Deep Learning for Prostate Segmentation (GOPI-Segm)

Civil Hospices of Lyon

Status: Unknown

Conditions: Prostate Cancer

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

Study type: Observational

Funder types: Other

Identifiers

NCT04191980

GOPI-Segmentation_2019

Details and patient eligibility

About

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.

Enrollment: 62 estimated patients

Sex: Male

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

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

Exclusion criteria

• MRIs from patients who already had treatment for prostate cancer

Trial design

62 participants in 2 patient groups

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

Description:

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

Treatment:

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

Description:

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

Treatment:

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