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Automatic Segmentation of Polycystic Liver (ASEPOL)

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ClinicalTrials.gov Identifier: NCT03960710
Recruitment Status : Recruiting
First Posted : May 23, 2019
Last Update Posted : May 27, 2019
Sponsor:
Information provided by (Responsible Party):
Hospices Civils de Lyon

Brief 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.

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.


Condition or disease Intervention/treatment
Polycystic Liver Disease Polycystic Hepatorenal Disease Liver Injury Other: Anonymized CT examinations Other: Training (1) Other: Training (2) Other: Validation (1) Other: Validation (2)

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Study Type : Observational
Estimated Enrollment : 120 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Automatic Segmentation by a Convolutional Neural Network (Artificial Intelligence - Deep Learning) of Polycystic Livers, as a Model of Multi-lesional Dysmorphic Livers
Actual Study Start Date : April 1, 2019
Estimated Primary Completion Date : July 2019
Estimated Study Completion Date : September 2019

Resource links provided by the National Library of Medicine


Group/Cohort Intervention/treatment
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).
Other: 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.

Other: 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: 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).
Other: 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.

Other: 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: 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





Primary Outcome Measures :
  1. 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



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Patients with hepato-renal polycystosis, with or without surgery
Criteria

Inclusion Criteria:

  • 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

Exclusion Criteria:

  • Patients with no CT scan images available
  • Patients with bad quality of CT scan images

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03960710


Contacts
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Contact: Bénédicte CAYOT 472110400 ext +33 benedicte.cayot@chu-lyon.fr
Contact: Pierre-Jean VALETTE, MD, Prof. 472117544 ext +33 pierre-jean.valette@chu-lyon.fr

Locations
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France
Service de radiologie - Pavillon B - Cellule Recherche imagerie, Hôpital Edouard Herriot (HCL) Recruiting
Lyon, France, 69437
Contact: Bénédicte CAYOT    472110400 ext +33    benedicte.cayot@chu-lyon.fr   
Contact: Pierre-Jean VALETTE, MD, Prof.    472117544 ext +33    pierre-jean.valette@chu-lyon.fr   
Principal Investigator: Pierre-Jean VALETTE, MD, Prof.         
Sponsors and Collaborators
Hospices Civils de Lyon

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Responsible Party: Hospices Civils de Lyon
ClinicalTrials.gov Identifier: NCT03960710     History of Changes
Other Study ID Numbers: ASEPOL
First Posted: May 23, 2019    Key Record Dates
Last Update Posted: May 27, 2019
Last Verified: May 2019

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Additional relevant MeSH terms:
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Liver Diseases
Cysts
Digestive System Diseases
Neoplasms
Pathological Conditions, Anatomical