Medical Physics,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Oct. 1, 2024
Abstract
Background
Clear
cell
renal
carcinoma
(ccRCC)
is
one
of
the
most
common
histological
subtypes
tumors.
Purpose
To
identify
high‐risk
subregions
associated
with
synchronous
distant
metastasis.
Methods
This
study
enrolled
a
total
277
patients
ccRCC.
Voxel
intensity
and
local
entropy
values
were
compiled
within
region
interest
for
all
patients.
Unsupervised
k
‐means
clustering
yielded
three
per
tumor.
Radiomic
features
extracted,
random
forest‐based
feature
selection
was
conducted.
The
selected
used
in
multi‐instance
support
vector
machine
(mi‐SVM)
model
training,
predictions
made
on
validation
cohort.
Model
performance
evaluated
using
five‐fold
cross‐validation.
subregion
highest
score
metastasis
identified
across
cohorts.
Results
mi‐SVM
an
average
area
under
curve
(AUC)
0.812
training
cohort
0.805
In
entire
metastasis,
2,
characterized
by
tumor
periphery
intratumoral
transitional
components,
accounted
proportion
(48.57%,
30.6/63)
among
subregions.
It
represents
clear
carcinoma.
Conclusion
peripheral
transition
zones
are
Acta Radiologica,
Journal Year:
2022,
Volume and Issue:
64(7), P. 2221 - 2228
Published: Dec. 6, 2022
The
preoperative
prediction
of
lymph
node
metastasis
(LNM)
in
pancreatic
ductal
adenocarcinoma
(PDAC)
is
essential
prognosis
and
treatment
strategy
formulation.To
compare
the
performance
computed
tomography
(CT)
magnetic
resonance
imaging
(MRI)
radiomics
models
for
LNM
PDAC.In
total,
160
consecutive
patients
with
PDAC
were
retrospectively
included,
who
divided
into
training
validation
sets
(ratio
8:2).
Two
radiologists
evaluated
basing
on
morphological
abnormalities.
Radiomics
features
extracted
from
T2-weighted
imaging,
T1-weighted
multiphase
contrast
enhanced
MRI
CT,
respectively.
Overall,
1184
each
volume
interest
drawn.
Only
an
intraclass
correlation
coefficient
≥0.75
included.
Three
sequential
feature
selection
steps-variance
threshold,
variance
thresholding
least
absolute
shrinkage
operator-were
repeated
20
times
fivefold
cross-validation
set.
based
CT
multiparametric
built
five
most
frequent
features.
Model
was
using
area
under
curve
(AUC)
values.Multiparametric
model
achieved
improved
AUCs
(0.791
0.786
sets,
respectively)
than
that
(0.672
0.655
radiologists'
assessment
(0.600-0.613
0.560-0.587
respectively).Multiparametric
may
serve
as
a
potential
tool
preoperatively
evaluating
had
superior
predictive
to
CT-based
assessment.
PLoS ONE,
Journal Year:
2023,
Volume and Issue:
18(9), P. e0287031 - e0287031
Published: Sept. 26, 2023
Background
Dose
adjuvant
chemotherapy
(AC)
should
be
offered
in
nasopharyngeal
carcinoma
(NPC)
patients?
Different
guidelines
provided
the
different
recommendations.
Methods
In
this
retrospective
study,
a
total
of
140
patients
were
enrolled
and
followed
for
3
years,
with
24
clinical
features
being
collected.
The
imaging
on
enhanced-MRI
sequence
extracted
by
using
PyRadiomics
platform.
pearson
correlation
coefficient
random
forest
was
used
to
filter
associated
recurrence
or
metastasis.
A
clinical-radiomics
model
(CRM)
constructed
Cox
multivariable
analysis
training
cohort,
validated
validation
cohort.
All
divided
into
high-
low-risk
groups
through
median
Rad-score
model.
Kaplan-Meier
survival
curves
compare
3-year
metastasis
free
rate
(RMFR)
without
AC
low-groups.
Results
total,
960
extracted.
CRM
from
nine
(seven
two
factors).
area
under
curve
(AUC)
RMFR
0.872
(P
<0.001),
sensitivity
specificity
0.935
0.672,
respectively;
AUC
0.864
1.00
0.75,
respectively.
showed
that
cancer
specific
(CSS)
high-risk
group
significantly
lower
than
those
<0.001).
group,
who
received
had
greater
did
not
receive
(78.6%
vs.
48.1%)
(p
=
0.03).
Conclusion
Considering
increasing
RMFR,
prediction
NPC
based
factors
seven
suggested
needs
added
group.
Medical Physics,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Oct. 1, 2024
Abstract
Background
Clear
cell
renal
carcinoma
(ccRCC)
is
one
of
the
most
common
histological
subtypes
tumors.
Purpose
To
identify
high‐risk
subregions
associated
with
synchronous
distant
metastasis.
Methods
This
study
enrolled
a
total
277
patients
ccRCC.
Voxel
intensity
and
local
entropy
values
were
compiled
within
region
interest
for
all
patients.
Unsupervised
k
‐means
clustering
yielded
three
per
tumor.
Radiomic
features
extracted,
random
forest‐based
feature
selection
was
conducted.
The
selected
used
in
multi‐instance
support
vector
machine
(mi‐SVM)
model
training,
predictions
made
on
validation
cohort.
Model
performance
evaluated
using
five‐fold
cross‐validation.
subregion
highest
score
metastasis
identified
across
cohorts.
Results
mi‐SVM
an
average
area
under
curve
(AUC)
0.812
training
cohort
0.805
In
entire
metastasis,
2,
characterized
by
tumor
periphery
intratumoral
transitional
components,
accounted
proportion
(48.57%,
30.6/63)
among
subregions.
It
represents
clear
carcinoma.
Conclusion
peripheral
transition
zones
are
