Frontiers in Oncology,
Journal Year:
2025,
Volume and Issue:
15
Published: May 1, 2025
Immunotherapy
has
transformed
the
landscape
of
melanoma
treatment,
offering
significant
extensions
in
survival
for
many
patients.
Despite
these
advancements,
nearly
50%
cases
remain
resistant
to
such
therapies,
highlighting
need
novel
approaches.
Emerging
research
identified
lipid
metabolism
reprogramming
as
a
key
factor
promoting
progression
and
resistance
immunotherapy.
This
not
only
supports
tumor
growth
metastasis
but
also
creates
an
immunosuppressive
environment
that
impairs
effectiveness
treatments
immune
checkpoint
inhibitors
(ICIs).
review
delves
into
intricate
relationship
between
system
interactions
melanoma.
We
will
explore
how
alterations
metabolic
pathways
contribute
evasion
therapy
resistance,
emphasizing
recent
discoveries
this
area.
Additionally,
we
highlights
therapeutic
strategies
targeting
enhance
inhibitor
(ICI)
efficacy.
Frontiers in Immunology,
Journal Year:
2023,
Volume and Issue:
13
Published: Jan. 4, 2023
Tumor
immunotherapy,
particularly
the
use
of
immune
checkpoint
inhibitors,
has
yielded
impressive
clinical
benefits.
Therefore,
it
is
critical
to
accurately
screen
individuals
for
immunotherapy
sensitivity
and
forecast
its
efficacy.
With
application
artificial
intelligence
(AI)
in
medical
field
recent
years,
an
increasing
number
studies
have
indicated
that
efficacy
can
be
better
anticipated
with
help
AI
technology
reach
precision
medicine.
This
article
focuses
on
current
prediction
models
based
information
from
histopathological
slides,
imaging-omics,
genomics,
proteomics,
reviews
their
research
progress
applications.
Furthermore,
we
also
discuss
existing
challenges
encountered
by
as
well
future
directions
need
improved,
provide
a
point
reference
early
implementation
AI-assisted
diagnosis
treatment
systems
future.
Cancers,
Journal Year:
2023,
Volume and Issue:
15(9), P. 2573 - 2573
Published: April 30, 2023
Cancer
care
increasingly
relies
on
imaging
for
patient
management.
The
two
most
common
cross-sectional
modalities
in
oncology
are
computed
tomography
(CT)
and
magnetic
resonance
(MRI),
which
provide
high-resolution
anatomic
physiological
imaging.
Herewith
is
a
summary
of
recent
applications
rapidly
advancing
artificial
intelligence
(AI)
CT
MRI
oncological
that
addresses
the
benefits
challenges
resultant
opportunities
with
examples.
Major
remain,
such
as
how
best
to
integrate
AI
developments
into
clinical
radiology
practice,
vigorous
assessment
quantitative
MR
data
accuracy,
reliability
utility
research
integrity
oncology.
Such
necessitate
an
evaluation
robustness
biomarkers
be
included
developments,
culture
sharing,
cooperation
knowledgeable
academics
vendor
scientists
companies
operating
fields.
Herein,
we
will
illustrate
few
solutions
these
efforts
using
novel
methods
synthesizing
different
contrast
modality
images,
auto-segmentation,
image
reconstruction
examples
from
lung
well
abdome,
pelvis,
head
neck
MRI.
community
must
embrace
need
metrics
beyond
lesion
size
measurement.
extraction
longitudinal
tracking
registered
lesions
understanding
tumor
environment
invaluable
interpreting
disease
status
treatment
efficacy.
This
exciting
time
work
together
move
field
forward
narrow
AI-specific
tasks.
New
datasets
used
improve
personalized
management
cancer
patients.
Cureus,
Journal Year:
2024,
Volume and Issue:
unknown
Published: May 9, 2024
This
comprehensive
literature
review
explores
the
transformative
impact
of
artificial
intelligence
(AI)
predictive
analytics
on
healthcare,
particularly
in
improving
patient
outcomes
regarding
disease
progression,
treatment
response,
and
recovery
rates.
AI,
encompassing
capabilities
such
as
learning,
problem-solving,
decision-making,
is
leveraged
to
predict
optimize
plans,
enhance
rates
through
analysis
vast
datasets,
including
electronic
health
records
(EHRs),
imaging,
genetic
data.
The
utilization
machine
learning
(ML)
deep
(DL)
techniques
enables
personalized
medicine
by
facilitating
early
detection
conditions,
precision
drug
discovery,
tailoring
individual
profiles.
Ethical
considerations,
data
privacy,
bias,
accountability,
emerge
vital
responsible
implementation
AI
healthcare.
findings
underscore
potential
revolutionizing
clinical
decision-making
healthcare
delivery,
emphasizing
necessity
ethical
guidelines
continuous
model
validation
ensure
its
safe
effective
use
augmenting
human
judgment
medical
practice.
Diagnostics,
Journal Year:
2024,
Volume and Issue:
14(2), P. 174 - 174
Published: Jan. 12, 2024
Pancreatic
cancer
is
a
highly
aggressive
and
difficult-to-detect
with
poor
prognosis.
Late
diagnosis
common
due
to
lack
of
early
symptoms,
specific
markers,
the
challenging
location
pancreas.
Imaging
technologies
have
improved
diagnosis,
but
there
still
room
for
improvement
in
standardizing
guidelines.
Biopsies
histopathological
analysis
are
tumor
heterogeneity.
Artificial
Intelligence
(AI)
revolutionizes
healthcare
by
improving
treatment,
patient
care.
AI
algorithms
can
analyze
medical
images
precision,
aiding
disease
detection.
also
plays
role
personalized
medicine
analyzing
data
tailor
treatment
plans.
It
streamlines
administrative
tasks,
such
as
coding
documentation,
provides
assistance
through
chatbots.
However,
challenges
include
privacy,
security,
ethical
considerations.
This
review
article
focuses
on
potential
transforming
pancreatic
care,
offering
diagnostics,
treatments,
operational
efficiency,
leading
better
outcomes.
Archives of Pathology & Laboratory Medicine,
Journal Year:
2024,
Volume and Issue:
148(7), P. 757 - 774
Published: April 16, 2024
Context.—
Rapid
advancements
in
the
understanding
and
manipulation
of
tumor-immune
interactions
have
led
to
approval
immune
therapies
for
patients
with
non–small
cell
lung
cancer.
Certain
checkpoint
inhibitor
require
use
companion
diagnostics,
but
methodologic
variability
has
uncertainty
around
test
selection
implementation
practice.
Objective.—
To
develop
evidence-based
guideline
recommendations
testing
immunotherapy/immunomodulatory
biomarkers,
including
programmed
death
ligand-1
(PD-L1)
tumor
mutation
burden
(TMB),
Design.—
The
College
American
Pathologists
convened
a
panel
experts
cancer
biomarker
accordance
standards
trustworthy
clinical
practice
guidelines
established
by
National
Academy
Medicine.
A
systematic
literature
review
was
conducted
address
8
key
questions.
Using
Grading
Recommendations
Assessment,
Development,
Evaluation
(GRADE)
approach,
were
created
from
available
evidence,
certainty
that
judgments
as
defined
GRADE
Evidence
Decision
framework.
Results.—
Six
recommendation
statements
developed.
Conclusions.—
This
summarizes
current
hurdles
associated
PD-L1
expression
TMB
therapy
advanced
presents
setting.
Cancer Immunology Immunotherapy,
Journal Year:
2024,
Volume and Issue:
73(8)
Published: June 4, 2024
Abstract
Background
The
non-invasive
biomarkers
for
predicting
immunotherapy
response
are
urgently
needed
to
prevent
both
premature
cessation
of
treatment
and
ineffective
extension.
This
study
aimed
construct
a
model
response,
based
on
the
integration
deep
learning
habitat
radiomics
in
patients
with
advanced
non-small
cell
lung
cancer
(NSCLC).
Methods
Independent
patient
cohorts
from
three
medical
centers
were
enrolled
training
(
n
=
164)
test
82).
Habitat
imaging
features
derived
sub-regions
clustered
individual’s
tumor
by
K-means
method.
extracted
3D
ResNet
algorithm.
Pearson
correlation
coefficient,
T
least
absolute
shrinkage
selection
operator
regression
used
select
features.
Support
vector
machine
was
applied
implement
radiomics,
respectively.
Then,
combination
developed
integrating
sources
data.
Results
obtained
strong
well-performance,
achieving
area
under
receiver
operating
characteristics
curve
0.865
(95%
CI
0.772–0.931).
significantly
discerned
high
low-risk
patients,
exhibited
significant
benefit
clinical
use.
Conclusion
deep-leaning
contributed
NSCLC.
may
be
as
potential
tool
individual
management.
