Cancers,
Год журнала:
2024,
Номер
16(16), С. 2872 - 2872
Опубликована: Авг. 18, 2024
This
study
aims
to
evaluate
the
repeatability
of
radiomics
and
dosiomics
features
via
image
perturbation
patients
with
cervical
cancer.
A
total
304
cancer
planning
CT
images
dose
maps
were
retrospectively
included.
Random
translation,
rotation,
contour
randomization
applied
before
feature
extraction.
The
was
assessed
using
intra-class
correlation
coefficient
(ICC).
Pearson
(r)
adopted
quantify
between
characteristics
repeatability.
In
general,
lower
compared
features,
especially
after
small-sigma
Laplacian-of-Gaussian
(LoG)
wavelet
filtering.
More
repeatable
(ICC
>
0.9)
observed
when
extracted
from
original,
Large-sigma
LoG
filtered,
LLL-/LLH-wavelet
filtered
images.
Positive
correlations
found
entropy
high-repeatable
number
in
both
(r
=
0.56,
0.68).
Radiomics
showed
higher
features.
These
findings
highlight
potential
for
robust
quantitative
imaging
analysis
patients,
while
suggesting
need
further
refinement
approaches
enhance
their
European Radiology,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 10, 2025
Abstract
Objectives
Conduct
a
systematic
review
and
meta-analysis
on
the
application
of
Radiomics
Quality
Score
(RQS).
Materials
methods
A
search
was
conducted
from
January
1,
2022,
to
December
31,
2023,
for
reviews
which
implemented
RQS.
Identification
articles
prior
2022
via
previously
published
review.
scores
individual
radiomics
papers,
their
associated
criteria
scores,
these
all
readers
were
extracted.
Errors
in
RQS
noted
corrected.
The
papers
matched
with
publication
date,
imaging
modality,
country,
where
available.
Results
total
130
included,
quality
117/130
(90.0%),
98/130
(75.4%),
multiple
reader
data
24/130
(18.5%)
3258
correlated
study
date
publication.
Criteria
scoring
errors
discovered
39/98
(39.8%)
articles.
Overall
mean
9.4
±
6.4
(95%
CI,
9.1–9.6)
(26.1%
17.8%
(25.3%–26.7%)).
positively
year
(Pearson
R
=
0.32,
p
<
0.01)
significantly
higher
after
(year
2018,
5.6
6.1
(5.1–6.1);
≥
10.1
(9.9–10.4);
0.01).
Only
233/3258
(7.2%)
50%
maximum
different
across
modalities
(
Ten
year,
one
negatively
correlated.
Conclusion
adherence
is
increasing
time,
although
vast
majority
studies
are
developmental
rarely
provide
high
level
evidence
justify
clinical
translation
proposed
models.
Key
Points
Question
What
have
achieved
has
it
increased
sufficient?
Findings
extracted
resulted
score
6.4.
time.
Clinical
relevance
Although
many
not
demonstrated
sufficient
translation.
As
new
appraisal
tools
emerge,
current
role
may
change.
Frontiers in Oncology,
Год журнала:
2025,
Номер
14
Опубликована: Янв. 14, 2025
Purpose
To
provide
a
detailed
pooled
analysis
of
the
diagnostic
accuracy
microRNAs
(miRNAs)
in
predicting
response
to
transarterial
chemoembolization
(TACE)
hepatocellular
carcinoma
(HCC).
Methods
A
comprehensive
literature
search
was
conducted
across
PubMed,
Embase,
Cochrane
Library,
and
Web
Science
identify
studies
assessing
performance
miRNAs
TACE
HCC.
Two
independent
reviewers
performed
quality
assessment
data
extraction
using
Quality
Assessment
Diagnostic
Accuracy
Studies
(QUADAS-2)
tool.
Pooled
sensitivity,
specificity,
positive
likelihood
ratio
(PLR),
negative
(NLR),
odds
(DOR),
area
under
summary
receiver
operating
characteristic
(SROC)
curve
were
calculated
bivariate
random-effects
model.
Subgroup
analyses
meta-regression
explore
potential
sources
heterogeneity,
including
sample
size,
criteria,
specimen
source,
evaluation
methods,
efficacy
interval
window,
geographical
location.
Results
Seven
studies,
comprising
320
HCC
responders
187
non-responders,
included
this
meta-analysis.
The
studied
miR-373,
miR-210,
miR-4492,
miR-1271,
miR-214,
miR-133b,
miR-335.
sensitivity
recurrence
after
0.79
[95%
CI:
0.72-0.84],
specificity
0.82
0.74-0.88].
DOR
17
9-33],
SROC
(AUC)
0.85
0.81-0.88],
indicating
excellent
accuracy.
revealed
significant
differences
based
on
criteria
Meta-regression
did
not
any
interstudy
heterogeneity.
Conclusion
MiRNAs
show
promise
as
tools
for
patients.
However,
their
clinical
application
requires
further
validation
larger
cohorts.
Future
research
should
focus
standardizing
RNA
selecting
consistent
endogenous
controls,
adopting
uniform
improve
reliability
reduce
variability.
Abstract
The
advent
of
radiomics
has
revolutionized
medical
image
analysis,
affording
the
extraction
high
dimensional
quantitative
data
for
detailed
examination
normal
and
abnormal
tissues.
Artificial
intelligence
(AI)
can
be
used
enhancement
a
series
steps
in
pipeline,
from
acquisition
preprocessing,
to
segmentation,
feature
extraction,
selection,
model
development.
aim
this
review
is
present
most
AI
methods
explaining
advantages
limitations
methods.
Some
prominent
architectures
mentioned
include
Boruta,
random
forests,
gradient
boosting,
generative
adversarial
networks,
convolutional
neural
transformers.
Employing
these
models
process
analysis
significantly
enhance
quality
effectiveness
while
addressing
several
that
reduce
predictions.
Addressing
enable
clinical
decisions
wider
adoption.
Importantly,
will
highlight
how
assist
overcoming
major
bottlenecks
implementation,
ultimately
improving
translation
potential
method.
Cancers,
Год журнала:
2024,
Номер
16(17), С. 3111 - 3111
Опубликована: Сен. 9, 2024
The
role
of
magnetic
resonance
imaging
(MRI)
in
rectal
cancer
management
has
significantly
increased
over
the
last
decade,
line
with
more
personalized
treatment
approaches.
Total
neoadjuvant
(TNT)
plays
a
pivotal
shift
from
traditional
surgical
approach
to
non-surgical
approaches
such
as
‘watch-and-wait’.
MRI
central
this
evolving
landscape,
providing
essential
morphological
and
functional
data
that
support
clinical
decision-making.
Key
MRI-based
biomarkers,
including
circumferential
resection
margin
(CRM),
extramural
venous
invasion
(EMVI),
tumour
deposits,
diffusion-weighted
(DWI),
regression
grade
(mrTRG),
have
proven
valuable
for
staging,
response
assessment,
patient
prognosis.
Functional
techniques,
dynamic
contrast-enhanced
(DCE-MRI),
alongside
emerging
biomarkers
derived
radiomics
artificial
intelligence
(AI)
potential
transform
offering
enhance
T
N
histopathological
characterization,
prediction
response,
recurrence
detection,
identification
genomic
features.
This
review
outlines
validated
MRI-derived
both
prognostic
predictive
significance,
while
also
exploring
management.
Furthermore,
we
discuss
‘watch-and-wait’
approach,
highlighting
important
practical
aspects
selecting
patients
Breast Cancer Targets and Therapy,
Год журнала:
2025,
Номер
Volume 17, С. 103 - 113
Опубликована: Янв. 1, 2025
Young
onset
breast
cancer,
diagnosed
in
women
under
50,
is
known
for
its
aggressive
nature
and
challenging
prognosis.
Precisely
forecasting
axillary
lymph
node
metastasis
(ALNM)
essential
customizing
treatment
plans
enhancing
patient
results.
This
research
sought
to
create
verify
a
clinical-radiomics
nomogram
that
combines
radiomic
features
from
Dynamic
Contrast-Enhanced
Magnetic
Resonance
Imaging
(DCE-MRI)
with
standard
clinical
predictors
improve
the
accuracy
of
predicting
ALNM
young
cancer
patients.
We
performed
retrospective
analysis
at
one
facility,
involving
creation
validation
two
stages.At
first,
medical
model
was
developed
utilizing
conventional
indicators
like
tumor
dimensions,
molecular
classifications,
multifocal
presence,
MRI-determined
ALN
status.A
more
detailed
subsequently
by
integrating
characteristics
derived
DCE-MRI
images.These
models
were
created
using
logistic
regression
analyses
on
training
dataset,
their
effectiveness
assessed
measuring
area
receiver
operating
characteristic
curve
(AUC)
separate
dataset.
The
surpassed
clinical-only
model,
recording
an
AUC
0.892
dataset
0.877
dataset.Significant
included
MRI-reported
status
select
features,
which
markedly
enhanced
model's
predictive
capacity.
Integrating
significantly
improves
prediction
providing
valuable
tool
personalized
planning.
study
underscores
potential
merging
advanced
imaging
data
insights
refine
oncological
models.
Future
should
expand
multicentric
studies
include
genomic
boost
nomogram's
generalizability
precision.
Cancers,
Год журнала:
2025,
Номер
17(5), С. 768 - 768
Опубликована: Фев. 24, 2025
Radiomics
has
seen
substantial
growth
in
medical
imaging;
however,
its
potential
optical
coherence
tomography
(OCT)
not
been
widely
explored.
We
systematically
evaluate
the
repeatability
and
reproducibility
of
handcrafted
radiomics
features
(HRFs)
from
OCT
scans
benign
nevi
examine
impact
bin
width
(BW)
selection
on
HRF
stability.
The
effect
using
stable
a
classification
model
was
also
assessed.
In
this
prospective
study,
20
volunteers
underwent
test-retest
imaging
40
nevi,
resulting
80
scans.
HRFs
extracted
manually
delineated
regions
interest
(ROIs)
were
assessed
concordance
correlation
coefficients
(CCCs)
across
BWs
ranging
5
to
50.
A
unique
set
identified
at
each
BW
after
removing
highly
correlated
eliminate
redundancy.
These
robust
incorporated
into
multiclass
classifier
trained
distinguish
basal
cell
carcinoma
(BCC),
Bowen's
disease.
Six
all
BWs,
with
25
emerging
as
optimal
choice,
balancing
ability
capture
meaningful
textural
details.
Additionally,
intermediate
(20-25)
yielded
53
reproducible
features.
six
achieved
90%
accuracy
AUCs
0.96
0.94
for
BCC
disease,
respectively,
compared
76%
0.86
0.80
conventional
feature
approach.
This
study
highlights
critical
role
enhancing
stability
provides
methodological
framework
optimizing
preprocessing
radiomics.
By
demonstrating
integration
diagnostic
models,
we
establish
promising
tool
aid
non-invasive
diagnosis
dermatology.