Challenges and limitations in applying radiomics to PET imaging: Possible opportunities and avenues for research
Computers in Biology and Medicine,
Год журнала:
2024,
Номер
179, С. 108827 - 108827
Опубликована: Июль 3, 2024
Radiomics,
the
high-throughput
extraction
of
quantitative
imaging
features
from
medical
images,
holds
immense
potential
for
advancing
precision
medicine
in
oncology
and
beyond.
While
radiomics
applied
to
positron
emission
tomography
(PET)
offers
unique
insights
into
tumor
biology
treatment
response,
it
is
imperative
elucidate
challenges
constraints
inherent
this
domain
facilitate
their
translation
clinical
practice.
This
review
examines
limitations
applying
PET
imaging,
synthesizing
findings
last
five
years
(2019-2023)
highlights
significance
addressing
these
realize
full
molecular
imaging.
A
comprehensive
search
was
conducted
across
multiple
electronic
databases,
including
PubMed,
Scopus,
Web
Science,
using
keywords
relevant
issues
Only
studies
published
peer-reviewed
journals
were
eligible
inclusion
review.
Although
many
have
highlighted
predicting
assessing
heterogeneity,
enabling
risk
stratification,
personalized
therapy
selection,
various
regarding
practical
implementation
proposed
models
still
need
be
addressed.
illustrates
cancer
types,
encompassing
both
phantom
investigations.
The
analyzed
highlight
importance
reproducible
segmentation
methods,
standardized
pre-processing
post-processing
methodologies,
create
large
multicenter
registered
a
centralized
database
promote
continuous
validation
integration
Язык: Английский
Integrating ChatGPT, Bard, and leading-edge generative artificial intelligence in architectural design and engineering: applications, framework, and challenges
SSRN Electronic Journal,
Год журнала:
2023,
Номер
unknown
Опубликована: Янв. 1, 2023
This
research
paper
delves
into
the
integration
of
advanced
generative
artificial
intelligence
(AI)
models,
such
as
ChatGPT,
Bard,
and
similar
architectures,
within
realms
architectural
design
engineering.
The
comprehensive
study
explores
various
aspects,
including
applications,
frameworks,
challenges,
prospective
developments
in
context
In
domain
design,
investigates
transformative
impact
on
Architectural
Theory,
highlighting
how
AI
fosters
creativity
innovation
thinking.
Design
Process
is
scrutinized,
showcasing
models
streamline
ideation,
iteration,
collaboration
among
teams.
role
Representation
Visualization
explored,
emphasizing
its
capacity
to
generate
immersive
realistic
visualizations.
Furthermore,
examines
influence
Interior
Design,
Urban
Planning,
considers
nuanced
aspects
Cultural
Social
factors,
elucidating
these
technologies
contribute
inclusive
context-sensitive
practices.
Within
realm
engineering,
assesses
Structural
Engineering,
demonstrating
potential
optimize
innovate
structural
analysis
designs
for
enhanced
safety
efficiency.
It
applications
Building
Systems
Construction
Management,
illustrating
can
project
workflows
resource
allocation.
compliance
with
Codes
Regulations
analyzed,
error
reduction
adherence
standards.
Additionally,
probes
Materials
Technology,
advancements
material
selection
construction
methodologies.
also
promoting
Sustainability
Environmental
energy
efficiency,
reduce
environmental
impact,
enhance
overall
sustainability.
While
presenting
critically
evaluates
challenges
posed
by
integrating
domains,
ethical
considerations,
bias
mitigation,
user
adaptability.
Finally,
it
outlines
future
directions
development,
necessity
interdisciplinary
collaboration,
guidelines,
ongoing
fully
harness
shaping
Язык: Английский
Integrating ChatGPT, Bard, and Leading-Edge Generative Artificial Intelligence in Architectural Design and Engineering: Applications, Framework, and Challenges
International Journal of Architecture and Planning,
Год журнала:
2023,
Номер
3(2), С. 92 - 124
Опубликована: Сен. 5, 2023
This
research
paper
investigates
the
integration
of
advanced
generative
artificial
intelligence
(AI)
models,
such
as
ChatGPT,
Bard,
and
similar
architectures,
in
architectural
design
engineering.The
comprehensive
study
explores
various
aspects,
including
applications,
frameworks,
challenges,
prospective
developments
context
engineering.In
design,
transformative
impact
on
Architectural
Theory,
highlighting
how
AI
fosters
creativity
innovation
thinking.The
Design
Process
is
scrutinized,
showcasing
models
streamline
ideation,
iteration,
collaboration
among
teams.Furthermore,
examines
influence
Interior
Design,
Urban
Planning,
considers
nuanced
aspects
Cultural
Social
factors,
elucidating
these
technologies
contribute
to
inclusive
context-sensitive
practices.In
engineering,
assesses
Structural
Engineering,
demonstrating
its
potential
optimize
innovate
structural
analysis
designs
for
enhanced
safety
efficiency.It
applications
Building
Systems
Construction
Management,
illustrating
can
project
workflows
resource
allocation.The
compliance
with
Codes
Regulations
analyzed,
emphasizing
error
reduction
adherence
standards.Additionally,
probes
into
Materials
Technology,
advancements
material
selection
construction
methodologies.The
also
role
promoting
Sustainability
Environmental
energy
efficiency,
reduce
environmental
impact,
enhance
overall
sustainability.Finally,
outlines
challenges
future
directions
development
fully
harness
shaping
engineering.
Язык: Английский
MRI and CT radiomics for the diagnosis of acute pancreatitis
European Journal of Radiology Open,
Год журнала:
2025,
Номер
14, С. 100636 - 100636
Опубликована: Янв. 31, 2025
Язык: Английский
Making sense of radiomics: insights on human–AI collaboration in medical interaction from an observational user study
Frontiers in Communication,
Год журнала:
2024,
Номер
8
Опубликована: Фев. 12, 2024
Technologies
based
on
“artificial
intelligence”
(AI)
are
transforming
every
part
of
our
society,
including
healthcare
and
medical
institutions.
An
example
this
trend
is
the
novel
field
in
oncology
radiology
called
radiomics,
which
extracting
mining
large-scale
quantitative
features
from
imaging
by
machine-learning
(ML)
algorithms.
This
paper
explores
situated
work
with
a
radiomics
software
platform,
QuantImage
(v2),
interaction
around
it,
educationally
framed
hands-on
trial
sessions
where
pairs
novice
users
(physicians
technicians)
task
consisting
developing
predictive
ML
model
co-present
tutor.
Informed
ethnomethodology
conversation
analysis
(EM/CA),
results
show
that
learning
about
more
generally
how
to
use
platform
specifically
deeply
intertwined.
Common-sense
knowledge
(e.g.,
meanings
colors)
can
interfere
visual
representation
standards
established
professional
domain.
Participants'
skills
using
routinely
displayed
assessment
performance
measures
resulting
models,
monitoring
platform's
pace
operation
for
possible
problems,
ascribing
independent
actions
related
algorithms)
platform.
The
findings
relevant
current
discussions
explainability
AI
medicine
as
well
issues
machinic
agency.
Язык: Английский
Reproducibility of lung cancer radiomics features extracted from data-driven respiratory gating and free-breathing flow imaging in [18F]-FDG PET/CT
European Journal of Hybrid Imaging,
Год журнала:
2022,
Номер
6(1)
Опубликована: Окт. 29, 2022
Abstract
Background
Quality
and
reproducibility
of
radiomics
studies
are
essential
requirements
for
the
standardisation
models.
As
recent
data-driven
respiratory
gating
(DDG)
[
18
F]-FDG
has
shown
superior
diagnostic
performance
in
lung
cancer,
we
evaluated
impact
DDG
on
features
derived
from
PET/CT
comparison
to
free-breathing
flow
(FB)
imaging.
Methods
Twenty
four
nodules
20
patients
were
delineated.
Radiomics
FB
corresponding
reconstruction
using
QuantImage
v2
platform.
Lin’s
concordance
factor
(
C
b
)
mean
difference
percentage
(DIFF%)
calculated
each
feature
delineated
which
also
classified
by
anatomical
localisation
volume.
Non-reproducible
defined
as
having
a
bias
correction
<
0
.
8
and/or
DIFF%
>
10.
Results
In
total
141
computed
analysis,
10
non-reproducible
all
pulmonary
lesions.
Those
first-order
Laplacian
Gaussian
(LoG)-filtered
images
(sigma
=
1
mm):
Energy,
Kurtosis,
Minimum,
Range,
Root
Mean
Squared,
Skewness
Variance;
Texture
Gray
Level
Cooccurence
Matrix
(GLCM):
Cluster
Prominence
Difference
First-order
Standardised
Uptake
Value
(SUV)
feature:
Kurtosis.
Pulmonary
lesions
located
lobes
had
only
stable
features,
ones
lower
parts
25
features.
with
greater
size
(defined
long
axis
length
median)
showed
higher
(9
features)
than
smaller
(20
features).
Conclusion
Calculated
lesions,
131
out
can
be
used
interchangeably
between
acquisitions.
inferior
subject
variability
well
size.
Язык: Английский
The Impact and Integration of Cloud Computing for Enhanced Patient Care and Operational Efficiency
Advances in medical technologies and clinical practice book series,
Год журнала:
2024,
Номер
unknown, С. 277 - 299
Опубликована: Июнь 21, 2024
Cloud
computing
is
reshaping
healthcare
by
offering
a
flexible
solution
for
stakeholders
to
access
data
remotely.
It
revolutionizes
creation,
storage,
and
sharing,
enabling
professionals
patient
information
from
anywhere,
enhancing
care
streamlining
operations.
Adoption
increasing
due
its
efficiency
innovation
benefits.
Services
like
SaaS,
PaaS,
IaaS
offer
flexibility,
driving
adoption.
Challenges
include
breaches,
necessitating
robust
security
measures.
Despite
challenges,
cloud
has
transformed
healthcare,
improving
decision-making,
security,
record
automation.
During
COVID-19,
it
been
crucial,
highlighting
importance
in
advancing
healthcare.
Providers
must
embrace
technology
potential
enhance
medical
analysis
improve
services.
Язык: Английский
Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature Evaluation
Diagnostics,
Год журнала:
2024,
Номер
14(23), С. 2760 - 2760
Опубликована: Дек. 8, 2024
Background/Objectives:
The
integration
of
machine
learning
into
the
domain
radiomics
has
revolutionized
approach
to
personalized
medicine,
particularly
in
oncology.
Our
research
presents
RadTA
(RADiomics
Trend
Analysis),
a
novel
framework
developed
facilitate
automatic
analysis
quantitative
imaging
biomarkers
(QIBs)
from
time-series
CT
volumes.
Methods:
is
designed
bridge
technical
gap
for
medical
experts
and
enable
sophisticated
radiomic
analyses
without
deep
expertise.
core
includes
an
automated
command
line
interface,
streamlined
image
segmentation,
comprehensive
feature
extraction,
robust
evaluation
mechanisms.
utilizes
advanced
segmentation
models,
specifically
TotalSegmentator
Body
Composition
Analysis
(BCA),
accurately
delineate
anatomical
structures
scans.
These
models
extraction
wide
variety
features,
which
are
subsequently
processed
compared
assess
health
dynamics
across
timely
corresponding
series.
Results:
effectiveness
was
tested
using
HNSCC-3DCT-RT
dataset,
scans
oncological
patients
undergoing
radiation
therapy.
results
demonstrate
significant
changes
tissue
composition
provide
insights
physical
effects
treatment.
Conclusions:
demonstrates
step
clinical
adoption
field
radiomics,
offering
user-friendly,
robust,
effective
tool
patient
dynamics.
It
can
potentially
also
be
used
other
specialties.
Язык: Английский