Clinical trial NCT06116344: Improving Prostate Lesion Classification and Development of a PI-RADS 3 Classifier
Official title: Improving Prostate Lesion Classification and Diagnostic Accuracy Using Machine Learning: A Comprehensive Evaluation and Development of a PI-RADS 3 Classifier
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Prostate Cancer
Keywords: prostate cancer
artificial intelligence
Lead sponsor: Paracelsus Medical University
Sponsor class: OTHER
First posted: 2023-11-03
Enrollment: 173
Sex: MALE
Minimum age (years): 18
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: the study population will be patients of a primary care clinic

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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Normalized Quantitative Signal - Intensity - Measurements with Region of Interest drawn in specific T2-weighted axial MRI Images
timeFrame: through study completion, an average of 3 years
measure: Quantitative Signal - Intensity - Measurements with Region of Interest in specific in Apparent diffusion coefficient (ADC) axial MRI Images
timeFrame: through study completion, an average of 3 years
measure: Quantitative Signal - Intensity - Measurements with Region of Interest in specific in high b-value (800, 1500, 4000) axial MRI Images
timeFrame: through study completion, an average of 3 years
measure: Signal - Intensity - Measurements with Region of Interest in specific dynamic contrast enhanced (DCE) MRI Images
timeFrame: through study completion, an average of 3 years
Secondary outcomes:
Other outcomes:
Locations:
city: Nuernberg
country: Germany
facility: Department of Radiology and Nuclear Medicine, Klinikum Nuernberg, Paracelsus Medical University, Germany
Study officials:
affiliation: Department of Radiology and Nuclear Medicine, Klinikum Nuernberg, Paracelsus Medical University, Germany
name: Michael M. Lell, Prof. Dr. med.
role: STUDY_DIRECTOR
References:
citation: 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.
citation: 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.
citation: 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.