Analysis
Diagnostics,
Journal Year:
2025,
Volume and Issue:
15(7), P. 896 - 896
Published: April 1, 2025
Background:
Dynamic
susceptibility
contrast
perfusion
MRI
(DSC-MRI)
is
a
promising
non-invasive
examination
to
predict
histological
and
molecular
characteristics
of
brain
gliomas.
However,
the
diagnostic
accuracy
relative
cerebral
blood
volume
(rCBV)
heterogeneously
reported
in
literature.
This
systematic
review
meta-analysis
aims
assess
mean
rCBV
derived
from
DSC-MRI
differentiating
Isocitrate
Dehydrogenase
(IDH)-mutant
IDH-wildtype
Methods:
A
comprehensive
literature
search
was
conducted
PubMed,
Web
Science,
EMBASE
up
January
2025,
following
PRISMA
guidelines.
Eligible
studies
CBV
values
treatment-naïve
gliomas
with
histologically
confirmed
IDH
status.
Pooled
estimates
standardized
differences
(SMDs),
odds
ratios
(DOR),
area
under
receiver-operating
characteristic
curve
(AUC)
were
computed
using
random-effects
model.
Heterogeneity
assessed
via
I2
statistic.
Meta-regression
analyses
also
performed.
Results:
An
analysis
18
(n
=
1733)
showed
that
significantly
lower
IDH-mutant
(SMD
-0.86;
p
<
0.0001).
The
pooled
AUC
0.80
(95%
CI,
0.75-0.90),
moderate
sensitivity
specificity.
revealed
no
significant
influence
acquisition
parameters,
although
flip
angle
trend
toward
significance
(p
0.055).
Conclusions:
Mean
reliable
imaging
biomarker
for
mutation
status
gliomas,
demonstrating
good
performance.
heterogeneity
parameters
post-processing
methods
limits
generalizability
results.
Future
research
should
focus
on
standardizing
protocols.
Language: Английский