Clinical trial · Observational
Validation of a Machine Learning Model Based on MR for the Prediction of Prostate Cancer
Validation of a Machine Learning Model Based on Multiparametric MR for the Prediction of Clinically Significant Prostate Cancer
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Prostate Cancer | Malignant Prostate Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (4)
- measure
- Specificity (SP)
- timeFrame
- From enrollment to the end of treatment at 5 years
- description
- Specificity is the probability of obtaining a negative classification or that the disease is indeed absent.
- measure
- Sensitivity (SN)
- timeFrame
- From enrollment to the end of treatment at 5 years
- description
- Sensitivity is the probability of a positive classification or that the disease is actually present.
- measure
- Positive Predictive Value (PPV)
- timeFrame
- From enrollment to the end of treatment at 5 years
- description
- 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).
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * 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 Exclusion Criteria: * Previous prostate surgery or hormone therapy * Technically sub-optimal investigations for the presence of artifacts (hip prosthesis, movement of the endorectal probe, etc.)
References
Publications (0)
Data not yet available