Post-hepatectomy Liver FailureNCT06031818
Usability and Clinical Effectiveness of an Interpretable Deep Learning Framework for Post-Hepatectomy Liver Failure Prediction
An observational study of Post-hepatectomy Liver Failure and Hepatocellular Carcinoma, run by Maastricht University at 1 site in China.
Trial summary
The goal of this in-silico clinical trial is to learn about the usability and clinical effectiveness of an interpretable deep learning framework (VAE-MLP) using counterfactual explanations and layerwise relevance propagation for prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Post-hepatectomy Liver Failure, Hepatocellular Carcinoma, Artificial Intelligence
- What is being tested?
- The explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation, The model prediction and The model prediction and the explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation
- How many people
- 80 participants
- Where
- 1 siteChina
- Study dates
- 10 Dec 2023to 15 Mar 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients who underwent curative liver resection for HCC.
Who may be able to join
- patients with treatment-naive and resectable HCC;
- performance status Eastern Cooperative Oncology Group (PS) score 0-1.
Who cannot join
- liver resection was not performed;
- pathological diagnosis of non-HCC;
- failure in liver stiffness measurement defined as the elastography color map was less than 75% filled or interquartile range (IQR)/median > 30%;
- immune-active chronic hepatitis indicated by an elevation of alanine aminotransferase (ALT) levels ≥ 2×upper limit of normal (ULN);
- obstructive jaundice or dilated intrahepatic bile ducts with a diameter of >3 mm;
- hypoalbuminemia, hyperbilirubinemia, or coagulopathy not related to the liver.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
The First Affiliated Hospital of Sun Yat-Sen University
Guangzhou, Guangdong, 510000
Xian Zhong, 86-13632460144, x.zhong@maastrichtuniversity.nl
About this study
Official title: Usability and Clinical Effectiveness of an Interpretable Deep Learning Framework (VAE-MILP) Using Counterfactual Explanations and Layerwise Relevance Propagation Framework for Post-Hepatectomy Liver Failure Prediction
The goal of this in-silico clinical trial is to learn about the usability and clinical effectiveness of an interpretable deep learning framework (VAE-MLP) using counterfactual explanations and layerwise relevance propagation for prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC). The main questions it aims to answer are:
- To investigate the usability of the VAE-MLP framework for explanation of the deep learning model.
- To investigate the clinical effectiveness of VAE-MLP framework for prediction of post-hepatectomy liver failure in patients with hepatocellular carcinoma.
Read more
In the usability trial the clinicians and radiologists will be shown the counterfactual explanations and layerwise relevance propagation (LRP) plots to evaluate the usability of the framework.
In the clinical trial the clinicians and radiologists will make the prediction under two different conditions: with model explanation and without model explanation with a washout period of at least 14 days to evaluate the clinical effectiveness of the explanation framework.
Read the detailed description
Post-hepatectomy liver failure (PHLF) is a severe complication after liver resection. It is important to develop an interpretable model for predicting PHLF in order to facilitate effective collaboration with clinicians for decision-making. Two-dimensional shear wave elastography (2D-SWE) is a liver stiffness measurement (LSM) technology that was proven to be useful in liver fibrosis staging. Therefore 2D-SWE shows the potential value for liver function assessment and PHLF prediction. 2D-SWE images display color-coded tissue stiffness map of liver parenchyma, with red representing a solid tissue (higher stiffness) and blue representing a soft tissue (lower stiffness). Routine analysis of 2D-SWE fails to fully utilize all information available in the images and also suffers from inter-observer variance in choosing the optimal quantification region.
Deep learning (DL) has demonstrated state-of-the-art performance on many medical imaging tasks such as classification or segmentation. However, despite significant progress in DL, the clinical translation of DL tools has so far been limited, partially due to a lack of interpretability of models, the so-called "black box" problem. Interpretability of DL systems is important for fostering clinical trust as well as timely correcting any faulty processes in the algorithms.
Here, the investigators present a novel interpretable DL framework (VAE-MLP) which incorporates counterfactual analysis for the explanation of 2D medical images and LRP for the explanation of feature attributions of both medical images and clinical variables.
The goal of this in-silico clinical trial is to learn about the usability and clinical effectiveness of an interpretable deep learning framework (VAE-MLP) using counterfactual explanations and layerwise relevance propagation for prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma. The main questions it aims to answer are:
- To investigate the usability of the the interpretable deep learning framework (VAE-MLP) for explanation of the deep learning model.
- To investigate the clinical effectiveness of the interpretable deep learning framework (VAE-MLP) for prediction of post-hepatectomy liver failure in patients with hepatocellular carcinoma.
In the usability trial the clinicians and radiologists will be shown the counterfactual explanations and layerwise relevance propagation plots of 6 examples. The score of the Likert scale of a designed questionnaire is used to evaluate the usability of the framework.
In the clinical trial the clinicians and radiologists will make the prediction under two different conditions: with model explanation and without model explanation with a washout period of at least 14 days. The accuracy, sensitivity and specificity is used to compare the clinical effectiveness of the explanation framework.
- Being tested
- The explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation, The model prediction, The model prediction and the explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation
- Keywords
- deep learning, interpretability, post-hepatectomy liver failure, hepatocellular carcinoma, shear wave elastography
- Medical subject headings
- Carcinoma, Hepatocellular
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
The explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagationOther
The radiologist and clinicians will be provided the model prediction results with the explanation of the model and they will fill in a questionnaire to evaluate the usability of the interpretable framework.
The model predictionOther
The radiologist and clinicians will be provided the model prediction results without the explanation of the model and they will be asked to give their own prediction.
The model prediction and the explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagationOther
The radiologist and clinicians will be provided the model prediction results with the explanation of the model and they will be asked to give their own prediction.
| Study group | Role | What they receive |
|---|---|---|
| Patients with HCC | Patients who underwent curative liver resection for HCC in the First Affiliated Hospital of Sun Yat-Sen University in China. Receives: Other: The explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation; Other: The model prediction; Other: The model prediction and the explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation |
What is being measured
The primary outcome is the main question the study is designed to answer.
Clinical effectiveness of the explanation framework
The accuracy, sensitivity and specificity will be compared between the prediction made with and without the explanation of the DL model to determine the clinical effectiveness of the explanation framework.
Measured: From enrollment to the end of trial at 8 weeks
Secondary outcomes (1)
Usability of the explanation framework
The score of the Likert scale of a designed questionnaire is used to evaluate the usability of the framework. Each item is given a score from 1 to 5. Higher scores mean a better outcome.
Measured: From enrollment to the end of trial at 8 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Sep 2023
- Study start10 Dec 2023
- Primary completion28 Feb 2024 (estimated)
- Study completion15 Mar 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Post-hepatectomy Liver FailureStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 80 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Maastricht University
- Sponsor type
- University, hospital or other
- Collaborators
- First Affiliated Hospital, Sun Yat-Sen University
- Responsible party
- Sponsor
- Investigators
- Philippe Lambin, Principal investigator, Maastricht University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06031818
- Sponsor's protocol ID
- Interpretable DL
- Other IDs
- 92059201
- Status last verified
- Feb 2024
- First submitted
- 4 Sep 2023
- Last updated
- 6 Feb 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: Individual participant data (IPD) can be shared if asked
Study at a glance
From the registry record, last updated by the sponsor on 6 Feb 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Usability and Clinical Effectiveness of an Interpretable Deep Learning Framework (VAE-MILP) Using Counterfactual Explanations and Layerwise Relevance Propagation Framework for Post-Hepatectomy Liver Failure Prediction
- ClinicalTrials.gov ID
- NCT06031818
- Acronym / sponsor ID
- Interpretable DL
- Keywords
- deep learning, interpretability, post-hepatectomy liver failure, hepatocellular carcinoma, shear wave elastography
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 80 participants (estimated)
- Lead sponsor
- Maastricht UniversityUniversity, hospital or other
- Collaborators
- First Affiliated Hospital, Sun Yat-Sen University
- Study start
- 10 Dec 2023 (actual)
- Primary completion
- 28 Feb 2024 (estimated)
- Study completion
- 15 Mar 2024 (estimated)
- First posted
- 11 Sep 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The goal of this in-silico clinical trial is to learn about the usability and clinical effectiveness of an interpretable deep learning framework (VAE-MLP) using counterfactual explanations and layerwise relevance propagation for prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC). The main questions it aims to answer are:
- To investigate the usability of the VAE-MLP framework for explanation of the deep learning model.
- To investigate the clinical effectiveness of VAE-MLP framework for prediction of post-hepatectomy liver failure in patients with hepatocellular carcinoma.
In the usability trial the clinicians and radiologists will be shown the counterfactual explanations and layerwise relevance propagation (LRP) plots to evaluate the usability of the framework.
In the clinical trial the clinicians and radiologists will make the prediction under two different conditions: with model explanation and without model explanation with a washout period of at least 14 days to evaluate the clinical effectiveness of the explanation framework.
Detailed description
Post-hepatectomy liver failure (PHLF) is a severe complication after liver resection. It is important to develop an interpretable model for predicting PHLF in order to facilitate effective collaboration with clinicians for decision-making. Two-dimensional shear wave elastography (2D-SWE) is a liver stiffness measurement (LSM) technology that was proven to be useful in liver fibrosis staging. Therefore 2D-SWE shows the potential value for liver function assessment and PHLF prediction. 2D-SWE images display color-coded tissue stiffness map of liver parenchyma, with red representing a solid tissue (higher stiffness) and blue representing a soft tissue (lower stiffness). Routine analysis of 2D-SWE fails to fully utilize all information available in the images and also suffers from inter-observer variance in choosing the optimal quantification region.
Deep learning (DL) has demonstrated state-of-the-art performance on many medical imaging tasks such as classification or segmentation. However, despite significant progress in DL, the clinical translation of DL tools has so far been limited, partially due to a lack of interpretability of models, the so-called "black box" problem. Interpretability of DL systems is important for fostering clinical trust as well as timely correcting any faulty processes in the algorithms.
Here, the investigators present a novel interpretable DL framework (VAE-MLP) which incorporates counterfactual analysis for the explanation of 2D medical images and LRP for the explanation of feature attributions of both medical images and clinical variables.
The goal of this in-silico clinical trial is to learn about the usability and clinical effectiveness of an interpretable deep learning framework (VAE-MLP) using counterfactual explanations and layerwise relevance propagation for prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma. The main questions it aims to answer are:
- To investigate the usability of the the interpretable deep learning framework (VAE-MLP) for explanation of the deep learning model.
- To investigate the clinical effectiveness of the interpretable deep learning framework (VAE-MLP) for prediction of post-hepatectomy liver failure in patients with hepatocellular carcinoma.
In the usability trial the clinicians and radiologists will be shown the counterfactual explanations and layerwise relevance propagation plots of 6 examples. The score of the Likert scale of a designed questionnaire is used to evaluate the usability of the framework.
In the clinical trial the clinicians and radiologists will make the prediction under two different conditions: with model explanation and without model explanation with a washout period of at least 14 days. The accuracy, sensitivity and specificity is used to compare the clinical effectiveness of the explanation framework.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients with HCC | – | Other: The explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation, Other: The model prediction, Other: The model prediction and the explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagationPatients who underwent curative liver resection for HCC in the First Affiliated Hospital of Sun Yat-Sen University in China. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients who underwent curative liver resection for HCC.
Key inclusion criteria (2 listed)
- patients with treatment-naive and resectable HCC;
- performance status Eastern Cooperative Oncology Group (PS) score 0-1.
Key exclusion criteria (6 listed)
- liver resection was not performed;
- pathological diagnosis of non-HCC;
- failure in liver stiffness measurement defined as the elastography color map was less than 75% filled or interquartile range (IQR)/median > 30%;
- immune-active chronic hepatitis indicated by an elevation of alanine aminotransferase (ALT) levels ≥ 2×upper limit of normal (ULN);
- obstructive jaundice or dilated intrahepatic bile ducts with a diameter of >3 mm;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| The explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagationThe radiologist and clinicians will be provided the model prediction results with the explanation of the model and they will fill in a questionnaire to evaluate the usability of the interpretable framework. | Other | Patients with HCC |
| The model predictionThe radiologist and clinicians will be provided the model prediction results without the explanation of the model and they will be asked to give their own prediction. | Other | Patients with HCC |
| The model prediction and the explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagationThe radiologist and clinicians will be provided the model prediction results with the explanation of the model and they will be asked to give their own prediction. | Other | Patients with HCC |
Primary outcome
Clinical effectiveness of the explanation framework
Time frame: From enrollment to the end of trial at 8 weeks
The accuracy, sensitivity and specificity will be compared between the prediction made with and without the explanation of the DL model to determine the clinical effectiveness of the explanation framework.
1 secondary outcome
Usability of the explanation framework
Time frame: From enrollment to the end of trial at 8 weeks
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Feb 2024.
- Individual participant dataSharing planned: Undecided. Individual participant data (IPD) can be shared if asked
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Philippe Lambin principal investigator
Maastricht University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Post-hepatectomy Liver Failure and Hepatocellular Carcinoma, run by Maastricht University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 6 Feb 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Xian Zhong (86-13632460144, x.zhong@maastrichtuniversity.nl), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Clinical effectiveness of the explanation framework (time frame: From enrollment to the end of trial at 8 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Feb 2024.