Polycystic Liver DiseaseASEPOLNCT03960710
Automatic Segmentation of Polycystic Liver
An observational study of Polycystic Liver Disease and Polycystic Hepatorenal Disease, run by Hospices Civils de Lyon at 1 site in France.
Trial summary
Assessing the volume of the liver before surgery, predicting the volume of liver remaining after surgery, detecting primary or secondary lesions in the liver parenchyma are common applications that require optimal detection of liver contours, and therefore liver segmentation. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Polycystic Liver Disease, Polycystic Hepatorenal Disease, Liver Injury
- What is being tested?
- Anonymized CT examinations, Training (1) and Training (2)
- Type of treatment
- Injection or infusion
- How many people
- 120 participants
- Where
- 1 siteFrance
- Study dates
- 1 Apr 2019to Sep 2019
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with hepato-renal polycystosis, with or without surgery
Who may be able to join
- Patients ≥ 18 years old
- Patients with hepato-renal polycystosis, with or without surgery
- Patients with at least one abdominal-pelvic CT scan without injection or with injection between January 1, 2016 and August 2018
- Patients with good quality and available images
Who cannot join
- Patients with no CT scan images available
- Patients with bad quality of CT scan images
Trial locations and contacts
1 site in 1 country.
France1 site, 1 recruiting
- Recruiting
Service de radiologie - Pavillon B - Cellule Recherche imagerie, Hôpital Edouard Herriot (HCL)
Lyon, 69437
Bénédicte CAYOT, 472110400 ext. +33, benedicte.cayot@chu-lyon.fr
Pierre-Jean VALETTE, MD, Prof., 472117544 ext. +33, pierre-jean.valette@chu-lyon.fr
About this study
Official title: Automatic Segmentation by a Convolutional Neural Network (Artificial Intelligence - Deep Learning) of Polycystic Livers, as a Model of Multi-lesional Dysmorphic Livers
Assessing the volume of the liver before surgery, predicting the volume of liver remaining after surgery, detecting primary or secondary lesions in the liver parenchyma are common applications that require optimal detection of liver contours, and therefore liver segmentation.
Several manual and laborious, semi-automatic and even automatic techniques exist.
Read more
However, severe pathology deforming the contours of the liver (multi-metastatic livers...), the hepatic environment of similar density to the liver or lesions, the CT examination technique are all variables that make it difficult to detect the contours. Current techniques, even automatic ones, are limited in this type of case (not rare) and most often require readjustments that make automatisation lose its value.
All these criteria of segmentation difficulties are gathered in the livers of hepatorenal polycystosis, which therefore constitute an adapted study model for the development of an automatic segmentation tool.
To obtain an automatic segmentation of any lesional liver, by exceeding the criteria of difficulty considered, investigators have developed a convolutional neural network (artificial intelligence - deep learning) useful for clinical practice.
- Being tested
- Anonymized CT examinations, Training (1), Training (2), Validation (1), Validation (2)
- Medical subject headings
- Polycystic liver disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Anonymized CT examinationsOther
The anonymized CT examinations will be reviewed in Lyon, in the imaging department of Edouard Herriot Hospital, by an expert radiologist and an intern from the Lyon hospitals.
Training (1)Other
An initial training phase of the artificial intelligence network will be carried out :
- Segmentation of the livers of a first part of the CT examination, by an intern of the Lyon hospitals
Training (2)Other
An initial training phase of the artificial intelligence network will be carried out :
- Use of computer data to drive the artificial intelligence network.
Validation (1)Other
A validation phase of the artificial intelligence tool will be carried out with segmentation of the livers of the second part of the CT examinations :
- Carried out by an intern at the Lyon hospitals
Validation (2)Other
A validation phase of the artificial intelligence tool will be carried out with segmentation of the livers of the second part of the CT examinations :
- Carried out by the neural network
| Study group | Role | What they receive |
|---|---|---|
| Neuronal network Training group | The following radiological variables, related to each CT examinations, will be collected for each patient:
* Injection modalities (without injection, injected)
* Major hepatectomy surgery
* Importance of hepatic dysmorphia
* Presence of intraperitoneal fluid effusion
* Presence of renal polycystosis (especially on the right side). Receives: Other: Anonymized CT examinations; Other: Training (1); Other: Training (2) | |
| Neuronal network Validation group | The following radiological variables, related to each CT examinations, will be collected for each patient:
* Injection modalities (without injection, injected)
* Major hepatectomy surgery
* Importance of hepatic dysmorphia
* Presence of intraperitoneal fluid effusion
* Presence of renal polycystosis (especially on the right side). Receives: Other: Anonymized CT examinations; Other: Validation (1); Other: Validation (2) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Test of automatic segmentation by the convolutional neural network on these group and collection of data set
Development of an automatic segmentation tool for highly dysmorphic polycystic livers as a prerequisite for segmentation of any type of multi-lesional livers that are difficult to segment, in order to facilitate lesion detection and volume measurement in clinical practice.
Randomisation of the patient into two data groups, one for training the other for Validating the convolutional neural network (artificial intelligence)
- Manual segmentation of polycystic livers of the 1st training group and deep learning of convolutional neural network
- Manual segmentation of polycystic livers of 2nd validation group
- Test of automatic segmentation by the convolutional neural network on these group and collection of data set
Measured: At 4 months after randomization
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 May 2019
- Study start1 Apr 2019
- Primary completionJul 2019 (estimated)
- Study completionSep 2019 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Polycystic Liver DiseaseStudies of the same condition, open and under way.
- More trials run by Hospices Civils de LyonThe sponsor's other registered studies.
- 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
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hospices Civils de Lyon
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03960710
- Sponsor's protocol ID
- ASEPOL
- Acronym
- ASEPOL
- Status last verified
- May 2019
- First submitted
- 21 May 2019
- Last updated
- 28 May 2019
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 May 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Automatic Segmentation by a Convolutional Neural Network (Artificial Intelligence - Deep Learning) of Polycystic Livers, as a Model of Multi-lesional Dysmorphic Livers
- ClinicalTrials.gov ID
- NCT03960710
- Acronym / sponsor ID
- ASEPOL / ASEPOL
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- Hospices Civils de LyonUniversity, hospital or other
- Study start
- 1 Apr 2019 (actual)
- Primary completion
- Jul 2019 (estimated)
- Study completion
- Sep 2019 (estimated)
- First posted
- 23 May 2019 (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.
Assessing the volume of the liver before surgery, predicting the volume of liver remaining after surgery, detecting primary or secondary lesions in the liver parenchyma are common applications that require optimal detection of liver contours, and therefore liver segmentation.
Several manual and laborious, semi-automatic and even automatic techniques exist.
However, severe pathology deforming the contours of the liver (multi-metastatic livers...), the hepatic environment of similar density to the liver or lesions, the CT examination technique are all variables that make it difficult to detect the contours. Current techniques, even automatic ones, are limited in this type of case (not rare) and most often require readjustments that make automatisation lose its value.
All these criteria of segmentation difficulties are gathered in the livers of hepatorenal polycystosis, which therefore constitute an adapted study model for the development of an automatic segmentation tool.
To obtain an automatic segmentation of any lesional liver, by exceeding the criteria of difficulty considered, investigators have developed a convolutional neural network (artificial intelligence - deep learning) useful for clinical practice.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Neuronal network Training group | – | Other: Anonymized CT examinations, Other: Training (1), Other: Training (2)The following radiological variables, related to each CT examinations, will be collected for each patient: * Injection modalities (without injection, injected) * Major hepatectomy surgery * Importance of hepatic dysmorphia * Presence of intraperitoneal fluid effusion * Presence of renal polycystosis (especially on the right side). |
| Neuronal network Validation group | – | Other: Anonymized CT examinations, Other: Validation (1), Other: Validation (2)The following radiological variables, related to each CT examinations, will be collected for each patient: * Injection modalities (without injection, injected) * Major hepatectomy surgery * Importance of hepatic dysmorphia * Presence of intraperitoneal fluid effusion * Presence of renal polycystosis (especially on the right side). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with hepato-renal polycystosis, with or without surgery
Key inclusion criteria (4 listed)
- Patients ≥ 18 years old
- Patients with hepato-renal polycystosis, with or without surgery
- Patients with at least one abdominal-pelvic CT scan without injection or with injection between January 1, 2016 and August 2018
- Patients with good quality and available images
Key exclusion criteria (2 listed)
- Patients with no CT scan images available
- Patients with bad quality of CT scan images
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 |
|---|---|---|
| Anonymized CT examinationsThe anonymized CT examinations will be reviewed in Lyon, in the imaging department of Edouard Herriot Hospital, by an expert radiologist and an intern from the Lyon hospitals. | Other | Neuronal network Training group, Neuronal network Validation group |
| Training (1)An initial training phase of the artificial intelligence network will be carried out : - Segmentation of the livers of a first part of the CT examination, by an intern of the Lyon hospitals | Other | Neuronal network Training group |
| Training (2)An initial training phase of the artificial intelligence network will be carried out : - Use of computer data to drive the artificial intelligence network. | Other | Neuronal network Training group |
| Validation (1)A validation phase of the artificial intelligence tool will be carried out with segmentation of the livers of the second part of the CT examinations : - Carried out by an intern at the Lyon hospitals | Other | Neuronal network Validation group |
| Validation (2)A validation phase of the artificial intelligence tool will be carried out with segmentation of the livers of the second part of the CT examinations : - Carried out by the neural network | Other | Neuronal network Validation group |
Primary outcome
Test of automatic segmentation by the convolutional neural network on these group and collection of data set
Time frame: At 4 months after randomization
Development of an automatic segmentation tool for highly dysmorphic polycystic livers as a prerequisite for segmentation of any type of multi-lesional livers that are difficult to segment, in order to facilitate lesion detection and volume measurement in clinical practice. Randomisation of the patient into two data groups, one for training the other for Validating the convolutional neural network (artificial intelligence) * Manual segmentation of polycystic livers of the 1st training group and deep learning of convolutional neural network * Manual segmentation of polycystic livers of 2nd validation group * Test of automatic segmentation by the convolutional neural network on these group and collection of data set
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 May 2019.
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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.
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 Polycystic Liver Disease and Polycystic Hepatorenal Disease, run by Hospices Civils de Lyon at 1 site in France.
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 4 inclusion and 2 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 28 May 2019. 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 France. 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 Bénédicte CAYOT (472110400 ext. +33, benedicte.cayot@chu-lyon.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Test of automatic segmentation by the convolutional neural network on these group and collection of data set (time frame: At 4 months after randomization). 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 28 May 2019.