Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
120 participants
OBSERVATIONAL
2019-04-01
2019-09-30
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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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).
Anonymized CT examinations
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)
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)
An initial training phase of the artificial intelligence network will be carried out :
\- Use of computer data to drive the artificial intelligence network.
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).
Anonymized CT examinations
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.
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
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
Interventions
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Anonymized CT examinations
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)
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)
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)
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)
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
Eligibility Criteria
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Inclusion Criteria
* 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
Exclusion Criteria
* Patients with bad quality of CT scan images
18 Years
ALL
No
Sponsors
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Hospices Civils de Lyon
OTHER
Responsible Party
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Locations
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Service de radiologie - Pavillon B - Cellule Recherche imagerie, Hôpital Edouard Herriot (HCL)
Lyon, , France
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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ASEPOL
Identifier Type: -
Identifier Source: org_study_id
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