Validation of a Machine Learning Model Based on MR for the Prediction of Prostate Cancer

NCT06773598 | Recruiting

• 1 other identifier: PROSTATE_02
• Study Type: observational
• Target: 1,100
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this observational study is to validate a clinically significant predictive machine learning model based on the processing of images RMmp (Multiparametric Magnetic Resonance Imaging). To be validated the model should be evaluated on:

• Specificity (SP): is the probability of a negative test result, conditioned on the individual truly being negative
• Sensitivity (SN): is the probability of a positive test result, conditioned on the individual truly being positive

Conditions

Prostate Cancer

Keywords

Radiomics; mpMRI

Outcome Measures

Primary Outcomes (4)

• Specificity (SP)

  Specificity is the probability of obtaining a negative classification or that the disease is indeed absent.

  From enrollment to the end of treatment at 5 years

• Sensitivity (SN)

  Sensitivity is the probability of a positive classification or that the disease is actually present.

  From enrollment to the end of treatment at 5 years

• Positive Predictive Value (PPV)

  It is the ratio of patients truly diagnosed as positive to all those who had positive test results (including healthy subjects who were incorrectly diagnosed as patient).

  From enrollment to the end of treatment at 5 years

• Negative Predictive Value (NPV)

  It is the ratio of subjects truly diagnosed as negative to all those who had negative test results (including patients who were incorrectly diagnosed as healthy).

  From enrollment to the end of treatment at 5 years

Eligibility Criteria

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

Study Population

The study will include participants who have performed an MMR at the U.O.C. Radiology Addomino-Pelvic Diagnostic and Interventional of the IRCCS University Hospital of Bologna, Sant'Orsola Polyclinic and a TRUS biopsy with fusion technique with RMmp of the prostate. The retrospective enrollment refers to the period between 15 September 2015 (date start of TRUS biopsy with fusion technique with RMmp at the Policlinico di Sant'Orsola) and the date of approval of this study. The prospective enrollment refers to the period of time from the study approval until the following 3 years.

You may qualify if:

• Participants aged 18 at the time of examination
• Obtaining informed consent
• Presence of one or more lesions classified as PI-RADSv2.1 ≥ 1 at a prostate RMmp at the IRCCS Azienda Ospedaliero-Universitaria in Bologna
• Indication for TRUS biopsy by fusion technique integrated with systematic biopsy at the IRCCS Azienda Ospedaliero-Universitaria in Bologna

You may not qualify if:

• Previous prostate surgery or hormone therapy
• Technically sub-optimal investigations for the presence of artifacts (hip prosthesis, movement of the endorectal probe, etc.)

Sponsors & Collaborators

• IRCCS Azienda Ospedaliero-Universitaria di Bologna: lead

Study Sites (1)

IRCCS Azienda Ospedaliero-Universitaria di Bologna

Bologna, 40138, Italy

RECRUITING

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

• Caterina Gaudiano, MD

  IRCCS Azienda Ospedaliero-Universitaria di Bologna

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Caterina Gaudiano, MD

CONTACT

+39 0515142307; caterina.gaudiano@aosp.bo.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 9, 2025
• First Posted: January 14, 2025
• Study Start: May 21, 2024
• Primary Completion (Estimated): October 1, 2028
• Study Completion (Estimated): November 1, 2028
• Last Updated: January 14, 2025

Record last verified: 2024-11

Locations

IT (1)