Ignite Creation Date:
2025-12-25 @ 3:47 AM
Ignite Modification Date:
2025-12-26 @ 2:35 AM
Study NCT ID:
NCT05384002
Status:
COMPLETED
Last Update Posted:
2025-04-04
First Post:
2022-05-17
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
An AI Platform Integrating Imaging Data and Models, Supporting Precision Care Through Prostate Cancer's Continuum
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D011471', 'term': 'Prostatic Neoplasms'}, {'id': 'C565201', 'term': 'Prostate Cancer, Hereditary, 7'}], 'ancestors': [{'id': 'D005834', 'term': 'Genital Neoplasms, Male'}, {'id': 'D014565', 'term': 'Urogenital Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D005832', 'term': 'Genital Diseases, Male'}, {'id': 'D000091662', 'term': 'Genital Diseases'}, {'id': 'D000091642', 'term': 'Urogenital Diseases'}, {'id': 'D011469', 'term': 'Prostatic Diseases'}, {'id': 'D052801', 'term': 'Male Urogenital Diseases'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D008279', 'term': 'Magnetic Resonance Imaging'}], 'ancestors': [{'id': 'D014054', 'term': 'Tomography'}, {'id': 'D003952', 'term': 'Diagnostic Imaging'}, {'id': 'D019937', 'term': 'Diagnostic Techniques and Procedures'}, {'id': 'D003933', 'term': 'Diagnosis'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'OTHER', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 14000}, 'targetDuration': '1 Year', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2021-02-24', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-04', 'completionDateStruct': {'date': '2025-03-31', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-04-01', 'studyFirstSubmitDate': '2022-05-17', 'studyFirstSubmitQcDate': '2022-05-17', 'lastUpdatePostDateStruct': {'date': '2025-04-04', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-05-20', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-03-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'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'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'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'], 'conditions': ['Prostate Cancer', 'Prostate Cancer Metastatic', 'Prostate Cancer Recurrent', 'Prostate Cancer Aggressiveness']}, 'descriptionModule': {'briefSummary': '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.\n\nThe 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.\n\nTo 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.'}, 'eligibilityModule': {'sex': 'MALE', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '85 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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).', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. histological confirmed PCa or suspicion of PCa (abnormal PSA values and/or positive DRE);\n2. 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);\n3. age ≥ 18 years at the time of diagnosis\n4. signed written informed consent form (only for prospective enrollement).'}, 'identificationModule': {'nctId': 'NCT05384002', 'briefTitle': "An AI Platform Integrating Imaging Data and Models, Supporting Precision Care Through Prostate Cancer's Continuum", 'nctIdAliases': ['NCT05380518'], 'organization': {'class': 'OTHER', 'fullName': "Fondazione del Piemonte per l'Oncologia"}, 'officialTitle': "An AI Platform Integrating Imaging Data and Models, Supporting Precision Care Through Prostate Cancer's Continuum", 'orgStudyIdInfo': {'id': 'ProCAncer-I'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Retrospective (training model)', 'interventionNames': ['Diagnostic Test: Magnetic Resonance Imaging']}, {'label': 'Prospective (validation model)', 'interventionNames': ['Diagnostic Test: Magnetic Resonance Imaging']}], 'interventions': [{'name': 'Magnetic Resonance Imaging', 'type': 'DIAGNOSTIC_TEST', 'description': 'Patients who underwent MRI with confirmed pathology data (either biopsy or prostatectomy)', 'armGroupLabels': ['Prospective (validation model)', 'Retrospective (training model)']}]}, 'contactsLocationsModule': {'locations': [{'zip': '10060', 'city': 'Candiolo', 'state': 'Italy', 'country': 'Italy', 'facility': "Fondazione del Piemonte per l'Oncologia", 'geoPoint': {'lat': 44.95858, 'lon': 7.59812}}], 'overallOfficials': [{'name': 'Manolis Tsiknakis', 'role': 'STUDY_DIRECTOR', 'affiliation': 'FORTH'}, {'name': 'Nickolas Papanikolau', 'role': 'STUDY_CHAIR', 'affiliation': 'Fundacao Champalimaud'}, {'name': 'Kostantinos Marias', 'role': 'STUDY_CHAIR', 'affiliation': 'FORTH'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'YES'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': "Fondazione del Piemonte per l'Oncologia", 'class': 'OTHER'}, 'collaborators': [{'name': 'Fundacao Champalimaud', 'class': 'OTHER'}, {'name': 'Stichting Katholieke Universiteit', 'class': 'OTHER'}, {'name': 'Fundacion Para La Investigacion Hospital La Fe', 'class': 'OTHER'}, {'name': 'University of Pisa', 'class': 'OTHER'}, {'name': 'Institut Paoli-Calmettes', 'class': 'OTHER'}, {'name': 'Hacettepe University', 'class': 'OTHER'}, {'name': "Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta", 'class': 'OTHER'}, {'name': 'JCC DIAGNOSTIC IMAGING', 'class': 'UNKNOWN'}, {'name': 'National Cancer Institute (NCI)', 'class': 'NIH'}, {'name': 'Agios Savas', 'class': 'UNKNOWN'}, {'name': 'Royal Marsden NHS Foundation Trust', 'class': 'OTHER'}, {'name': 'QS INSTITUTO DE INVESTIGACION E INNOVACION SL', 'class': 'UNKNOWN'}, {'name': 'IDRYMA TECHNOLOGIAS KAI EREVNAS', 'class': 'UNKNOWN'}, {'name': 'Fondazione C.N.R./Regione Toscana "G. Monasterio", Pisa, Italy', 'class': 'OTHER_GOV'}, {'name': 'THE GENERAL HOSPITAL CORPORATION', 'class': 'UNKNOWN'}, {'name': 'BIOTRONICS 3D LIMITED', 'class': 'UNKNOWN'}, {'name': 'Advantis Medical Imaging', 'class': 'UNKNOWN'}, {'name': 'QUIBIM SOCIEDAD LIMITADA', 'class': 'UNKNOWN'}, {'name': 'University of Vienna', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}