Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT05637788 |
| Other study ID # |
ART.I.HCC |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
July 1, 2021 |
| Est. completion date |
November 1, 2025 |
Study information
| Verified date |
March 2024 |
| Source |
Humanitas Clinical and Research Center |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial 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.
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.
