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NCT Number: NCT07647445

Prostate MRI Analysis by Radiologists and Artificial Intelligence - Disease Identification and Guided Management

Prostate cancer is the most common male cancer in 112 countries and makes up 7% of global cancer cases, and is the second leading cause of cancer-related deaths in men.

Normally, men with suspected prostate cancer undergo a prostate MRI, and then a Radiologist would review this scan to identify any suspicious areas for cancer within the prostate. Prostate MRI interpretation, however, is an expert skill with a steep learning curve, and internationally, there is a growing shortage of Radiologists.

The PARADIGM trial aims to assess if AI can perform just as well as Radiologists in interpreting prostate MRI scans to identify prostate cancer. Enrolled participants will undergo a prostate MRI, which is the normal method used for investigating suspected prostate cancer. AI and a Radiologist will both interpret the MRI, without knowledge of each other's interpretation. Once both reports have been made, the Radiologist will be asked to produce a third, combined report.

If there is a suspicious area in the prostate identified either by AI or the Radiologist, targeted biopsies will be performed. If there are no suspicious areas on the MRI and if you are at low risk of harbouring cancer, which occurs in about 30% of men, then no biopsy will be taken at all.

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Key information

Conditions

Prostate CA Neoplasms

Age range

18 year and older

Sex eligibility

Male

Study type

Interventional

Phase

Not applicable

About this study

Aim: To assess whether artificial intelligence is non-inferior to radiologists in the diagnosis of clinically significant prostate cancer on MRI.

Objectives

Primary

  • To compare the proportion of men who have clinically significant prostate cancer detected on MRI using AI ± targeted biopsy with radiologists ± targeted biopsy.

Secondary

  • To compare the proportion of men who have clinically insignificant prostate cancer detected on MRI using AI ± targeted biopsy with radiologists ± targeted biopsy.
  • To compare the proportion of men with non-suspicious MRIs for AI vs radiologists.
  • To compare the proportion of men with indeterminately scored MRI as reported by AI vs radiologists.
  • To compare the diagnostic test performance of AI vs radiologist.
  • To compare the additive value of AI when used together with a radiologist interpretation (summative of all identified lesions) compared to a radiologist alone.
  • To compare the additive value of AI when used together with a radiologist interpretation (where the radiologist can interact with the AI system by accepting or rejecting AI-identified lesions) compared to a radiologist alone.
  • To determine the frequency of AI failures.
  • To compare treatment eligibility decisions between AI and Radiologist.
  • To compare the cost-effectiveness of unblinded AI interpreted by the radiologist compared to radiologist alone for prostate cancer detection, and AI alone vs. radiologist alone, and a 3-arm analysis considering all three.

Design:

Prospective, international, within-patient, multi-centre, level-1 evidence trial in participants referred to hospital with a clinical suspicion of prostate cancer.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Men at least 18 years of age referred with clinical suspicion of prostate cancer
  • Serum PSA ≤ 20 ng/mL
  • Fit to undergo all procedures listed in the protocol
  • Able to provide written informed consent

Exclusion criteria

  • Prior prostate biopsy
  • Prior prostate MRI on a previous encounter*
  • Prior treatment for prostate cancer
  • Contraindication to MRI (e.g. claustrophobia, pacemaker)
  • Metalwork that would give rise to artefact on MRI (e.g. hip prosthesis, pelvic/spinal metalwork)
  • Contraindication to prostate biopsy
  • Unfit to undergo any procedures listed in protocol
  • An MRI on a previous encounter means a previous prostate MRI which has been seen by a doctor and has been used to inform patient management at the time of the original MRI.

Treatment and study plan

AI (Lucida Pi) interpretation

Diagnostic Test

AI algorithm that will interpretate the prostate MRI

Radiologist interpretation

Diagnostic Test

Radiologist will interpret the prostate MRI (as per standard of care)

Primary outcomes

  1. Proportion of men with clinically significant cancer

    Time frame: When biopsy results available, at an expected average of 30 days post-biopsy

    Proportion of men with clinically significant cancer detected (any pattern 4 disease on any core (i.e. Gleason Grade ≥ 3+4/Gleason grade group ≥2).

Secondary outcomes

  1. Proportion of men with clinically insignificant cancer

    Time frame: When biopsy results available, at an expected average of 30 days post-biopsy

    Proportion of men with clinically insignificant cancer detected (Gleason grade 3+3/Gleason grade group 1).

  2. Proportion of men with non-suspicious MRIs

    Time frame: When MRI results available, at an expected average of 30 days post-MRI

    Proportion of men with non-suspicious MRIs for AI vs Radiologists

  3. Proportion of MRIs with indeterminate scores.

    Time frame: When MRI results available, at an expected average of 30 days post-MRI

    Proportion of men with indeterminately scored MRI as reported by AI vs radiologists

  4. Agreement between AI and Radiologist in score of suspicion

    Time frame: When MRI results available, at an expected average of 30 days post-MRI

    Compare the proportion of MRIs with concordant scores between AI and Radiologist in score of suspicion

  5. Diagnostic test performance characteristics (AI versus Radiologist)

    Time frame: When biopsy results available, at an expected average of 30 days post-biopsy

    Test performance characteristics for AI and Radiologists, including sensitivity, specificity, area under the receive operating characteristic curve, positive predictive value and negative predictive value.

  6. Diagnostic test performance characteristics (AI plus Radiologist)

    Time frame: When biopsy results available, at an expected average of 30 days post-biopsy

    Test performance characteristics of AI in combination with Radiologist (summative of all identified lesions) compared to a radiologist alone, including sensitivity, specificity, area under the receive operating characteristic curve, positive predictive value and negative predictive value.

  7. Diagnostic test performance characteristics (AI-assisted Radiologist)

    Time frame: When biopsy results available, at an expected average of 30 days post-biopsy

    Test performance characteristics of AI in combination with Radiologist (where the radiologist can interact with the AI system by accepting or rejecting AI-identified lesions) compared to a radiologist alone, including sensitivity, specificity, area under the receive operating characteristic curve, positive predictive value and negative predictive value.

  8. Significant cancer detected by peri-lesional biopsies

    Time frame: When biopsy results available, at an expected average of 30 days post-biopsy

    Proportion of patients with significant cancer detected taking into account peri-lesional biopsies of AI and Radiologist declared lesions.

  9. Significant cancer detected by systematic biopsies

    Time frame: When biopsy results available, at an expected average of 30 days post-biopsy

    Proportion of patients with significant cancer detected by systematic biopsies

  10. Frequency of AI failures

    Time frame: When MRI results available, at an expected average of 30 days post-MRI

    Proportion of patients where AI was unable to interpret the MRI scan

  11. Treatment eligibility decisions

    Time frame: When biopsy results available, at an expected average of 30 days post-biopsy

    Proportion of patients where treatment eligibility changed between AI and Radiologist

  12. Cost-efffectiveness

    Time frame: At an expected average of 30 days post-intervention

    Cost-effectiveness of unblinded AI interpreted by the radiologist compared to radiologist alone in detecting significant prostate cancer, and AI alone vs. radiologist alone, and a 3-arm analysis considering all three.

Study contacts

Contact information is provided by the study sponsor or research team.

Ng Alexander, MBBS BSc (Hons)

CONTACT

[email protected]

+44 0207 679 5057

PARADIGM Study Team

CONTACT

[email protected]

Sponsors and collaborators

Lead sponsor

University College, London

Other

Collaborators

  • Lucida Medical Ltd

Registry information

Official study title

A Study Assessing Whether Artificial Intelligence is Non-inferior to Radiologists in the Diagnosis of Clinically Significant Prostate Cancer.

Acronym: PARADIGM

Important dates

Study start
2026
Primary completion
2029
Study completion
2029
First posted
Jun 15, 2026
Registry last updated
Jun 30, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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