Life,
Journal Year:
2025,
Volume and Issue:
15(2), P. 320 - 320
Published: Feb. 19, 2025
Metastatic
colorectal
cancer
(mCRC)
is
a
severe
condition
with
high
rates
of
illness
and
death.
Current
treatments
are
limited
not
always
effective
because
the
responds
differently
to
drugs
in
different
patients.
This
research
aims
use
artificial
intelligence
(AI)
improve
treatment
by
predicting
which
therapies
will
work
best
for
individual
By
analyzing
large
sets
patient
data
using
machine
learning,
we
hope
create
model
that
can
identify
patients
respond
chemotherapy,
either
alone
or
combined
other
targeted
treatments.
The
study
involve
dividing
into
training
validation
develop
test
models,
avoiding
overfitting.
Various
learning
algorithms,
like
random
survival
forest
neural
networks,
be
integrated
highly
accurate
stable
predictive
model.
model's
performance
evaluated
statistical
measures
such
as
sensitivity,
specificity,
area
under
curve
(AUC).
aim
personalize
treatments,
outcomes,
reduce
healthcare
costs,
make
process
more
efficient.
If
successful,
this
could
significantly
impact
medical
community
providing
new
tool
better
managing
treating
mCRC,
leading
personalized
care.
In
addition,
examine
applicability
methods
biomarker
discovery
therapy
prediction
considering
recent
narrative
publications.
Nature Medicine,
Journal Year:
2023,
Volume and Issue:
29(5), P. 1273 - 1286
Published: May 1, 2023
The
lack
of
multi-omics
cancer
datasets
with
extensive
follow-up
information
hinders
the
identification
accurate
biomarkers
clinical
outcome.
In
this
cohort
study,
we
performed
comprehensive
genomic
analyses
on
fresh-frozen
samples
from
348
patients
affected
by
primary
colon
cancer,
encompassing
RNA,
whole-exome,
deep
T
cell
receptor
and
16S
bacterial
rRNA
gene
sequencing
tumor
matched
healthy
tissue,
complemented
whole-genome
for
further
microbiome
characterization.
A
type
1
helper
cell,
cytotoxic,
expression
signature,
called
Immunologic
Constant
Rejection,
captured
presence
clonally
expanded,
tumor-enriched
clones
outperformed
conventional
prognostic
molecular
biomarkers,
such
as
consensus
subtype
microsatellite
instability
classifications.
Quantification
genetic
immunoediting,
defined
a
lower
number
neoantigens
than
expected,
refined
its
value.
We
identified
driven
Ruminococcus
bromii,
associated
favorable
By
combining
signature
developed
validated
composite
score
(mICRoScore),
which
identifies
group
excellent
survival
probability.
publicly
available
dataset
provides
resource
better
understanding
biology
that
could
facilitate
discovery
personalized
therapeutic
approaches.
Cancer Discovery,
Journal Year:
2024,
Volume and Issue:
14(5), P. 711 - 726
Published: March 21, 2024
Artificial
intelligence
(AI)
in
oncology
is
advancing
beyond
algorithm
development
to
integration
into
clinical
practice.
This
review
describes
the
current
state
of
field,
with
a
specific
focus
on
integration.
AI
applications
are
structured
according
cancer
type
and
domain,
focusing
four
most
common
cancers
tasks
detection,
diagnosis,
treatment.
These
encompass
various
data
modalities,
including
imaging,
genomics,
medical
records.
We
conclude
summary
existing
challenges,
evolving
solutions,
potential
future
directions
for
field.
Journal of Pathology Informatics,
Journal Year:
2024,
Volume and Issue:
unknown, P. 100357 - 100357
Published: Jan. 1, 2024
Computational
Pathology
(CPath)
is
an
interdisciplinary
science
that
augments
developments
of
computational
approaches
to
analyze
and
model
medical
histopathology
images.
The
main
objective
for
CPath
develop
infrastructure
workflows
digital
diagnostics
as
assistive
CAD
system
clinical
pathology,
facilitating
transformational
changes
in
the
diagnosis
treatment
cancer
are
mainly
address
by
tools.
With
evergrowing
deep
learning
computer
vision
algorithms,
ease
data
flow
from
currently
witnessing
a
paradigm
shift.
Despite
sheer
volume
engineering
scientific
works
being
introduced
image
analysis,
there
still
considerable
gap
adopting
integrating
these
algorithms
practice.
This
raises
significant
question
regarding
direction
trends
undertaken
CPath.
In
this
article
we
provide
comprehensive
review
more
than
800
papers
challenges
faced
problem
design
all-the-way
application
implementation
viewpoints.
We
have
catalogued
each
paper
into
model-card
examining
key
layout
current
landscape
hope
helps
community
locate
relevant
facilitate
understanding
field's
future
directions.
nutshell,
oversee
cycle
stages
which
required
be
cohesively
linked
together
associated
with
such
multidisciplinary
science.
overview
different
perspectives
data-centric,
model-centric,
application-centric
problems.
finally
sketch
remaining
directions
technical
integration
For
updated
information
on
survey
accessing
original
cards
repository,
please
refer
GitHub.
Updated
version
draft
can
also
found
arXiv.
Cell,
Journal Year:
2024,
Volume and Issue:
187(10), P. 2502 - 2520.e17
Published: May 1, 2024
Human
tissue,
which
is
inherently
three-dimensional
(3D),
traditionally
examined
through
standard-of-care
histopathology
as
limited
two-dimensional
(2D)
cross-sections
that
can
insufficiently
represent
the
tissue
due
to
sampling
bias.
To
holistically
characterize
histomorphology,
3D
imaging
modalities
have
been
developed,
but
clinical
translation
hampered
by
complex
manual
evaluation
and
lack
of
computational
platforms
distill
insights
from
large,
high-resolution
datasets.
We
present
TriPath,
a
deep-learning
platform
for
processing
volumes
efficiently
predicting
outcomes
based
on
morphological
features.
Recurrence
risk-stratification
models
were
trained
prostate
cancer
specimens
imaged
with
open-top
light-sheet
microscopy
or
microcomputed
tomography.
By
comprehensively
capturing
morphologies,
volume-based
prognostication
achieves
superior
performance
traditional
2D
slice-based
approaches,
including
clinical/histopathological
baselines
six
certified
genitourinary
pathologists.
Incorporating
greater
volume
improves
prognostic
mitigates
risk
prediction
variability
bias,
further
emphasizing
value
larger
extents
heterogeneous
morphology.
