Clinical trial NCT05384002: An AI Platform Integrating Imaging Data and Models, Supporting Precision Care Through Prostate Cancer's Continuum
Official title: An AI Platform Integrating Imaging Data and Models, Supporting Precision Care Through Prostate Cancer's Continuum
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Prostate Cancer
Prostate Cancer Metastatic
Prostate Cancer Recurrent
Prostate Cancer Aggressiveness
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
Lead sponsor: Fondazione del Piemonte per l'Oncologia
Sponsor class: OTHER
Collaborators: name: Fundacao Champalimaud
name: Stichting Katholieke Universiteit
name: Fundacion Para La Investigacion Hospital La Fe
name: University of Pisa
name: Institut Paoli-Calmettes
name: Hacettepe University
name: Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta
name: JCC DIAGNOSTIC IMAGING
name: National Cancer Institute (NCI)
name: Agios Savas
name: Royal Marsden NHS Foundation Trust
name: QS INSTITUTO DE INVESTIGACION E INNOVACION SL
name: IDRYMA TECHNOLOGIAS KAI EREVNAS
name: Fondazione C.N.R./Regione Toscana "G. Monasterio", Pisa, Italy
name: THE GENERAL HOSPITAL CORPORATION
name: BIOTRONICS 3D LIMITED
name: Advantis Medical Imaging
name: QUIBIM SOCIEDAD LIMITADA
name: University of Vienna
First posted: 2022-05-20
Enrollment: 14000
Sex: MALE
Minimum age (years): 18
Maximum age (years): 85
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: OTHER
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).

Brief 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.


Eligibility criteria: 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).

Primary outcomes:
measure: To create a repository (Prostate-NET) of retrospective MRI examinations with related clinical and pathology data dedicated to prostate cancer.
timeFrame: 24 months
measure: 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.
timeFrame: 36 months
measure: To develop vendor-specific and vendor neutral AI models exploiting the prospective data that will be uploaded to the Prostate-NET platform.
timeFrame: 48 months
Secondary outcomes:
Other outcomes:
Locations:
city: Candiolo
country: Italy
facility: Fondazione del Piemonte per l'Oncologia
Study officials:
affiliation: FORTH
name: Manolis Tsiknakis
role: STUDY_DIRECTOR
affiliation: Fundacao Champalimaud
name: Nickolas Papanikolau
role: STUDY_CHAIR
affiliation: FORTH
name: Kostantinos Marias
role: STUDY_CHAIR
References: