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A new risk stratification algorithm for the management of patients with adrenal incidentalomas.
[adrenal incidentaloma]
Although
adrenal
incidentalomas
(
AI
)
are
detected
in
≤
5
%
of
patients
undergoing
chest
and
abdominal
computed
tomography
(
CT
)
,
their
management
is
challenging
.
The
current
guidelines
include
recommendations
from
the
National
Institutes
of
Health
,
the
American
Association
of
Endocrine
Surgeons
(
AAES
)
,
and
the
American
Association
for
Cancer
Education
(
AACE
)
.
The
aim
of
this
study
was
to
develop
a
new
risk
stratification
model
and
compare
its
performance
against
the
existing
guidelines
for
managing
AI
.
A
risk
stratification
model
was
designed
by
assigning
points
for
adrenal
size
(
1
,
2
,
or
3
points
for
tumors
<
4
,
4
-
6
,
or
>
6
Â
cm
,
respectively
)
and
Hounsfield
unit
(
HU
)
density
on
noncontrast
CT
(
1
,
2
,
or
3
points
for
HU
<
10
,
10
-
20
,
or
>
20
,
respectively
)
.
This
model
was
applied
retrospectively
to
157
patients
with
AI
managed
in
an
endocrine
surgery
clinic
to
assign
a
score
to
each
tumor
.
The
utility
of
this
model
versus
the
AAES
/
AACE
guidelines
was
assessed
.
Of
the
157
patients
,
54
(
34
%
)
,
had
tumors
<
4
Â
cm
with
HU
<
10
(
a
score
of
2
)
.
One
third
of
these
were
hormonally
active
on
biochemical
workup
and
underwent
adrenalectomy
.
The
remaining
two
thirds
were
nonsecretory
lesions
and
have
been
followed
conservatively
with
annual
testing
.
In
103
patients
(
66
%
)
,
the
adrenal
mass
was
>
4
Â
cm
and
/
or
had
indeterminate
features
on
noncontrast
CT
(
HU
>
10
,
irregular
borders
,
heterogeneity
)
,
and
adrenalectomy
was
performed
after
hormonal
evaluation
was
completed
(
10
were
hormonally
active
on
biochemical
testing
)
.
Seven
of
these
patients
(
7
%
)
had
adrenocortical
cancer
on
final
pathology
with
tumor
size
<
4
Â
cm
in
0
,
4
-
6
Â
cm
in
1
,
and
>
6
Â
cm
in
5
patients
.
Of
the
hormonally
inactive
patients
,
32
%
had
a
score
of
3
,
38
%
4
,
and
30
%
5
or
6
.
The
incidence
of
adrenocortical
cancer
in
these
subgroups
was
0
,
0
,
and
25
%
,
respectively
.
This
study
shows
that
an
algorithm
that
utilizes
the
hormonal
activity
at
the
first
decision
step
followed
by
a
consolidated
risk
stratification
,
based
on
tumor
size
and
HU
density
,
has
a
potential
to
spare
a
substantial
number
of
patients
from
unnecessary
"
diagnostic
"
surgery
for
AI
.
Diseases
Validation
Diseases presenting
"cancer"
symptom
achondroplasia
acute rheumatic fever
adrenal incidentaloma
alpha-thalassemia
benign recurrent intrahepatic cholestasis
cadasil
canavan disease
carcinoma of the gallbladder
cholangiocarcinoma
coats disease
congenital adrenal hyperplasia
congenital diaphragmatic hernia
cowden syndrome
cushing syndrome
cutaneous mastocytosis
dedifferentiated liposarcoma
dystrophic epidermolysis bullosa
epidermolysis bullosa simplex
erdheim-chester disease
erythropoietic protoporphyria
esophageal adenocarcinoma
esophageal carcinoma
esophageal squamous cell carcinoma
familial hypocalciuric hypercalcemia
familial mediterranean fever
gm1 gangliosidosis
heparin-induced thrombocytopenia
hereditary cerebral hemorrhage with amyloidosis
hirschsprung disease
hodgkin lymphoma, classical
inclusion body myositis
junctional epidermolysis bullosa
kabuki syndrome
kallmann syndrome
kindler syndrome
lamellar ichthyosis
liposarcoma
locked-in syndrome
lymphangioleiomyomatosis
monosomy 21
neuralgic amyotrophy
oculocutaneous albinism
oligodontia
oral submucous fibrosis
papillon-lefèvre syndrome
pendred syndrome
pleomorphic liposarcoma
primary effusion lymphoma
proteus syndrome
pyomyositis
pyruvate dehydrogenase deficiency
severe combined immunodeficiency
sneddon syndrome
systemic capillary leak syndrome
triple a syndrome
von hippel-lindau disease
waldenström macroglobulinemia
well-differentiated liposarcoma
werner syndrome
wiskott-aldrich syndrome
wolf-hirschhorn syndrome
x-linked adrenoleukodystrophy
This symptom has already been validated
