Frontiers in Oncology,
Journal Year:
2022,
Volume and Issue:
12
Published: Feb. 4, 2022
The
aim
of
this
study
was
to
perform
a
meta-analysis
evaluate
the
diagnostic
performance
machine
learning(ML)-based
radiomics
dynamic
contrast-enhanced
(DCE)
magnetic
resonance
imaging
(MRI)
DCE-MRI
in
predicting
axillary
lymph
node
metastasis
(ALNM)
and
sentinel
metastasis(SLNM)
breast
cancer.English
Chinese
databases
were
searched
for
original
studies.
Quality
Assessment
Diagnostic
Accuracy
Studies
(QUADAS-2)
Radiomics
Score
(RQS)
used
assess
methodological
quality
included
pooled
sensitivity,
specificity,
odds
ratio
(DOR),
area
under
curve
(AUC)
summarize
accuracy.
Spearman's
correlation
coefficient
subgroup
analysis
performed
investigate
cause
heterogeneity.Thirteen
studies
(1618
participants)
meta-analysis.
DOR,
AUC
with
95%
confidence
intervals
0.82
(0.75,
0.87),
0.83
(0.74,
0.89),
21.56
(10.60,
43.85),
0.89
(0.86,
0.91),
respectively.
showed
significant
heterogeneity
among
There
no
threshold
effect
test.
result
that
ML,
3.0
T,
interest
comprising
ALN,
being
manually
drawn,
including
ALNs
combined
(SLN)s
groups
could
slightly
improve
compared
deep
learning,
1.5
tumor,
semiautomatic
scanning,
SLN,
respectively.ML-based
has
potential
predict
ALNM
SLNM
accurately.
diagnoses
between
is
major
limitation.
Current Oncology,
Journal Year:
2021,
Volume and Issue:
28(4), P. 2351 - 2372
Published: June 25, 2021
Radiomics
is
an
emerging
translational
field
of
medicine
based
on
the
extraction
high-dimensional
data
from
radiological
images,
with
purpose
to
reach
reliable
models
be
applied
into
clinical
practice
for
purposes
diagnosis,
prognosis
and
evaluation
disease
response
treatment.
We
aim
provide
basic
information
radiomics
radiologists
clinicians
who
are
focused
breast
cancer
care,
encouraging
cooperation
scientists
mine
a
better
application
in
practice.
investigate
workflow
as
well
outlook
challenges
recent
studies.
Currently,
has
potential
ability
distinguish
between
benign
malignant
lesions,
predict
cancer's
molecular
subtypes,
neoadjuvant
chemotherapy
lymph
node
metastases.
Even
though
been
used
tumor
diagnosis
prognosis,
it
still
research
phase
some
need
faced
obtain
translation.
In
this
review,
we
discuss
current
limitations
promises
improvement
further
research.
Journal of Magnetic Resonance Imaging,
Journal Year:
2023,
Volume and Issue:
59(2), P. 613 - 625
Published: May 18, 2023
Background
Radiomics
has
been
applied
for
assessing
lymphovascular
invasion
(LVI)
in
patients
with
breast
cancer.
However,
associations
between
features
from
peritumoral
regions
and
the
LVI
status
were
not
investigated.
Purpose
To
investigate
value
of
intra‐
radiomics
LVI,
to
develop
a
nomogram
assist
making
treatment
decisions.
Study
Type
Retrospective.
Population
Three
hundred
sixteen
enrolled
two
centers
divided
into
training
(
N
=
165),
internal
validation
83),
external
68)
cohorts.
Field
Strength/Sequence
1.5
T
3.0
T/dynamic
contrast‐enhanced
(DCE)
diffusion‐weighted
imaging
(DWI).
Assessment
extracted
selected
based
on
magnetic
resonance
(MRI)
sequences
create
multiparametric
MRI
combined
signature
(RS‐DCE
plus
DWI).
The
clinical
model
was
built
MRI‐axillary
lymph
nodes
(MRI
ALN),
MRI‐reported
edema
(MPE),
apparent
diffusion
coefficient
(ADC).
constructed
RS‐DCE
DWI,
ALN,
MPE,
ADC.
Statistical
Tests
Intra‐
interclass
correlation
analysis,
Mann–Whitney
U
test,
least
absolute
shrinkage
selection
operator
regression
used
feature
selection.
Receiver
operating
characteristic
decision
curve
analyses
compare
performance
model,
nomogram.
Results
A
total
10
found
be
associated
3
7
areas.
showed
good
(AUCs,
vs.
0.884
0.695
0.870),
0.813
0.794),
0.862
0.601
0.849)
Data
Conclusion
preoperative
might
effectively
assess
LVI.
Level
Evidence
Technical
Efficacy
Stage
2
BMC Surgery,
Journal Year:
2020,
Volume and Issue:
20(1)
Published: Dec. 1, 2020
Abstract
Background
Lymph
node
metastasis
(LNM)
is
an
important
factor
for
thyroid
cancer
patients’
treatment
and
prognosis.
The
aim
of
this
study
was
to
explore
the
clinical
value
ultrasound
features
radiomics
analysis
in
predicting
LNM
patients
before
surgery.
Methods
characteristics
images
150
nodules
were
retrospectively
analysed.
All
confirmed
as
cancer.
Among
assessed
patients,
only
one
hundred
twenty-six
underwent
lymph
dissection.
examination
In
radiomic
analysis,
area
interest
identified
from
selected
by
using
ITK-SNAP
software.
extracted
Ultrosomics
Then,
data
classified
into
a
training
set
validation
set.
Hypothetical
tests
bagging
used
build
model.
diagnostic
performance
different
assessed,
conducted,
receiver
operating
characteristic
(ROC)
curve
performed
accuracy.
Results
Regarding
prediction
LNM,
ROC
curves
showed
that
under
(AUC)
values
irregular
shape
microcalcification
0.591
(P
=
0.059)
0.629
0.007),
respectively.
set,
AUC
0.759,
with
sensitivity
0.90
specificity
0.860.
verification
0.803,
0.727
0.800.
Conclusions
Microcalcification
are
predictors
carcinoma
patients.
addition,
has
promising
screening
meaningful
LNM.
Therefore,
based
on
useful
making
appropriate
decisions
regarding
surgery
interventions
Technology in Cancer Research & Treatment,
Journal Year:
2020,
Volume and Issue:
19
Published: Jan. 1, 2020
Breast
cancer
has
been
a
worldwide
burden
of
women’s
health.
Although
concerns
have
raised
for
early
diagnosis
and
timely
treatment,
the
efforts
are
still
needed
precision
medicine
individualized
treatment.
Radiomics
is
new
technology
with
immense
potential
to
obtain
mineable
data
provide
rich
information
about
prognosis
breast
cancer.
In
our
study,
we
introduced
workflow
application
radiomics
as
well
its
outlook
challenges
based
on
published
studies.
ability
differentiate
between
malignant
benign
lesions,
predict
axillary
lymph
node
status,
molecular
subtypes
cancer,
tumor
response
chemotherapy,
survival
outcomes.
Our
study
aimed
help
clinicians
radiologists
know
basic
encourage
cooperation
scientists
mine
better
in
clinical
practice.
