Usability and Clinical Effectiveness of an Interpretable Deep Learning Framework for Post-Hepatectomy Liver Failure Prediction
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ClinicalTrials.gov Identifier: NCT06031818 |
Recruitment Status :
Recruiting
First Posted : September 11, 2023
Last Update Posted : February 6, 2024
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Tracking Information | |||||
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First Submitted Date | September 4, 2023 | ||||
First Posted Date | September 11, 2023 | ||||
Last Update Posted Date | February 6, 2024 | ||||
Actual Study Start Date | December 10, 2023 | ||||
Estimated Primary Completion Date | February 28, 2024 (Final data collection date for primary outcome measure) | ||||
Current Primary Outcome Measures |
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.
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Original Primary Outcome Measures | Same as current | ||||
Change History | |||||
Current Secondary Outcome Measures |
Usability of the explanation framework [ Time Frame: From enrollment to the end of trial at 8 weeks ] 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.
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Original Secondary Outcome Measures |
Usability of the explanation framework [ Time Frame: From enrollment to the end of trial at 8 weeks ] The score of the Likert scale of a designed questionnaire is used to evaluate the usability of the framework
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Current Other Pre-specified Outcome Measures | Not Provided | ||||
Original Other Pre-specified Outcome Measures | Not Provided | ||||
Descriptive Information | |||||
Brief Title | Usability and Clinical Effectiveness of an Interpretable Deep Learning Framework for Post-Hepatectomy Liver Failure Prediction | ||||
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 | ||||
Brief 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 carcinoma (HCC). The main questions it aims to answer are:
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. |
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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:
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. |
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Study Type | Observational | ||||
Study Design | Observational Model: Cohort Time Perspective: Retrospective |
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Target Follow-Up Duration | Not Provided | ||||
Biospecimen | Not Provided | ||||
Sampling Method | Non-Probability Sample | ||||
Study Population | Patients who underwent curative liver resection for HCC. | ||||
Condition |
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Intervention |
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Study Groups/Cohorts | Patients with HCC
Patients who underwent curative liver resection for HCC in the First Affiliated Hospital of Sun Yat-Sen University in China.
Interventions:
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Publications * | Not Provided | ||||
* Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline. |
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Recruitment Information | |||||
Recruitment Status | Recruiting | ||||
Estimated Enrollment |
80 | ||||
Original Estimated Enrollment | Same as current | ||||
Estimated Study Completion Date | March 15, 2024 | ||||
Estimated Primary Completion Date | February 28, 2024 (Final data collection date for primary outcome measure) | ||||
Eligibility Criteria | Inclusion Criteria:
Exclusion Criteria:
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Sex/Gender |
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Ages | Child, Adult, Older Adult | ||||
Accepts Healthy Volunteers | No | ||||
Contacts |
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Listed Location Countries | China | ||||
Removed Location Countries | |||||
Administrative Information | |||||
NCT Number | NCT06031818 | ||||
Other Study ID Numbers | Interpretable DL 92059201 ( Other Grant/Funding Number: National Natural Science Foundation of China ) |
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Has Data Monitoring Committee | No | ||||
U.S. FDA-regulated Product |
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IPD Sharing Statement |
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Current Responsible Party | Maastricht University | ||||
Original Responsible Party | Same as current | ||||
Current Study Sponsor | Maastricht University | ||||
Original Study Sponsor | Same as current | ||||
Collaborators | First Affiliated Hospital, Sun Yat-Sen University | ||||
Investigators |
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PRS Account | Maastricht University | ||||
Verification Date | February 2024 |