Ignite Creation Date:
2024-05-06 @ 3:55 PM
Last Modification Date:
2024-10-26 @ 1:59 PM
Study NCT ID:
NCT04802954
Status:
RECRUITING
Last Update Posted:
2023-12-13
First Post:
2021-03-10
Brief Title:
Risk Stratification of Hepatocarcinogenesis Using a Deep Learning Based Clinical Biological and Ultrasound Model in High-risk Patients
Sponsor:
IHU Strasbourg
Organization:
IHU Strasbourg
Study Overview
Official Title:
Risk Stratification of Hepatocarcinogenesis Using a Deep Learning Based Clinical Biological and Ultrasound Model in High-risk Patients
Status:
RECRUITING
Status Verified Date:
2023-12
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
STARHE
Brief Summary:
By 2030 hepatocellular carcinoma HCC will become the second leading cause of cancer-related death accounting for more than one million deaths per year according to the World Health Organization
To this date screening for hepatocellular carcinoma in France remains uniform for all patients based solely on a liver ultrasound every 6 months This strategy has three main limitations lack of personalisation low compliance relatively poor performance of the ultrasound
Risk stratification models have been developed for chronic hepatitis C alcoholic cirrhosis and non-alcoholic steatohepatitis NASH including clinical and biological parameters but no analysis of the liver parenchyma which is the physiopathological substrate of hepatocarcinogenesis
The advent of new artificial intelligence techniques could revolutionize the approach and lead to a personalised radiological screening strategy
Deep learning a subclass of machine learning is a popular area of research that can help humans performing certain tasks by automatically identifying new image features not defined by humans
The hypothesis of this study is that the non-tumor cirrhotic liver parenchyma is rich in structural information reflecting the severity of the hepatopathy its carcinological risk and the process of hepatocarcinogenesis Its analysis combined with clinical and biological data which have already been studied to stratify the risk of hepatocarcinogenesis will allow to define a very high-risk population particularly in the context of Hepatitis C Virus HCV eradication and Hepatitis B Virus HBV control
Consequently this study proposes to design prospectively a deep learning model for stratification of the risk of hepatocarcinogenesis by including clinical biological and radiological ultrasound parameters
To this date screening for hepatocellular carcinoma in France remains uniform for all patients based solely on a liver ultrasound every 6 months This strategy has three main limitations lack of personalisation low compliance relatively poor performance of the ultrasound
Risk stratification models have been developed for chronic hepatitis C alcoholic cirrhosis and non-alcoholic steatohepatitis NASH including clinical and biological parameters but no analysis of the liver parenchyma which is the physiopathological substrate of hepatocarcinogenesis
The advent of new artificial intelligence techniques could revolutionize the approach and lead to a personalised radiological screening strategy
Deep learning a subclass of machine learning is a popular area of research that can help humans performing certain tasks by automatically identifying new image features not defined by humans
The hypothesis of this study is that the non-tumor cirrhotic liver parenchyma is rich in structural information reflecting the severity of the hepatopathy its carcinological risk and the process of hepatocarcinogenesis Its analysis combined with clinical and biological data which have already been studied to stratify the risk of hepatocarcinogenesis will allow to define a very high-risk population particularly in the context of Hepatitis C Virus HCV eradication and Hepatitis B Virus HBV control
Consequently this study proposes to design prospectively a deep learning model for stratification of the risk of hepatocarcinogenesis by including clinical biological and radiological ultrasound parameters
Detailed Description:
By 2030 hepatocellular carcinoma HCC will become the second leading cause of cancer-related death accounting for more than one million deaths per year according to the World Health Organization
To this date screening for hepatocellular carcinoma in France remains uniform for all patients based solely on a liver ultrasound every 6 months This scheme has the advantage of associating an acceptable cost-effectiveness ratio and above all of obtaining an increased overall survival However this strategy has three main limitations lack of personalisation low compliance relatively poor performance of the ultrasound
Risk stratification models have been developed for chronic hepatitis C alcoholic cirrhosis and non-alcoholic steatohepatitis NASH including clinical age sex body mass index and diabetes and biological ASATALAT platelets albumin parameters However they didnt include analysis of the liver parenchyma which is the physiopathological substrate of hepatocarcinogenesis In the 1990s several authors studied the incidence of hepatocellular carcinoma according to the liver echostructure They agreed on the over-risk represented by a nodular heterogeneous echostructure with an estimated rate ratio of up to 20
However all these results have not yet led to a personalised radiological screening strategy The advent of new artificial intelligence techniques could revolutionize the approach
Deep learning a subclass of machine learning is a popular area of research that can help humans performing certain tasks Unlike radiomics deep learning can automatically identify new image features not defined by humans
The hypothesis of this study is that the non-tumor cirrhotic liver parenchyma is rich in structural information reflecting the severity of the hepatopathy its carcinological risk and the process of hepatocarcinogenesis Its analysis combined with clinical and biological data which have already been studied to stratify the risk of hepatocarcinogenesis will allow to define a very high-risk population particularly in the context of Hepatitis C Virus HCV eradication and Hepatitis B Virus HBV control
Consequently this study proposes to design prospectively a deep learning model for stratification of the risk of hepatocarcinogenesis by including clinical biological and radiological ultrasound parameters The primary objective of the study is to identify a population at very high risk of developing hepatocarcinoma in order to propose different screening modalities to the patients most at risk
This clinical study will include patients aged over 18 years referred by their hepatologist in the framework of ultrasound screening according to the European Association for the Study of the Liver EASL recommendations for hepatocellular carcinoma screening except for non-cirrhotic HBV liver disease non-cirrhotic F3-stage liver disease from any cause based on individual risk assessment for hepatocarcinoma cirrhosis from any cause non-viral or virologically cured HCV or controlled HBV Patients with a history of treated hepatocellular carcinoma will be excluded
Two groups of patients will be constituted prospectively group 1 will include patients with a diagnosis of hepatocellular carcinoma greater than 1 cm reference diagnostic standards radiological or histological These patients will therefore correspond to a very high-risk Group 2 will include patients without hepatocellular carcinoma thus corresponding to a lower risk A 1 year-interval ultrasound will be performed in patients of group 2 to confirm the absence of new nodule in the year following inclusion The proportion of new hepatocellular carcinoma should not exceed 3
The data collected will be clinical biological elastographic and ultrasonic parameters
A Deep Learning model using a deep convolutional neural network architecture will be developed on Python using these data
On a total of 7 investigation sites 300 patients equitably distributed between the two groups will be included in the trainingvalidation cohort and 100 patients equitably distributed between the two groups in the test cohort These numbers are calculated from ultrasound studies reporting a rate ratio of HCC risk of up to 20 in case of macronodular ultrasound pattern and Deep Learning requirements large numbers needed
The trainingvalidation and test cohorts will be from external and independent centres
The diagnostic performance of the model will be estimated by Area Under the Curve AUC sensitivity specificity and F1-score 95 confidence intervals on the test cohort
To this date screening for hepatocellular carcinoma in France remains uniform for all patients based solely on a liver ultrasound every 6 months This scheme has the advantage of associating an acceptable cost-effectiveness ratio and above all of obtaining an increased overall survival However this strategy has three main limitations lack of personalisation low compliance relatively poor performance of the ultrasound
Risk stratification models have been developed for chronic hepatitis C alcoholic cirrhosis and non-alcoholic steatohepatitis NASH including clinical age sex body mass index and diabetes and biological ASATALAT platelets albumin parameters However they didnt include analysis of the liver parenchyma which is the physiopathological substrate of hepatocarcinogenesis In the 1990s several authors studied the incidence of hepatocellular carcinoma according to the liver echostructure They agreed on the over-risk represented by a nodular heterogeneous echostructure with an estimated rate ratio of up to 20
However all these results have not yet led to a personalised radiological screening strategy The advent of new artificial intelligence techniques could revolutionize the approach
Deep learning a subclass of machine learning is a popular area of research that can help humans performing certain tasks Unlike radiomics deep learning can automatically identify new image features not defined by humans
The hypothesis of this study is that the non-tumor cirrhotic liver parenchyma is rich in structural information reflecting the severity of the hepatopathy its carcinological risk and the process of hepatocarcinogenesis Its analysis combined with clinical and biological data which have already been studied to stratify the risk of hepatocarcinogenesis will allow to define a very high-risk population particularly in the context of Hepatitis C Virus HCV eradication and Hepatitis B Virus HBV control
Consequently this study proposes to design prospectively a deep learning model for stratification of the risk of hepatocarcinogenesis by including clinical biological and radiological ultrasound parameters The primary objective of the study is to identify a population at very high risk of developing hepatocarcinoma in order to propose different screening modalities to the patients most at risk
This clinical study will include patients aged over 18 years referred by their hepatologist in the framework of ultrasound screening according to the European Association for the Study of the Liver EASL recommendations for hepatocellular carcinoma screening except for non-cirrhotic HBV liver disease non-cirrhotic F3-stage liver disease from any cause based on individual risk assessment for hepatocarcinoma cirrhosis from any cause non-viral or virologically cured HCV or controlled HBV Patients with a history of treated hepatocellular carcinoma will be excluded
Two groups of patients will be constituted prospectively group 1 will include patients with a diagnosis of hepatocellular carcinoma greater than 1 cm reference diagnostic standards radiological or histological These patients will therefore correspond to a very high-risk Group 2 will include patients without hepatocellular carcinoma thus corresponding to a lower risk A 1 year-interval ultrasound will be performed in patients of group 2 to confirm the absence of new nodule in the year following inclusion The proportion of new hepatocellular carcinoma should not exceed 3
The data collected will be clinical biological elastographic and ultrasonic parameters
A Deep Learning model using a deep convolutional neural network architecture will be developed on Python using these data
On a total of 7 investigation sites 300 patients equitably distributed between the two groups will be included in the trainingvalidation cohort and 100 patients equitably distributed between the two groups in the test cohort These numbers are calculated from ultrasound studies reporting a rate ratio of HCC risk of up to 20 in case of macronodular ultrasound pattern and Deep Learning requirements large numbers needed
The trainingvalidation and test cohorts will be from external and independent centres
The diagnostic performance of the model will be estimated by Area Under the Curve AUC sensitivity specificity and F1-score 95 confidence intervals on the test cohort
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None