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Differentiation of lipoma from liposarcoma on MRI using texture and shape analysis.
[liposarcoma]
To
determine
if
differentiation
of
lipoma
from
liposarcoma
on
magnetic
resonance
imaging
can
be
improved
using
computer-assisted
diagnosis
(
CAD
)
.
Forty
-
four
histologically
proven
lipomatous
tumors
(
24
lipomas
and
20
liposarcomas
)
were
studied
retrospectively
.
Studies
were
performed
at
1
.
5
T
and
included
T
1
-
weighted
,
T
2
-
weighted
,
T
2
-
fat-suppressed
,
short
inversion
time
inversion
recovery
,
and
contrast-enhanced
sequences
.
Two
experienced
musculoskeletal
radiologists
blindly
and
independently
noted
their
degree
of
confidence
in
malignancy
using
all
available
images
/
sequences
for
each
patient
.
For
CAD
,
tumors
were
segmented
in
three
dimensions
using
T
1
-
weighted
images
.
Gray
-level
co
-occurrence
and
run-length
matrix
textural
features
,
as
well
as
morphological
features
,
were
extracted
from
each
tumor
volume
.
Combinations
of
shape
and
textural
features
were
used
to
train
multiple
,
linear
discriminant
analysis
classifiers
.
We
assessed
sensitivity
,
specificity
,
and
accuracy
of
each
classifier
for
delineating
lipoma
from
liposarcoma
using
10
-
fold
cross-validation
.
Diagnostic
accuracy
of
the
two
radiologists
was
determined
using
contingency
tables
.
Interreader
agreement
was
evaluated
by
Cohen
kappa
.
Using
optimum-threshold
criteria
,
CAD
produced
superior
values
(
sensitivity
,
specificity
,
and
accuracy
are
85
%
,
96
%
,
and
91
%
,
respectively
)
compared
to
radiologist
A
(
75
%
,
83
%
,
and
80
%
)
and
radiologist
B
(
80
%
,
75
%
,
and
77
%
)
.
Interreader
agreement
between
radiologists
was
substantial
(
kappa
[
95
%
confidence
interval
]
=
0
.
69
[
0
.
48
-
0
.
90
]
)
.
CAD
may
help
radiologists
distinguish
lipoma
from
liposarcoma
.
Diseases
Validation
Diseases presenting
"independently noted their degree of confidence in malignancy using all available images"
symptom
liposarcoma
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