Cancers,
Journal Year:
2024,
Volume and Issue:
16(19), P. 3351 - 3351
Published: Sept. 30, 2024
Purpose:
To
develop
and
validate
an
MRI-based
radiomic
model
for
predicting
overall
survival
(OS)
in
patients
diagnosed
with
glioblastoma
multiforme
(GBM),
utilizing
a
retrospective
dataset
from
multiple
institutions.
Materials
Methods:
Pre-treatment
MRI
images
of
289
GBM
were
collected.
From
each
patient’s
tumor
volume,
660
features
(RFs)
extracted
subjected
to
robustness
analysis.
The
initial
prognostic
minimum
RFs
was
subsequently
enhanced
by
including
clinical
variables.
final
clinical–radiomic
derived
through
repeated
three-fold
cross-validation
on
the
training
dataset.
Performance
evaluation
included
assessment
concordance
index
(C-Index),
integrated
area
under
curve
(iAUC)
alongside
patient
stratification
into
low
high-risk
groups
(OS).
Results:
model,
which
has
highest
level
interpretability,
utilized
primary
gross
volume
(GTV)
one
modality
(T2-FLAIR)
as
predictor
age
variable
two
independent,
robust
RFs,
achieving
moderately
good
discriminatory
performance
(C-Index
[95%
confidence
interval]:
0.69
[0.62–0.75])
significant
(p
=
7
×
10−5)
validation
cohort.
Furthermore,
trained
exhibited
iAUC
at
11
months
(0.81)
literature.
Conclusion:
We
identified
validated
based
OS
using
multicenter
Future
work
will
focus
use
deep
learning-based
features,
recently
standardized
convolutional
filters
tasks.
Journal of Cancer,
Journal Year:
2024,
Volume and Issue:
15(12), P. 3943 - 3957
Published: Jan. 1, 2024
Triple-negative
breast
cancer
(TNBC)
poses
significant
diagnostic
challenges
due
to
its
aggressive
nature.
This
research
develops
an
innovative
deep
learning
(DL)
model
based
on
the
latest
multi-omics
data
enhance
accuracy
of
TNBC
subtype
and
prognosis
prediction.
The
study
focuses
addressing
constraints
prior
studies
by
showcasing
a
with
substantial
advancements
in
integration,
statistical
performance,
algorithmic
optimization.
Cancers,
Journal Year:
2024,
Volume and Issue:
16(19), P. 3351 - 3351
Published: Sept. 30, 2024
Purpose:
To
develop
and
validate
an
MRI-based
radiomic
model
for
predicting
overall
survival
(OS)
in
patients
diagnosed
with
glioblastoma
multiforme
(GBM),
utilizing
a
retrospective
dataset
from
multiple
institutions.
Materials
Methods:
Pre-treatment
MRI
images
of
289
GBM
were
collected.
From
each
patient’s
tumor
volume,
660
features
(RFs)
extracted
subjected
to
robustness
analysis.
The
initial
prognostic
minimum
RFs
was
subsequently
enhanced
by
including
clinical
variables.
final
clinical–radiomic
derived
through
repeated
three-fold
cross-validation
on
the
training
dataset.
Performance
evaluation
included
assessment
concordance
index
(C-Index),
integrated
area
under
curve
(iAUC)
alongside
patient
stratification
into
low
high-risk
groups
(OS).
Results:
model,
which
has
highest
level
interpretability,
utilized
primary
gross
volume
(GTV)
one
modality
(T2-FLAIR)
as
predictor
age
variable
two
independent,
robust
RFs,
achieving
moderately
good
discriminatory
performance
(C-Index
[95%
confidence
interval]:
0.69
[0.62–0.75])
significant
(p
=
7
×
10−5)
validation
cohort.
Furthermore,
trained
exhibited
iAUC
at
11
months
(0.81)
literature.
Conclusion:
We
identified
validated
based
OS
using
multicenter
Future
work
will
focus
use
deep
learning-based
features,
recently
standardized
convolutional
filters
tasks.
