Intratumoral microbiota-aided fusion radiomics model for predicting tumor response to neoadjuvant chemoimmunotherapy in triple-negative breast cancer
Journal of Translational Medicine,
Год журнала:
2025,
Номер
23(1)
Опубликована: Март 20, 2025
Neoadjuvant
chemoimmunotherapy
(NACI)
has
emerged
as
the
standard
treatment
for
early-stage
triple-negative
breast
cancer
(TNBC).
However,
reliable
biomarkers
identifying
patients
who
are
likely
to
benefit
from
NACI
lacking.
This
study
aims
develop
an
intratumoral
microbiota-aided
radiomics
model
predicting
pathological
complete
response
(pCR)
in
with
TNBC.
Intratumoral
microbiota
characterized
by
16S
rDNA
sequencing
and
quantified
through
experimental
assays.
Single-cell
RNA
is
performed
analyze
tumor
microenvironment
of
tumors
various
responses
NACI.
Radiomics
features
extracted
regions
on
longitudinal
magnetic
resonance
images
(MRIs)
scanned
before
after
training
set.
On
basis
(pCR
or
non-pCR)
scoring,
we
select
key
construct
a
fusion
integrating
multi-timepoint
(pre-NACI
post-NACI)
MRI
predict
efficacy
immunotherapy,
followed
independent
external
validation.
A
total
124
enrolled,
88
set
36
validation
Tumors
achieves
pCR
present
significantly
greater
load
than
achieve
non-pCR
(p
<
0.05).
Additionally,
group
exhibit
infiltration
tumor-associated
SPP1+
macrophages,
which
negatively
correlated
load.
17
use
them
model.
The
highest
AUC
0.945
set,
outperforming
pre-NACI
(AUC
=
0.875)
post-NACI
0.917)
models.
In
this
maintains
superior
0.873,
surpassing
those
0.769)
0.802)
Clinically,
distinguishes
do
not
accuracy
77.8%.
Decision
curve
analysis
demonstrates
net
clinical
across
varying
risk
thresholds.
Our
could
serve
powerful
noninvasive
tool
TNBC
Язык: Английский
Synthetic imaging for research and education in nuclear medicine: Who’s afraid of the black box?
European Journal of Nuclear Medicine and Molecular Imaging,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 22, 2025
Язык: Английский
Dual-region MRI radiomic analysis indicates increased risk in high-risk breast lesions: bridging intratumoral and peritumoral radiomics for precision decision-making
BMC Cancer,
Год журнала:
2025,
Номер
25(1)
Опубликована: Май 6, 2025
To
evaluate
the
clinical
utility
of
dynamic
contrast-enhanced
magnetic
resonance
imaging
(DCE-MRI)-derived
clinicoradiological
characteristics
and
intratumoral/peritumoral
radiomic
features
in
predicting
pathological
upgrades
(malignant
transformation)
high-risk
breast
lesions.
Retrospectively
collected
data
174
patients
with
lesions
who
underwent
preoperative
MRI
examinations
were
confirmed
by
biopsy
pathology
Shenzhen
People's
Hospital
between
January
1,
2019
2024.
The
dataset
was
randomly
divided
into
a
training
set
(n
=
121)
test
53)
at
ratio
7:3.
Initially,
during
second
stage
DCE-MRI,
region
interest
(ROI)
delineated
along
maximum
cross-section
lesion,
then
automatically
expanded
outward
3
mm,
5
7
mm
as
peritumoral
ROIs.
intratumoral,
each
peritumoral,
combined
intratumoral
models
established
respectively.
Independent
risk
factors
predictive
malignant
identified
through
univariate
multivariable
logistic
regression
analyses,
which
subsequently
incorporated
characteristics.
Finally,
model
integrating
features.
performance
analyzed
using
receiver
operating
characteristic
(ROC)
curves,
area
under
curve
(AUC)
calculated.
radiomics
achieved
highest
diagnostic
among
all
models,
AUC
values
0.704
0.654
for
sets,
In
set,
showed
(AUC
0.883),
superior
to
that
0.745,
P
0.003),
0.791,
0.027),
0.704,
0.001),
0.830,
0.004).
also
0.851).
constructed
had
best
performance,
sensitivity,
specificity,
accuracy
79.4%,
82.7%,
81.8%
72.7%,
85.7%,
83.0%
model,
integrates
data,
exhibited
strong
clinically
applicable
nomogram
stratify
individualized
upgrade
risk,
assisting
clinicians
making
more
precise
decisions.
Язык: Английский
18F-FDG PET/CT Radiomics for Predicting Therapy Response in Primary Mediastinal B-Cell Lymphoma: A Bi-Centric Pilot Study
Cancers,
Год журнала:
2025,
Номер
17(11), С. 1827 - 1827
Опубликована: Май 30, 2025
Purpose:
This
bi-centric
pilot
study
investigates
the
predictive
value
of
pre-treatment
[18F]FDG
PET/CT
radiomics
for
assessing
therapy
response
in
primary
mediastinal
B-cell
lymphoma
(PMBCL).
Methods:
All
PMBCL
patients
underwent
with
between
January
2011
and
2022
at
Policlinico
Tor
Vergata
University
Hospital
Rome
(70%
training
30%
internal
validation
cohort)
Sant’Anna
Ferrara
(external
cohort).
The
Deauville
score
(DS)
was
used
as
a
predictor
(DS1-DS3
vs.
DS4/DS5).
A
total
121
quantitative
features
(RFts)
were
extracted
from
manually
segmented
volumes
interest
(VOIs)
PET
CT
images,
according
to
IBSI.
ComBat
harmonization
applied
correct
center
variability
features,
followed
by
class
balancing
SMOTE.
Two
machine
learning
(ML)
prediction
models,
model
model,
independently
developed
using
robust
RFts.
For
each
ML
two
different
algorithms
trained
(i.e.,
Random
Forest,
RF,
Support
Vector
Machine,
SVM)
10-fold
cross
validation,
tested
on
internal/external
set.
Receiver
operating
characteristic
(ROC)
curves,
area
under
curve
(AUC),
classification
accuracy
(CA),
precision
(Prec),
sensitivity
(Sen),
specificity
(Spec),
true
positive
(TP)
scores,
negative
(TN)
scores
computed.
Results:
entire
dataset
composed
29
samples
cohort
(23
D1–D3
6
D4/D5)
9
(4
5
D4/D5).
27
RFts
identified
imaging
modality.
Both
models
effectively
predicted
score.
performance
metrics
best
classifier
(SVM)
external
AUC
=
0.75/0.80,
CA
0.85/0.77,
Prec
0.97/0.67,
Sen
0.60/0.80,
Spec
0.98/0.75,
TP
75.0%/66.7%,
TN
77.8%/85.7%,
respectively.
Conclusions:
radiomic
PMBLC
could
predict
Язык: Английский
Multi-modal radiomics model based on four imaging modalities for predicting pathological complete response to neoadjuvant treatment in breast cancer
BMC Cancer,
Год журнала:
2025,
Номер
25(1)
Опубликована: Июнь 2, 2025
The
radiomics
model
based
on
single
imaging
modality
has
been
demonstrated
as
a
promising
approach
for
predicting
the
response
to
neoadjuvant
treatment
(NAT)
in
breast
cancer.
However,
whether
integrating
multiple
modalities
improve
performance
of
is
undetermined.
This
study
aims
develop
multi-modal
four
modalities,
including
ultrasound
(US),
mammography
(MM),
computed
tomography
(CT),
and
magnetic
resonance
(MRI),
pathological
complete
(pCR)
cancer
after
NAT.
Patients
who
underwent
surgery
NAT
from
January
2019
July
2023
were
retrospectively
studied.
Univariate
multivariate
analyses
performed
identify
independent
clinical
risk
factors
pCR.
radiomic
features
extracted
volume
interest
modalities.
least
absolute
shrinkage
selection
operator
was
used
developing
signatures.
developed
by
combining
combined
A
nomogram
visualize
model.
Model
internally
validated
using
five-fold
cross-validation.
In
total,
89
patients
included,
with
pCR
rate
31.5%
(28/89).
Multivariate
identified
PR
status
(OR
=
4.450,
95%
confidence
interval
[CI],
1.228-18.063,
P
0.028),
HER2
9.95,
CI,
1.525-201.894,
0.044)
T
stage
0.253,
0.076-0.753,
0.016)
AUCs
brier
scores
signatures
US,
MM,
CT,
MRI
0.702
(95%
CI:
0.583-0.821),
0.762
0.660-0.865),
0.814
0.725-0.903),
0.787
0.685-0.889)
0.198,
0.177,
0.165,
0.170
respectively.
superior
all
an
AUC
0.904
0.838-0.970)
score
0.111.
After
adding
factors,
further
improved,
achieving
0.943
0.893-0.992)
0.082.
showed
potential
value.
could
accurately
predict
NAT,
which
Incorporating
may
muti-modal
model,
provide
valuable
information
guiding
decisions.
Язык: Английский