Artificial intelligence in gastrointestinal cancers: diagnostic, prognostic, and surgical strategies
Cancer Letters,
Год журнала:
2025,
Номер
612, С. 217461 - 217461
Опубликована: Янв. 12, 2025
Язык: Английский
Dual-Time-Point Radiomics for Prognosis Prediction in Colorectal Liver Metastasis Treated with Neoadjuvant Therapy Before Radical Resection: A Two-Center Study
Annals of Surgical Oncology,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 5, 2025
Язык: Английский
Computed Tomography‐Based Habitat Analysis for Prognostic Stratification in Colorectal Liver Metastases
Cancer Innovation,
Год журнала:
2025,
Номер
4(2)
Опубликована: Март 12, 2025
ABSTRACT
Background
Colorectal
liver
metastasis
(CRLM)
has
a
poor
prognosis,
and
traditional
prognostic
models
have
certain
limitations
in
clinical
application.
This
study
aims
to
evaluate
the
value
of
CT‐based
habitat
analysis
CRLM
patients
compare
it
with
existing
provide
more
evidence
for
individualized
treatment
patients.
Methods
retrospective
included
197
whose
preoperative
contrast‐enhanced
CT
images
corresponding
DICOM
Segmentation
Objects
(DSOs)
were
obtained
from
The
Cancer
Imaging
Archive
(TCIA).
Tumor
regions
segmented,
features
representing
distinct
subregions
extracted.
An
unsupervised
K‐means
clustering
algorithm
classified
tumors
into
two
clusters
based
on
their
characteristics.
Kaplan–Meier
was
used
overall
survival
(OS),
disease‐free
(DFS),
liver‐specific
DFS.
model's
predictive
performance
compared
Clinical
Risk
Score
(CRS)
Burden
(TBS)
using
concordance
index
(C‐index),
Integrated
Brier
(IBS),
time‐dependent
area
under
curve
(AUC).
Results
model
identified
patient
significant
differences
OS,
DFS,
DFS
(
p
<
0.01).
Compared
CRS
TBS,
demonstrated
superior
accuracy,
particularly
higher
AUC
values
improved
calibration
(lower
IBS).
Conclusions
captures
spatial
tumor
heterogeneity
provides
enhanced
stratification
CRLM.
method
outperforms
conventional
offers
potential
personalized
planning.
Язык: Английский
Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis
BMC Medical Imaging,
Год журнала:
2024,
Номер
24(1)
Опубликована: Окт. 7, 2024
The
aim
of
this
study
was
to
conduct
a
systematic
review
and
meta-analysis
comprehensively
evaluate
the
performance
methodological
quality
artificial
intelligence
(AI)
in
predicting
recurrence
after
single
first-line
treatment
for
liver
cancer.
A
rigorous
evaluation
conducted
on
AI
studies
related
cancer,
retrieved
from
PubMed,
Embase,
Web
Science,
Cochrane
Library,
CNKI
databases.
area
under
curve
(AUC),
sensitivity
(SENC),
specificity
(SPEC)
each
were
extracted
meta-analysis.
Six
percutaneous
ablation
(PA)
studies,
16
surgical
resection
(SR)
5
transarterial
chemoembolization
(TACE)
included
hepatocellular
carcinoma
(HCC)
treatment,
respectively.
Four
SR
2
PA
intrahepatic
cholangiocarcinoma
(ICC)
colorectal
cancer
metastasis
(CRLM)
treatment.
pooled
SENC,
SEPC,
AUC
primary
HCC
via
PA,
SR,
TACE
0.78,
0.90,
0.92;
0.81,
0.77,
0.86;
0.73,
0.79,
values
ICC
treated
with
CRLM
0.85,
0.71,
0.86
0.69,
0.63,0.74,
This
demonstrates
comprehensive
application
value
satisfactory
results,
indicating
clinical
translation
potential
Язык: Английский