Deep Learning for Prostate Segmentation

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

• Registration Number: NCT04191980
• Lead Sponsor: Hospices Civils de Lyon

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2019-02-01
• Status Verified: 2019-12
• Study First Submitted: 2019-12-06
• Study First Submitted QC: 2019-12-06
• Last Update Submitted: 2019-12-06
• Study First Posted: 2019-12-10
• Last Update Posted: 2019-12-10
• Primary Completion: 2020-01
• Study Completion: 2020-06

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: Male
• Target Recruitment: 62

Inclusion Criteria

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

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Exclusion Criteria

• MRIs from patients who already had treatment for prostate cancer

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Patients with a MRI on a 3 Tesla (T) unit	Comparison of prostate multi-zone segmentation obtained with an automatic deep learning-based algorithm and two expert radiologists	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
Patients with a MRI on a 1.5 Tesla unit	Comparison of prostate multi-zone segmentation obtained with an automatic deep learning-based algorithm and two expert radiologists	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

Primary Outcome Measures

Name	Time	Method
Mean Mesh Distance (Mean) between the contours of the whole prostate made by the algorithm and the two radiologists	Month 11	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.

Trial Locations

Locations (1)

Hôpital Edouard Herriot; 🇫🇷 Lyon, France