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

Summary

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.

Conditions

• Prostate Cancer
• Prostate Cancer Metastatic
• Prostate Cancer Recurrent
• Prostate Cancer Aggressiveness

Interventions

DIAGNOSTIC_TEST

Magnetic Resonance Imaging

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

Sponsors & Collaborators

• Fundacao Champalimaud

  collaborator OTHER

• Stichting Katholieke Universiteit

  collaborator OTHER

• Fundacion Para La Investigacion Hospital La Fe

  collaborator OTHER

• University of Pisa

  collaborator OTHER

• Institut Paoli-Calmettes

  collaborator OTHER

• Hacettepe University

  collaborator OTHER

• Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta

  collaborator OTHER

• JCC DIAGNOSTIC IMAGING

  collaborator UNKNOWN

• National Cancer Institute (NCI)

  collaborator NIH

• Agios Savas

  collaborator UNKNOWN

• Royal Marsden NHS Foundation Trust

  collaborator OTHER

• QS INSTITUTO DE INVESTIGACION E INNOVACION SL

  collaborator UNKNOWN

• IDRYMA TECHNOLOGIAS KAI EREVNAS

  collaborator UNKNOWN

• Fondazione C.N.R./Regione Toscana "G. Monasterio", Pisa, Italy

  collaborator OTHER_GOV

• THE GENERAL HOSPITAL CORPORATION

  collaborator UNKNOWN

• BIOTRONICS 3D LIMITED

  collaborator UNKNOWN

• Advantis Medical Imaging

  collaborator UNKNOWN

• QUIBIM SOCIEDAD LIMITADA

  collaborator UNKNOWN

• University of Vienna

  collaborator OTHER

• Fondazione del Piemonte per l'Oncologia

  lead OTHER

Principal Investigators

• Manolis Tsiknakis · FORTH

• Nickolas Papanikolau · Fundacao Champalimaud

• Kostantinos Marias · FORTH

Eligibility

Min Age

18 Years

Max Age

85 Years

Sex

MALE

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2021-02-24

Primary Completion

2025-03-31

Completion

2025-03-31

Countries

• Italy

Study Locations