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

NCT06116344 | Completed

• 1 other identifier: AI_Prostate_1_KNN
• Study Type: observational
• Target: 173
• Locations: 1 country
• Sites: 1

Brief Summary

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

Conditions

Prostate Cancer

Keywords

prostate cancer; artificial intelligence

Outcome Measures

Primary Outcomes (4)

• Normalized Quantitative Signal - Intensity - Measurements with Region of Interest drawn in specific T2-weighted axial MRI Images

  Regions of interest for quantitative signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Image analysis will be performed on a PACS workstation. Signal intensity will be measured and normalized, therefore no units needed.

  through study completion, an average of 3 years

• Quantitative Signal - Intensity - Measurements with Region of Interest in specific in Apparent diffusion coefficient (ADC) axial MRI Images

  Regions of interest for signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Signal intensity will be measured and normalized in mm2/s

  through study completion, an average of 3 years

• Quantitative Signal - Intensity - Measurements with Region of Interest in specific in high b-value (800, 1500, 4000) axial MRI Images

  Regions of interest for signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Signal intensity will be measured and normalized in mm2/s

  through study completion, an average of 3 years

• Signal - Intensity - Measurements with Region of Interest in specific dynamic contrast enhanced (DCE) MRI Images

  Regions of interest for signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Signal intensity will be measured and normalized. Image analysis will be performed on a PACS workstation. The original Time inteisity curves are transformed in relative enhancement curves. Thus, they are normalized with respect to first point in time and represent the percentage increase compared to the time before contrast arrival, no units needed.

  through study completion, an average of 3 years

Study Arms (2)

experimental

experimental: patients with a condition

control group

control group: patients without condition

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: male
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

the study population will be patients of a primary care clinic

You may qualify if:

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

You may not qualify if:

• \. Contraindications for MRI

Sponsors & Collaborators

• Paracelsus Medical University: lead

Study Sites (1)

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

Nuremberg, Germany

Location

Related Publications (3)

• 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.

  PMID: 26427566 RESULT

• 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.

  PMID: 30898406 RESULT

• 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.

  PMID: 33830009 RESULT

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

Study Officials

• Michael M. Lell, Prof. Dr. med.

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

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Dr. med. Panagiota Manava, MD, senior physician

Study Record Dates

• First Submitted: August 24, 2023
• First Posted: November 3, 2023
• Study Start: January 1, 2018
• Primary Completion: December 31, 2020
• Study Completion: August 24, 2023
• Last Updated: November 3, 2023

Record last verified: 2023-11

Data Sharing

• IPD Sharing: Will not share

Locations

DE (1)