Deep Learning for Prostate Segmentation

NCT04191980 | Unknown

• 1 other identifier: GOPI-Segmentation_2019
• Study Type: observational
• Target: 62
• Locations: 1 country
• Sites: 1

Brief Summary

Because the diagnostic criteria for prostate cancer are different in the peripheral and the transition zone, prostate segmentation is needed for any computer-aided diagnosis system aimed at characterizing prostate lesions on magnetic resonance (MR) images. Manual segmentation is time consuming and may differ between radiologists with different expertise. We developed and trained a convolutional neural network algorithm for segmenting the whole prostate, the transition zone and the anterior fibromuscular stroma on T2-weighted images of 787 MRIs from an existing prospective radiological pathological correlation database containing prostate MRI of patients treated by prostatectomy between 2008 and 2014 (CLARA-P database). The purpose of this study is to validate this algorithm on an independent cohort of patients.

Conditions

Prostate Cancer

Keywords

prostate segmentation; MR images

Outcome Measures

Primary Outcomes (1)

• Mean Mesh Distance (Mean) between the contours of the whole prostate made by the algorithm and the two radiologists

  The Mean Mesh Distance corresponds to the Average Boundary Distance (ABD) for each point of the reference segmentation. The distance to the closest point of the compared segmentation is first computed. Then the average of all these distances is computed and gives the ABD. The Mean Mesh Distance between the contours of the whole prostate made by the algorithm and each radiologist will be used as primary outcome measure.

  Month 11

Study Arms (2)

Patients with a MRI on a 3 Tesla (T) unit

The total validation cohort is composed of axial T2-weighted images of the prostate obtained from 31 prostate MRIs on a 3T unit randomly chosen among the prostate MRIs performed at the Hospices Civils de Lyon in 20162015-2019

Other: Comparison of prostate multi-zone segmentation obtained with an automatic deep learning-based algorithm and two expert radiologists

Patients with a MRI on a 1.5 Tesla unit

The total validation cohort is composed of axial T2-weighted images of the prostate obtained from 31 prostate MRIs on a 1.5T unit randomly chosen among the prostate MRIs performed at the Hospices Civils de Lyon in 20162015-2019

Other: Comparison of prostate multi-zone segmentation obtained with an automatic deep learning-based algorithm and two expert radiologists

Interventions

Comparison of prostate multi-zone segmentation obtained with an automatic deep learning-based algorithm and two expert radiologists OTHER

The algorithm is used to perform a multizone segmentation of the prostate including delineation of : the whole prostate contours, the transition zone contours, the anterior fibromuscular stroma. The contours is independently corrected by 2 radiologists. The corrected contours of the different zones will be stored and for each zone 6 different metrics will be used to evaluate the difference between the initial and corrected contours: * Mean Mesh Distance: Average Boundary Distance (ABD) for each point of the reference segmentation. The distance to the closest point of the compared segmentation is first computed. Then the average of all these distances is computed and gives the ABD * General Hausdorff distance (HD) * 95% percentile (P) of the HD and the 95th (P) of the asymmetric HD distribution * 95% HD modified (HD95\_1): different approach by first computing the 95th (P) of the asymmetric HD then taking the maximum * Dice coefficient * Difference in volumes

Patients with a MRI on a 1.5 Tesla unit; Patients with a MRI on a 3 Tesla (T) unit

Eligibility Criteria

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

Study Population

Random selection in the Picture Archiving and Communication System (PACS) of the Hospices Civils de Lyon among examinations performed between 2016 and 2019

You may qualify if:

• Prostate MRI contained in the PACS of the Hospices Civils de Lyon
• Performed in 2016-2019

You may not qualify if:

• MRIs from patients who already had treatment for prostate cancer

Sponsors & Collaborators

• Hospices Civils de Lyon: lead

Study Sites (1)

Hôpital Edouard Herriot

Lyon, 69008, France

RECRUITING

MeSH Terms

Conditions

Prostatic Neoplasms

Condition Hierarchy (Ancestors)

Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Prostatic Diseases; Male Urogenital Diseases

Central Study Contacts

Olivier ROUVIERE, Pr

CONTACT

472 11 61 67; Olivier.rouviere@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: December 6, 2019
• First Posted: December 10, 2019
• Study Start: February 1, 2019
• Primary Completion: January 1, 2020
• Study Completion: June 1, 2020
• Last Updated: December 10, 2019

Record last verified: 2019-12

Locations

FR (1)