Cancers,
Journal Year:
2023,
Volume and Issue:
15(22), P. 5476 - 5476
Published: Nov. 20, 2023
Objectives:
We
aimed
to
develop
a
novel
non-linear
statistical
model
integrating
primary
tumor
features
on
baseline
[18F]-fluorodeoxyglucose
positron
emission
tomography/computed
tomography
(FDG-PET/CT),
molecular
subtype,
and
clinical
data
for
treatment
benefit
prediction
in
women
with
newly
diagnosed
breast
cancer
using
innovative
techniques,
as
opposed
conventional
methodological
approaches.
Methods:
In
this
single-center
retrospective
study,
we
conducted
comprehensive
assessment
of
who
had
undergone
FDG-PET/CT
scan
staging
prior
treatment.
Primary
(PT)
volume,
maximum
mean
standardized
uptake
value
(SUVmax
SUVmean),
metabolic
volume
(MTV),
total
lesion
glycolysis
(TLG)
were
measured
PET/CT.
Clinical
including
(TNM)
but
also
PT
anatomical
site,
histology,
receptor
status,
proliferation
index,
subtype
obtained
from
the
medical
records.
Overall
survival
(OS),
progression-free
(PFS),
(CB)
assessed
endpoints.
A
logistic
generalized
additive
was
chosen
approach
assess
impact
all
listed
variables
CB.
Results:
70
(mean
age
63.3
±
15.4
years)
included.
The
most
common
location
upper
outer
quadrant
(40.0%)
left
(52.9%).
An
invasive
ductal
adenocarcinoma
(88.6%)
high
index
ki-67
expression
35.1
24.5%)
B
(51.4%)
by
far
detected
tumor.
Most
PTs
displayed
hybrid
imaging
greater
(12.8
30.4
cm3)
hypermetabolism
SD
SUVmax,
SUVmean,
MTV,
TLG,
respectively:
8.1
7.2,
4.9
4.4,
12.7
30.4,
47.4
80.2).
Higher
(p
<
0.01),
SUVmax
=
0.04),
SUVmean
0.03),
MTV
(<0.01)
significantly
compromised
considerable
majority
patients
survived
throughout
period
(92.8%),
while
five
died
(7.2%).
fact,
OS
31.7
14.2
months
PFS
30.2
14.1
months.
multivariate
CB
excellent
accuracy
could
be
developed
age,
body
mass
(BMI),
T,
M,
predictive
parameters.
TLG
demonstrated
significant
influence
lower
ranges;
however,
beyond
specific
cutoff
(respectively,
29.52
cm3
161.95
TLG),
their
only
reached
negligible
levels.
Ultimately,
absence
distant
metastasis
M
strong
positive
ahead
size
T
(standardized
average
estimate
0.88
vs.
0.4).
Conclusions:
Our
results
emphasized
pivotal
role
played
forecasting
outcomes
cancer.
Nevertheless,
careful
consideration
is
required
when
selecting
approach,
our
techniques
unveiled
influences
biomarkers
benefit,
highlighting
importance
early
diagnosis.
Bioengineering,
Journal Year:
2023,
Volume and Issue:
10(12), P. 1435 - 1435
Published: Dec. 18, 2023
The
integration
of
artificial
intelligence
(AI)
into
medical
imaging
has
guided
in
an
era
transformation
healthcare.
This
literature
review
explores
the
latest
innovations
and
applications
AI
field,
highlighting
its
profound
impact
on
diagnosis
patient
care.
innovation
segment
cutting-edge
developments
AI,
such
as
deep
learning
algorithms,
convolutional
neural
networks,
generative
adversarial
which
have
significantly
improved
accuracy
efficiency
image
analysis.
These
enabled
rapid
accurate
detection
abnormalities,
from
identifying
tumors
during
radiological
examinations
to
detecting
early
signs
eye
disease
retinal
images.
article
also
highlights
various
imaging,
including
radiology,
pathology,
cardiology,
more.
AI-based
diagnostic
tools
not
only
speed
up
interpretation
complex
images
but
improve
disease,
ultimately
delivering
better
outcomes
for
patients.
Additionally,
processing
facilitates
personalized
treatment
plans,
thereby
optimizing
healthcare
delivery.
paradigm
shift
that
brought
role
revolutionizing
By
combining
techniques
their
practical
applications,
it
is
clear
will
continue
shaping
future
positive
ways.
Current Oncology,
Journal Year:
2023,
Volume and Issue:
30(3), P. 2673 - 2701
Published: Feb. 22, 2023
The
application
of
artificial
intelligence
(AI)
is
accelerating
the
paradigm
shift
towards
patient-tailored
brain
tumor
management,
achieving
optimal
onco-functional
balance
for
each
individual.
AI-based
models
can
positively
impact
different
stages
diagnostic
and
therapeutic
process.
Although
histological
investigation
will
remain
difficult
to
replace,
in
near
future
radiomic
approach
allow
a
complementary,
repeatable
non-invasive
characterization
lesion,
assisting
oncologists
neurosurgeons
selecting
best
option
correct
molecular
target
chemotherapy.
AI-driven
tools
are
already
playing
an
important
role
surgical
planning,
delimiting
extent
lesion
(segmentation)
its
relationships
with
structures,
thus
allowing
precision
surgery
as
radical
reasonably
acceptable
preserve
quality
life.
Finally,
AI-assisted
prediction
complications,
recurrences
response,
suggesting
most
appropriate
follow-up.
Looking
future,
AI-powered
promise
integrate
biochemical
clinical
data
stratify
risk
direct
patients
personalized
screening
protocols.
Journal of Magnetic Resonance Imaging,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 9, 2025
Breast
cancer
continues
to
be
a
major
health
concern,
and
early
detection
is
vital
for
enhancing
survival
rates.
Magnetic
resonance
imaging
(MRI)
key
tool
due
its
substantial
sensitivity
invasive
breast
cancers.
Computer‐aided
(CADe)
systems
enhance
the
effectiveness
of
MRI
by
identifying
potential
lesions,
aiding
radiologists
in
focusing
on
areas
interest,
extracting
quantitative
features,
integrating
with
computer‐aided
diagnosis
(CADx)
pipelines.
This
review
aims
provide
comprehensive
overview
current
state
CADe
MRI,
technical
details
pipelines
segmentation
models
including
classical
intensity‐based
methods,
supervised
unsupervised
machine
learning
(ML)
approaches,
latest
deep
(DL)
architectures.
It
highlights
recent
advancements
from
traditional
algorithms
sophisticated
DL
such
as
U‐Nets,
emphasizing
implementation
multi‐parametric
acquisitions.
Despite
these
advancements,
face
challenges
like
variable
false‐positive
negative
rates,
complexity
interpreting
extensive
data,
variability
system
performance,
lack
large‐scale
studies
multicentric
models,
limiting
generalizability
suitability
clinical
implementation.
Technical
issues,
image
artefacts
need
reproducible
explainable
algorithms,
remain
significant
hurdles.
Future
directions
emphasize
developing
more
robust
generalizable
AI
improve
transparency
trust
among
clinicians,
multi‐purpose
systems,
incorporating
large
language
diagnostic
reporting
patient
management.
Additionally,
efforts
standardize
streamline
protocols
aim
increase
accessibility
reduce
costs,
optimizing
use
practice.
Level
Evidence
NA
Efficacy
Stage
2
Journal of Medicine Surgery and Public Health,
Journal Year:
2024,
Volume and Issue:
3, P. 100120 - 100120
Published: June 17, 2024
Breast
cancer's
global
impact
and
high
mortality
rates
drive
interest
in
Artificial
intelligence
(AI)
applications.
AI's
pattern
recognition
decision-making
abilities
offer
promise
detection,
diagnosis,
personalized
treatment,
risk
assessment,
prevention.
Screening
early
detection
are
improved
by
AI-enhanced
mammography.
AI
aids
radiologists
lesion
though
concerns
about
false
positives
persist.
In
addition,
revolutionizes
breast
imaging,
assisting
reading
mammograms,
biomarker
lymph
node
outcome
prediction.
Genetic
insights
into
treatment
response
advanced
AI,
particularly
through
deep
learning
algorithms.
Collaborative
approaches
benefit
from
AI-guided
radiotherapy
planning.
However,
challenges
of
include
data
privacy,
ethics,
regulatory
issues
that
must
be
navigated
to
ensure
successful
implementation
while
upholding
healthcare
trust.
Therefore,
this
commentary
provided
an
overview
implication
cancer.
Life,
Journal Year:
2024,
Volume and Issue:
14(11), P. 1451 - 1451
Published: Nov. 8, 2024
Breast
cancer
is
the
most
prevalent
worldwide,
affecting
both
low-
and
middle-income
countries,
with
a
growing
number
of
cases.
In
2024,
about
310,720
women
in
U.S.
are
projected
to
receive
an
invasive
breast
diagnosis,
alongside
56,500
cases
ductal
carcinoma
situ
(DCIS).
occurs
every
country
world
at
any
age
after
puberty
but
increasing
rates
later
life.
About
65%
Cancers,
Journal Year:
2024,
Volume and Issue:
16(5), P. 848 - 848
Published: Feb. 20, 2024
Artificial
intelligence
(AI)
is
emerging
as
a
discipline
capable
of
providing
significant
added
value
in
Medicine,
particular
radiomic,
imaging
analysis,
big
dataset
and
also
for
generating
virtual
cohort
patients.
However,
coping
with
chronic
myeloid
leukemia
(CML),
considered
an
easily
managed
malignancy
after
the
introduction
TKIs
which
strongly
improved
life
expectancy
patients,
AI
still
its
infancy.
Noteworthy,
findings
initial
trials
are
intriguing
encouraging,
both
terms
performance
adaptability
to
different
contexts
can
be
applied.
Indeed,
improvement
diagnosis
prognosis
by
leveraging
biochemical,
biomolecular,
imaging,
clinical
data
crucial
implementation
personalized
medicine
paradigm
or
streamlining
procedures
services.
In
this
review,
we
present
state
art
applications
field
CML,
describing
techniques
objectives,
general
focus
that
goes
beyond
Machine
Learning
(ML),
but
instead
embraces
wider
field.
The
scooping
review
spans
on
publications
reported
Pubmed
from
2003
2023,
resulting
searching
“chronic
leukemia”
“artificial
intelligence”.
time
frame
reflects
real
literature
production
was
not
restricted.
We
take
opportunity
discussing
main
pitfalls
key
points
must
respond,
especially
considering
critical
role
‘human’
factor,
remains
domain.
Journal of Clinical Medicine,
Journal Year:
2024,
Volume and Issue:
13(15), P. 4337 - 4337
Published: July 25, 2024
BC,
affecting
both
women
and
men,
is
a
complex
disease
where
early
diagnosis
plays
crucial
role
in
successful
treatment
enhances
patient
survival
rates.
The
Metaverse,
virtual
world,
may
offer
new,
personalized
approaches
to
diagnosing
treating
BC.
Although
Artificial
Intelligence
(AI)
still
its
stages,
rapid
advancement
indicates
potential
applications
within
the
healthcare
sector,
including
consolidating
information
one
accessible
location.
This
could
provide
physicians
with
more
comprehensive
insights
into
details.
Leveraging
Metaverse
facilitate
clinical
data
analysis
improve
precision
of
diagnosis,
potentially
allowing
for
tailored
treatments
BC
patients.
However,
while
this
article
highlights
possible
transformative
impacts
technologies
on
treatment,
it
important
approach
these
developments
cautious
optimism,
recognizing
need
further
research
validation
ensure
enhanced
care
greater
accuracy
efficiency.
