Improving Prostate Lesion Classification and Development of a PI-RADS 3 Classifier

NCT ID: NCT06116344

Last Updated: 2023-11-03

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

173 participants

Study Classification

OBSERVATIONAL

Study Start Date

2018-01-01

Study Completion Date

2023-08-24

Brief Summary

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The investigators propose an AI methodology combining machine learning, histological results and expert image interpretation for the development of a PI-RADS 3 classifier.

Detailed Description

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Prostate cancer is the most common carcinoma in male patients in Western industrialized countries. Multiparametric prostate MRI (mpMRI) can select patients who may be potential candidates for biopsy. In this study, the investigators present a comprehensive methodology that evaluates a multitude of AI algorithms and assesses their performance on a large and high-quality dataset, aiming to generate an efficient model and develop a PI-RADS 3 classifier. By combining the power of machine learning with the information provided by mpMRI, histopathological results as well as expert image interpretation, the investigators attempt to improve the diagnostic accuracy, which in the future my lead to more informed clinical decisions and reduce unnecessary biopsies.

Conditions

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Prostate Cancer

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

RETROSPECTIVE

Study Groups

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experimental

experimental: patients with a condition

No interventions assigned to this group

control group

control group: patients without condition

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

1. Only patients with a clinical indication for mp prostate MRI will be included in this prospective study.
2. No allergies to GBCA

Exclusion Criteria

1\. Contraindications for MRI
Minimum Eligible Age

18 Years

Maximum Eligible Age

90 Years

Eligible Sex

MALE

Accepts Healthy Volunteers

No

Sponsors

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Paracelsus Medical University

OTHER

Sponsor Role lead

Responsible Party

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Dr. Panagiota Manava

Dr. med. Panagiota Manava, MD, senior physician

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Michael M. Lell, Prof. Dr. med.

Role: STUDY_DIRECTOR

Department of Radiology and Nuclear Medicine, Klinikum Nuernberg, Paracelsus Medical University, Germany

Locations

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Department of Radiology and Nuclear Medicine, Klinikum Nuernberg, Paracelsus Medical University, Germany

Nuremberg, , Germany

Site Status

Countries

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Germany

References

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Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ, Margolis D, Schnall MD, Shtern F, Tempany CM, Thoeny HC, Verma S. PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2. Eur Urol. 2016 Jan;69(1):16-40. doi: 10.1016/j.eururo.2015.08.052. Epub 2015 Oct 1.

Reference Type RESULT
PMID: 26427566 (View on PubMed)

Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ, Tempany CM, Choyke PL, Cornud F, Margolis DJ, Thoeny HC, Verma S, Barentsz J, Weinreb JC. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. Eur Urol. 2019 Sep;76(3):340-351. doi: 10.1016/j.eururo.2019.02.033. Epub 2019 Mar 18.

Reference Type RESULT
PMID: 30898406 (View on PubMed)

Morash C. What do you do with PI-RADS-3? Can Urol Assoc J. 2021 Apr;15(4):122. doi: 10.5489/cuaj.7262. No abstract available.

Reference Type RESULT
PMID: 33830009 (View on PubMed)

Other Identifiers

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AI_Prostate_1_KNN

Identifier Type: -

Identifier Source: org_study_id

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