HepatoBiliary Surgery and Nutrition,
Journal Year:
2021,
Volume and Issue:
11(5), P. 662 - 674
Published: June 16, 2021
For
patients
with
a
large
but
resectable
solitary
hepatocellular
carcinoma
(HCC)
of
>5
cm
in
diameter,
it
is
often
difficult
to
achieve
sufficient
resection
margin.
There
still
no
study
on
whether
two-stage
hepatectomy
increase
narrow
margin
would
be
beneficial.From
August
2014
February
2017,
HCC
and
preoperative
estimated
<1.0
were
retrospectively
studied.
They
divided
into
one-
groups.
A
retrospective
analysis
was
performed,
followed
by
propensity
score
matching
(PSM)
analysis.
Disease
recurrence,
survival,
intraoperative
postoperative
data
compared.Before
PSM,
the
1-,
2-,
3-and
4-year
recurrence-free
survival
rates
for
groups
44.3%,
31.7%,
24.3%,
19.2%
versus
60.6%,
45.4%,
43.5%,
32.3%,
respectively
(P=0.007).
The
corresponding
OS
61.0%,
45.2%,
43.8%,
38.4%
69.6%,
62.5%,
60.7%,
57.3%,
(P=0.029).
After
44.0%,
31.5%,
27.3%,
21.0%
(P=0.013).
41.1%,
37.5%
(P=0.038).
Differences
margins
between
before
[0.3
(0-0.5)
1.2
(0.8-2.2)
cm]
after
[0.2
PSM
also
significant.Two-stage
allowed
wider
cm,
resulted
significantly
better
long-term
outcomes
partial
hepatectomy.
IEEE Access,
Journal Year:
2020,
Volume and Issue:
9, P. 3660 - 3678
Published: Dec. 30, 2020
Smart
health
care
is
an
important
aspect
of
connected
living.
Health
one
the
basic
pillars
human
need,
and
smart
projected
to
produce
several
billion
dollars
in
revenue
near
future.
There
are
components
care,
including
Internet
Things
(IoT),
Medical
(IoMT),
medical
sensors,
artificial
intelligence
(AI),
edge
computing,
cloud
next-generation
wireless
communication
technology.
Many
papers
literature
deal
with
or
general.
Here,
we
present
a
comprehensive
survey
IoT-
IoMT-based
edge-intelligent
mainly
focusing
on
journal
articles
published
between
2014
2020.
We
this
by
answering
research
areas
IoT
IoMT,
AI,
security,
signals
fusion.
also
address
current
challenges
offer
some
future
directions.
IEEE Access,
Journal Year:
2020,
Volume and Issue:
9, P. 11358 - 11371
Published: Dec. 30, 2020
The
track
of
medical
imaging
has
witnessed
several
advancements
in
the
last
years.
Several
modalities
have
appeared
decades
including
X-ray,
Computed
Tomography
(CT),
Magnetic
Resonance
(MR),
Positron
Emission
(PET),
Single-Photon
(SPECT)
and
ultrasound
imaging.
Generally,
images
are
used
for
diagnosis
purpose.
Each
type
acquired
some
merits
limitations.
To
maximize
utilization
purpose
diagnosis,
fusion
trend
as
a
hot
research
field.
Different
fused
to
obtain
new
with
complementary
information.
This
paper
presents
survey
study
their
characteristics.
In
addition,
different
image
approaches
appropriate
quality
metrics
presented.
main
aim
this
comprehensive
analysis
is
contribute
advancement
that
can
help
better
diseases.
Heliyon,
Journal Year:
2024,
Volume and Issue:
10(17), P. e36743 - e36743
Published: Aug. 23, 2024
This
review
article
offers
a
comprehensive
analysis
of
current
developments
in
the
application
machine
learning
for
cancer
diagnostic
systems.
The
effectiveness
approaches
has
become
evident
improving
accuracy
and
speed
detection,
addressing
complexities
large
intricate
medical
datasets.
aims
to
evaluate
modern
techniques
employed
diagnostics,
covering
various
algorithms,
including
supervised
unsupervised
learning,
as
well
deep
federated
methodologies.
Data
acquisition
preprocessing
methods
different
types
data,
such
imaging,
genomics,
clinical
records,
are
discussed.
paper
also
examines
feature
extraction
selection
specific
diagnosis.
Model
training,
evaluation
metrics,
performance
comparison
explored.
Additionally,
provides
insights
into
applications
discusses
challenges
related
dataset
limitations,
model
interpretability,
multi-omics
integration,
ethical
considerations.
emerging
field
explainable
artificial
intelligence
(XAI)
diagnosis
is
highlighted,
emphasizing
XAI
proposed
improve
diagnostics.
These
include
interactive
visualization
decisions
importance
tailored
enhanced
interpretation,
aiming
enhance
both
transparency
decision-making.
concludes
by
outlining
future
directions,
personalized
medicine,
advancements,
guide
researchers,
clinicians,
policymakers
development
efficient
interpretable
learning-based
Applied Artificial Intelligence,
Journal Year:
2022,
Volume and Issue:
36(1)
Published: April 4, 2022
The
liver
tumor
is
one
of
the
most
foremost
critical
causes
death
in
world.
Nowadays,
Medical
Imaging
(MI)
prominent
Computer
Vision
fields
(CV),
which
helps
physicians
and
radiologists
to
detect
diagnose
tumors
at
an
early
stage.
Radiologists
use
manual
or
semi-automated
systems
read
hundreds
images,
such
as
Computed
Tomography
(CT)
for
diagnosis.
Therefore,
there
a
need
fully-automated
method
using
popular
widely
used
imaging
modality,
CT
images.
proposed
work
focuses
on
Machine
Learning
(ML)
methods:
Random
Forest
(RF),
J48,
Logistic
Model
Tree
(LMT),
(RT)
with
multiple
automated
Region
Interest
(ROI)
multiclass
classification.
dataset
comprises
four
classes:
hemangioma,
cyst,
hepatocellular
carcinoma,
metastasis.
Converted
images
into
gray-scale,
contrast
was
improved
by
applying
histogram
equalization.
noise
reduced
Gabor
filter,
image
quality
sharpening
algorithm.
Furthermore,
55
features
were
acquired
each
ROI
different
pixel
dimensions
texture,
binary,
rotational,
scalability,
translational
(RST)
techniques.
correlation-based
feature
selection
(CFS)
technique
deployed
obtain
20
optimized
from
these
results
showed
that
RF
RT
performed
better
than
J48
LMT,
accuracy
97.48%
97.08%,
respectively.
novel
framework
will
help
tumors.
