Automatic Segmentation of Polycystic Liver

NCT03960710 | Unknown

• 1 other identifier: ASEPOL
• Study Type: observational
• Target: 120
• Locations: 1 country
• Sites: 1

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.

Conditions

Polycystic Liver Disease; Polycystic Hepatorenal Disease; Liver Injury

Outcome Measures

Primary Outcomes (1)

• 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

  At 4 months after randomization

Study Arms (2)

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; 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).

Other: Anonymized CT examinations; Other: Validation (1); Other: Validation (2)

Interventions

Anonymized CT examinations OTHER

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.

Neuronal network Training group; Neuronal network Validation group

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

Neuronal network Training group

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.

Neuronal network Training group

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

Neuronal network Validation group

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

Neuronal network Validation group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with hepato-renal polycystosis, with or without surgery

You may qualify if:

• 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

You may not qualify if:

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

Sponsors & Collaborators

• Hospices Civils de Lyon: lead

Study Sites (1)

Service de radiologie - Pavillon B - Cellule Recherche imagerie, Hôpital Edouard Herriot (HCL)

Lyon, 69437, France

RECRUITING

MeSH Terms

Conditions

Polycystic liver disease

Central Study Contacts

Bénédicte CAYOT

CONTACT

472110400; benedicte.cayot@chu-lyon.fr

Pierre-Jean VALETTE, MD, Prof.

CONTACT

472117544; pierre-jean.valette@chu-lyon.fr

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 21, 2019
• First Posted: May 23, 2019
• Study Start: April 1, 2019
• Primary Completion: July 1, 2019
• Study Completion: September 1, 2019
• Last Updated: May 28, 2019

Record last verified: 2019-05

Locations

FR (1)