BJR|Open,
Journal Year:
2023,
Volume and Issue:
6(1)
Published: Dec. 12, 2023
Wilms
tumour,
a
common
paediatric
cancer,
is
difficult
to
treat
in
low-
and
middle-income
countries
due
limited
access
imaging.
Artificial
intelligence
(AI)
has
been
introduced
for
staging,
detecting,
classifying
tumours,
aiding
physicians
decision-making.
However,
challenges
include
algorithm
accuracy,
translation
into
conventional
diagnosis,
reproducibility,
reliability.
As
AI
technology
advances,
radiomics,
an
tool,
emerges
extract
tumour
morphology
stage
information.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Jan. 29, 2024
Abstract
The
increase
in
eye
disorders
among
older
individuals
has
raised
concerns,
necessitating
early
detection
through
regular
examinations.
Age-related
macular
degeneration
(AMD),
a
prevalent
condition
over
45,
is
leading
cause
of
vision
impairment
the
elderly.
This
paper
presents
comprehensive
computer-aided
diagnosis
(CAD)
framework
to
categorize
fundus
images
into
geographic
atrophy
(GA),
intermediate
AMD,
normal,
and
wet
AMD
categories.
crucial
for
precise
age-related
enabling
timely
intervention
personalized
treatment
strategies.
We
have
developed
novel
system
that
extracts
both
local
global
appearance
markers
from
images.
These
are
obtained
entire
retina
iso-regions
aligned
with
optical
disc.
Applying
weighted
majority
voting
on
best
classifiers
improves
performance,
resulting
an
accuracy
96.85%,
sensitivity
93.72%,
specificity
97.89%,
precision
93.86%,
F1
ROC
95.85%,
balanced
95.81%,
sum
95.38%.
not
only
achieves
high
but
also
provides
detailed
assessment
severity
each
retinal
region.
approach
ensures
final
aligns
physician’s
understanding
aiding
them
ongoing
follow-up
patients.
Journal of Personalized Medicine,
Journal Year:
2024,
Volume and Issue:
14(5), P. 475 - 475
Published: April 29, 2024
The
massive
amount
of
human
biological,
imaging,
and
clinical
data
produced
by
multiple
diverse
sources
necessitates
integrative
modeling
approaches
able
to
summarize
all
this
information
into
answers
specific
questions.
In
paper,
we
present
a
hypermodeling
scheme
combine
models
cancer
aspects
regardless
their
underlying
method
or
scale.
Describing
tissue-scale
cell
proliferation,
biomechanical
tumor
growth,
nutrient
transport,
genomic-scale
aberrant
metabolism,
cell-signaling
pathways
that
regulate
the
cellular
response
therapy,
hypermodel
integrates
mutation,
miRNA
expression,
data.
constituting
hypomodels,
as
well
orchestration
links,
are
described.
Two
types,
Wilms
(nephroblastoma)
non-small
lung
cancer,
addressed
proof-of-concept
study
cases.
Personalized
simulations
actual
anatomy
patient
have
been
conducted.
has
also
applied
predict
control
after
radiotherapy
relationship
between
proliferative
activity
neoadjuvant
chemotherapy.
Our
innovative
holds
promise
digital
twin-based
decision
support
system
core
future
in
silico
trial
platforms,
although
additional
retrospective
adaptation
validation
necessary.
Journal of Cancer Research and Clinical Oncology,
Journal Year:
2024,
Volume and Issue:
150(5)
Published: April 30, 2024
To
investigate
the
clinical
value
of
contrast-enhanced
computed
tomography
(CECT)
radiomics
for
predicting
response
primary
lesions
to
neoadjuvant
chemotherapy
in
hepatoblastoma.
American Journal of Roentgenology,
Journal Year:
2024,
Volume and Issue:
223(2)
Published: May 29, 2024
Artificial
intelligence
(AI)
is
transforming
the
medical
imaging
of
adult
patients.
However,
its
utilization
in
pediatric
oncology
remains
constrained,
part
due
to
inherent
scarcity
data
associated
with
childhood
cancers.
Pediatric
cancers
are
rare,
and
technologies
evolving
rapidly,
leading
insufficient
a
particular
type
effectively
train
these
algorithms.
The
small
market
size
patients
compared
could
also
contribute
this
challenge,
as
driver
commercialization.
This
review
provides
an
overview
current
state
AI
applications
for
cancer
imaging,
including
image
acquisition,
processing,
reconstruction,
segmentation,
diagnosis,
staging,
treatment
response
monitoring.
Although
developments
promising,
impediments
diverse
anatomies
growing
children
nonstandardized
protocols
have
led
limited
clinical
translation
thus
far.
Opportunities
include
leveraging
reconstruction
algorithms
achieve
accelerated
low-dose
automating
generation
metric-based
staging
monitoring
scores.
Transfer
learning
adult-based
models
cancers,
multiinstitutional
sharing,
ethical
privacy
practices
rare
will
be
keys
unlocking
full
potential
improving
outcomes
young
Bioengineering,
Journal Year:
2024,
Volume and Issue:
11(6), P. 629 - 629
Published: June 19, 2024
Prostate
cancer
is
a
significant
health
concern
with
high
mortality
rates
and
substantial
economic
impact.
Early
detection
plays
crucial
role
in
improving
patient
outcomes.
This
study
introduces
non-invasive
computer-aided
diagnosis
(CAD)
system
that
leverages
intravoxel
incoherent
motion
(IVIM)
parameters
for
the
of
prostate
(PCa).
IVIM
imaging
enables
differentiation
water
molecule
diffusion
within
capillaries
outside
vessels,
offering
valuable
insights
into
tumor
characteristics.
The
proposed
approach
utilizes
two-step
segmentation
through
use
three
U-Net
architectures
extracting
tumor-containing
regions
interest
(ROIs)
from
segmented
images.
performance
CAD
thoroughly
evaluated,
considering
optimal
classifier
comparing
diagnostic
value
commonly
used
apparent
coefficient
(ADC).
results
demonstrate
combination
central
zone
(CZ)
peripheral
(PZ)
features
Random
Forest
Classifier
(RFC)
yields
best
performance.
achieves
an
accuracy
84.08%
balanced
82.60%.
showcases
sensitivity
(93.24%)
reasonable
specificity
(71.96%),
along
good
precision
(81.48%)
F1
score
(86.96%).
These
findings
highlight
effectiveness
accurately
segmenting
diagnosing
PCa.
represents
advancement
methods
early
PCa,
showcasing
potential
machine
learning
techniques.
developed
solution
has
to
revolutionize
PCa
diagnosis,
leading
improved
outcomes
reduced
healthcare
costs.
Biomedicines,
Journal Year:
2024,
Volume and Issue:
12(7), P. 1455 - 1455
Published: June 30, 2024
Wilms
tumor
(WT),
or
nephroblastoma,
is
the
predominant
renal
malignancy
in
pediatric
population.
This
narrative
review
explores
evolution
of
personalized
care
strategies
for
WT,
synthesizing
critical
developments
molecular
diagnostics
and
treatment
approaches
to
enhance
patient-specific
outcomes.
We
surveyed
recent
literature
from
last
five
years,
focusing
on
high-impact
research
across
major
databases
such
as
PubMed,
Scopus,
Web
Science.
Diagnostic
advancements,
including
liquid
biopsies
diffusion-weighted
MRI,
have
improved
early
detection
precision.
The
prognostic
significance
genetic
markers,
particularly
WT1
mutations
miRNA
profiles,
discussed.
Novel
predictive
tools
integrating
clinical
data
anticipate
disease
trajectory
therapy
response
are
explored.
Progressive
strategies,
immunotherapy
targeted
agents
HIF-2α
inhibitors
GD2-targeted
immunotherapy,
highlighted
their
role
protocols,
especially
refractory
recurrent
WT.
underscores
necessity
management
supported
by
insights,
with
survival
rates
localized
exceeding
90%.
However,
knowledge
gaps
persist
therapies
high-risk
patients
reduce
long-term
treatment-related
morbidity.
In
conclusion,
this
highlights
need
ongoing
research,
outcomes
emerging
multi-omic
inform
decision-making,
paving
way
more
individualized
pathways.
