An AI Platform Integrating Imaging Data and Models, Supporting Precision Care Through Prostate Cancer's Continuum

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

14000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-02-24

Study Completion Date

2025-03-31

Brief Summary

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In Europe, prostate cancer (PCa) is the second most frequent type of cancer in men and the third most lethal. Current clinical practices, often leading to overdiagnosis and overtreatment of indolent tumors, suffer from lack of precision calling for advanced AI models to go beyond SoA by deciphering non-intuitive, high-level medical image patterns and increase performance in discriminating indolent from aggressive disease, early predicting recurrence and detecting metastases or predicting effectiveness of therapies. To date efforts are fragmented, based on single-institution, size-limited and vendorspecific datasets while available PCa public datasets (e.g. US TCIA) are only few hundred cases making model generalizability impossible.

The ProCAncer-I project brings together 20 partners, including PCa centers of reference, world leaders in AI and innovative SMEs, with recognized expertise in their respective domains, with the objective to design, develop and sustain a cloud based, secure European Image Infrastructure with tools and services for data handling. The platform hosts the largest collection of PCa multi-parametric (mp)MRI, anonymized image data worldwide (\>17,000 cases), based on data donorship, in line with EU legislation (GDPR). Robust AI models are developed, based on novel ensemble learning methodologies, leading to vendor-specific and -neutral AI models for addressing 8 PCa clinical scenarios.

To accelerate clinical translation of PCa AI models, we focus on improving the trust of the solutions with respect to fairness, safety, explainability and reproducibility. Metrics to monitor model performance and a causal explainability functionality are developed to further increase clinical trust and inform on possible failures and errors. A roadmap for AI models certification is defined, interacting with regulatory authorities, thus contributing to a European regulatory roadmap for validating the effectiveness of AI-based models for clinical decision making.

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Detailed Description

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Conditions

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Prostate Cancer Prostate Cancer Metastatic Prostate Cancer Recurrent Prostate Cancer Aggressiveness

Study Design

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Observational Model Type

COHORT

Study Time Perspective

OTHER

Study Groups

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Retrospective (training model)

Magnetic Resonance Imaging

Intervention Type DIAGNOSTIC_TEST

Patients who underwent MRI with confirmed pathology data (either biopsy or prostatectomy)

Prospective (validation model)

Magnetic Resonance Imaging

Intervention Type DIAGNOSTIC_TEST

Patients who underwent MRI with confirmed pathology data (either biopsy or prostatectomy)

Interventions

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Magnetic Resonance Imaging

Patients who underwent MRI with confirmed pathology data (either biopsy or prostatectomy)

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

1. histological confirmed PCa or suspicion of PCa (abnormal PSA values and/or positive DRE);
2. magnetic resonance imaging examination, including at least a high-resolution axial T2-weighted imaging and axila diffusion-weighted imaging (dynamic contrast-enhanced imaging is recommended, but not mandatory);
3. age ≥ 18 years at the time of diagnosis
4. signed written informed consent form (only for prospective enrollement).

Minimum Eligible Age

18 Years

Maximum Eligible Age

85 Years

Eligible Sex

MALE

Accepts Healthy Volunteers

Yes