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

Paracelsus Medical University (PMU)

Status: Completed

Conditions: Prostate Cancer

Study type: Observational

Funder types: Other

Identifiers

NCT06116344

AI_Prostate_1_KNN

Details and patient eligibility

About

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

Full 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.

Enrollment: 173 patients

Sex: Male

Ages: 18 to 90 years old

Volunteers: No Healthy Volunteers

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

Trial design

173 participants in 2 patient groups

experimental

Description:

experimental: patients with a condition

control group

Description:

control group: patients without condition