Trial Title:
Artificial Intelligence and Hepatocellular Carcinoma
NCT ID:
NCT05637788
Condition:
HCC
Conditions: Official terms:
Carcinoma
Carcinoma, Hepatocellular
Conditions: Keywords:
HCC
hepatocellular carcinoma
hepatectomy
liver primary tumor
recurrence
artificial intelligence
machine learning
prognostic prediction
microvascular invasion
Study type:
Observational
Overall status:
Active, not recruiting
Study design:
Time perspective:
Retrospective
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
CT Scan Radiomics Features Extraction
Description:
Extraction of CT radiomics data through AI
Arm group label:
HCC Patients Submitted Surgery
Summary:
To identify new relevant biomarkers for HCC patients and their risk of recurrence.
Radiomics data and computer-vision data will be explored for their ability to predict the
presence of particular pathological signs of aggressiveness (microvascular invasion and
satellitosis), and the prognosis after surgery.
Detailed description:
Hepatocellular carcinoma (HCC) is 1 of the 5 most common malignancies worldwide and the
third most common cause of cancer related mortality of 500,000 deaths globally every
year.
Although more common in East Asia, the incidence of HCC is increasing in the Western
world. Hepatic resection is the first-line therapeutic option, and it is accepted as a
safe treatment with a proven impact on prognosis, with a low operative mortality as the
result of advances in surgical techniques and perioperative management. Nevertheless,
surgical resection is applicable in only about 20% to 30% of patients with HCC, since
most have poor hepatic reserve function caused by underlying chronic liver disease and
multi focal hepatic distributions of HCC. Although hepatic resection is one of the
curative treatments for hepatocellular carcinoma, the recurrence rate of HCC even after
curative resection is quite high, estimated to be approximately 50 % during the first 3
years and more than 70 % during the first 5 years after curative resection, and so the
postoperative long term results remain unsatisfactory. In this scenario the role of liver
transplantation has been, in the last years, predominant, due to the ability of
transplant to reduce disease recurrence, because of the treatment of liver cirrhosis
associate to HCC which represent the most important driver to recurrence. Otherwise, the
scarcity of organ source has been a boost to the spread of liver resection, not only
confined in the boundary taken into account in the BCLC algorithm (guidelines endorsed by
EASL and AASLD), but even in patients considered not suitable for curative treatment as
well as liver resection. Although surgical treatment has been adopted in the last years
in more patients outside the Guidelines with satisfactory results in terms of mortality,
morbidity and Short term oncological outcomes, the limits of this approach remain the
long term disease free survival.
Risk factor for recurrence has been yet identified in the last years as hcc dimension,
grading, microvascular invasion and satellitosis. The evidence that these two prognostic
factors could negatively impact on the long term prognosis enhancing the risk of
recurrence, has led many Author to propose anatomical resection (segmental resection) as
the ideal surgical treatment to reduce these risks in HCC patients. Otherwise, literature
results are in conflict regarding the real benefit of this approach. In fact in many
patients with HCC and underlying cirrhosis the anatomical approach is not feasible due to
the risk of postoperative liver failure. So a parenchyma-sparing technique has been
developed and compared to anatomical resection in terms of oncological outcomes. At the
present, all these risk factors are not predictable, and the staging systems are based
only on crude radiological features as the number and the size of the nodules. In the
recent years, several authors proposed new approaches to increase our ability to extract
data from the radiological imaging: by the analysis of the measurements and numbers
obtained during the radiological acquisition (by CT or MRI scans), thousands of other
information are obtainable, overcoming the ability of human eyes. Those techniques go
under the names of "Radiomics", which is a very promising branch when merged with the
novel machine learning algorithms (e.g. Deep Learning, Neural Networks, etc). Moreover,
nowadays, novel data can be obtained also by simple intraoperative photo obtained during
the surgical procedure, for example of the liver cut surface: by the "computer-vision
analysis", another powerful machine-learning algorithm, other data can be produced to
predict short and long term outcomes. Those potentialities rely on the modern field of
"artificial intelligence", where a machine is trained to recognize different recurrent
patterns to create prediction models with a very powerful accuracy. On these data is
based the proposal to create the present multicentric study with the aim to develop a
prediction model for post-operative complications and HCC recurrence, based on the
analysis of CT-radiomics features, liver cut surface photos and machine learning
analysis.
Criteria for eligibility:
Study pop:
Patients affected by hepatocellular carcinoma (first diagnosis) treated by liver
resection.
Sampling method:
Non-Probability Sample
Criteria:
Inclusion Criteria:
- Age >= 18 years old.
- Hepatocarcinoma diagnosis confirmed at histological specimen
- Being at the first HCC diagnosis or with a recurrence/persistence disease evaluated
and treated for the first time with surgery at the participating center.
- Available contrast-enhanced CT Scan obtained no more than 1 month prior to surgery.
Exclusion Criteria:
- Surgery as a downstaging therapy for transplant
- Patients treated with surgery in case of not-curative intent (palliation, best
supportive care, etc).
- Histopathological specimen of combined liver primary neoplasms (e.g.
'epatocolangiocarcinoma').
Gender:
All
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
IRCCS Istituto Clinico Humanitas
Address:
City:
Rozzano
Zip:
20089
Country:
Italy
Start date:
July 1, 2021
Completion date:
November 1, 2025
Lead sponsor:
Agency:
Humanitas Clinical and Research Center
Agency class:
Other
Collaborator:
Agency:
San Gerardo Hospital
Agency class:
Other
Collaborator:
Agency:
Ospedale "Carlo Poma" - Mantova
Agency class:
Other
Collaborator:
Agency:
Ospedale Centrale di Bolzano
Agency class:
Other
Collaborator:
Agency:
University of Pavia
Agency class:
Other
Collaborator:
Agency:
Morgagni Pierantoni Hospital
Agency class:
Other
Collaborator:
Agency:
Regina Elena Cancer Institute
Agency class:
Other
Collaborator:
Agency:
Universita di Verona
Agency class:
Other
Collaborator:
Agency:
University of Milano Bicocca
Agency class:
Other
Collaborator:
Agency:
Treviso Regional Hospital
Agency class:
Other
Collaborator:
Agency:
Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Agency class:
Other
Collaborator:
Agency:
Miulli General Hospital
Agency class:
Other
Collaborator:
Agency:
Federico II University
Agency class:
Other
Collaborator:
Agency:
Research Institute Against Digestive Cancer IRCAD
Agency class:
Other
Source:
Humanitas Clinical and Research Center
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05637788
