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

NCT05384002 | Completed

• 1 other identifier: ProCAncer-I
• Study Type: observational
• Target: 14,000
• Locations: 1 country
• Sites: 1

Brief Summary

In Europe, prostate cancer (PCa) is the second most frequent type of cancer in men and the fifth 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 (\>14,000 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.

Conditions

Prostate Cancer Recurrent; Prostate Cancer Aggressiveness; Prostate Cancer; Prostate Cancer Metastatic

Keywords

prostate cancer; repository; open image space; annotated and under donorship images; quality of image providers; cloud infrastructure; tools to build AI applications; advanced AI techniques

Outcome Measures

Primary Outcomes (3)

• To create a repository (Prostate-NET) of retrospective MRI examinations with related clinical and pathology data dedicated to prostate cancer.

  24 months

• To use the retrospective data collection (Prostate-NET) to solve 9 different clinical scenarios to improve diagnosis, characterization, treatment and follow-up of men with prostate cancer.

  36 months

• To develop vendor-specific and vendor neutral AI models exploiting the prospective data that will be uploaded to the Prostate-NET platform.

  48 months

Study Arms (2)

Retrospective (training model)

Diagnostic Test: Magnetic Resonance Imaging

Prospective (validation model)

Diagnostic Test: Magnetic Resonance Imaging

Interventions

Magnetic Resonance Imaging DIAGNOSTIC_TEST

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

Prospective (validation model); Retrospective (training model)

Eligibility Criteria

• Age: 18 Years - 85 Years
• Sex: male
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients who underwent MRI examination with pathological confirmation of prostate cancer (positive group) or at least 1-year follow-up to exclude presence of prostate cancer (negative group).

You may qualify if:

• histological confirmed PCa or suspicion of PCa (abnormal PSA values and/or positive DRE);
• 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);
• age ≥ 18 years at the time of diagnosis
• signed written informed consent form (only for prospective enrollement).

Sponsors & Collaborators

• Fondazione del Piemonte per l'Oncologia: lead
• Fundacao Champalimaud: collaborator
• Stichting Katholieke Universiteit: collaborator
• Institut Paoli-Calmettes: collaborator
• JCC DIAGNOSTIC IMAGING: collaborator
• National Cancer Institute (NCI): collaborator
• Agios Savas: collaborator
• QS INSTITUTO DE INVESTIGACION E INNOVACION SL: collaborator
• THE GENERAL HOSPITAL CORPORATION: collaborator
• BIOTRONICS 3D LIMITED: collaborator
• Advantis Medical Imaging: collaborator
• QUIBIM SOCIEDAD LIMITADA: collaborator
• University of Vienna: collaborator
• University of Pisa: collaborator
• Hacettepe University: collaborator
• IDRYMA TECHNOLOGIAS KAI EREVNAS: collaborator
• Fundacion Para La Investigacion Hospital La Fe: collaborator
• Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta: collaborator
• Royal Marsden NHS Foundation Trust: collaborator
• Fondazione C.N.R./Regione Toscana "G. Monasterio", Pisa, Italy: collaborator

Study Sites (1)

Fondazione del Piemonte per l'Oncologia

Candiolo, Italy, 10060, Italy

Location

MeSH Terms

Conditions

Prostatic Neoplasms; Prostate Cancer, Hereditary, 7

Interventions

Magnetic Resonance Imaging

Condition Hierarchy (Ancestors)

Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Prostatic Diseases; Male Urogenital Diseases

Intervention Hierarchy (Ancestors)

Tomography; Diagnostic Imaging; Diagnostic Techniques and Procedures; Diagnosis

Study Officials

• Manolis Tsiknakis

  FORTH

  STUDY DIRECTOR

• Nickolas Papanikolau

  Fundacao Champalimaud

  STUDY CHAIR

• Kostantinos Marias

  FORTH

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 17, 2022
• First Posted: May 20, 2022
• Study Start: February 24, 2021
• Primary Completion: March 31, 2025
• Study Completion: March 31, 2025
• Last Updated: June 4, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will share

Locations

IT (1)