Automatic Segmentation of Polycystic Liver

• Conditions: Polycystic Hepatorenal Disease; Polycystic Liver Disease; Liver Injury
• Interventions: Other: Anonymized CT examinations; Other: Training (1); Other: Validation (1); Other: Training (2); Other: Validation (2)

• Registration Number: NCT03960710
• Lead Sponsor: 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.

Detailed Description

Not available

Study Timeline

• Study Start: 2019-04-01
• Status Verified: 2019-05
• Study First Submitted: 2019-05-21
• Study First Submitted QC: 2019-05-21
• Study First Posted: 2019-05-23
• Last Update Submitted: 2019-05-23
• Last Update Posted: 2019-05-28
• Primary Completion: 2019-07
• Study Completion: 2019-09

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 120

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

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Exclusion Criteria

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

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Neuronal network Training group	Training (1)	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 Training group	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	Anonymized CT examinations	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	Validation (1)	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 Training group	Anonymized CT examinations	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	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).

Primary Outcome Measures

Name	Time	Method
Test of automatic segmentation by the convolutional neural network on these group and collection of data set	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

Trial Locations

Locations (1)

Service de radiologie - Pavillon B - Cellule Recherche imagerie, Hôpital Edouard Herriot (HCL); 🇫🇷 Lyon, France