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

Sponsor
Paracelsus Medical University (Other)
Overall Status
Completed
CT.gov ID
NCT06116344
Collaborator
(none)
173
Enrollment
1
Location
67.7
Actual Duration (Months)
2.6
Patients Per Site Per Month

Study Details

Study Description

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.

Condition or Disease Intervention/Treatment Phase

    Detailed Description

    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.

    Study Design

    Study Type:
    Observational
    Actual Enrollment :
    173 participants
    Observational Model:
    Case-Control
    Time Perspective:
    Retrospective
    Official Title:
    Improving Prostate Lesion Classification and Diagnostic Accuracy Using Machine Learning: A Comprehensive Evaluation and Development of a PI-RADS 3 Classifier
    Actual Study Start Date :
    Jan 1, 2018
    Actual Primary Completion Date :
    Dec 31, 2020
    Actual Study Completion Date :
    Aug 24, 2023

    Arms and Interventions

    Arm Intervention/Treatment
    experimental

    experimental: patients with a condition
    control group

    control group: patients without condition

    Outcome Measures

    Primary Outcome Measures

    1. Normalized Quantitative Signal - Intensity - Measurements with Region of Interest drawn in specific T2-weighted axial MRI Images [through study completion, an average of 3 years]

      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.

    2. Quantitative Signal - Intensity - Measurements with Region of Interest in specific in Apparent diffusion coefficient (ADC) axial MRI Images [through study completion, an average of 3 years]

      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

    3. Quantitative Signal - Intensity - Measurements with Region of Interest in specific in high b-value (800, 1500, 4000) axial MRI Images [through study completion, an average of 3 years]

      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

    4. Signal - Intensity - Measurements with Region of Interest in specific dynamic contrast enhanced (DCE) MRI Images [through study completion, an average of 3 years]

      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.

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    18 Years to 90 Years
    Sexes Eligible for Study:
    Male
    Accepts Healthy Volunteers:
    No
    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

    Contacts and Locations

    Locations

    Site City State Country Postal Code
    1 Department of Radiology and Nuclear Medicine, Klinikum Nuernberg, Paracelsus Medical University, Germany Nuernberg Germany

    Sponsors and Collaborators

    • Paracelsus Medical University

    Investigators

    • Study Director: Michael M. Lell, Prof. Dr. med., Department of Radiology and Nuclear Medicine, Klinikum Nuernberg, Paracelsus Medical University, Germany

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    Dr. Panagiota Manava, Dr. med. Panagiota Manava, MD, senior physician, Paracelsus Medical University
    ClinicalTrials.gov Identifier:
    NCT06116344
    Other Study ID Numbers:
    • AI_Prostate_1_KNN
    First Posted:
    Nov 3, 2023
    Last Update Posted:
    Nov 3, 2023
    Last Verified:
    Nov 1, 2023
    Individual Participant Data (IPD) Sharing Statement:
    No
    Plan to Share IPD:
    No
    Studies a U.S. FDA-regulated Drug Product:
    No
    Studies a U.S. FDA-regulated Device Product:
    No
    Keywords provided by Dr. Panagiota Manava, Dr. med. Panagiota Manava, MD, senior physician, Paracelsus Medical University
    prostate cancer
    artificial intelligence
    Additional relevant MeSH terms:
    Prostatic Neoplasms

    Study Results

    No Results Posted as of Nov 3, 2023