Ignite Creation Date:
2025-12-24 @ 10:31 PM
Ignite Modification Date:
2025-12-30 @ 11:16 AM
Study NCT ID:
NCT06363435
Status:
RECRUITING
Last Update Posted:
2024-04-12
First Post:
2024-03-29
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI-based Measurements of Tumour Burden in PSMA PET-CT
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D011471', 'term': 'Prostatic Neoplasms'}], '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'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1500}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2024-03-29', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-04', 'completionDateStruct': {'date': '2033-03', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-04-09', 'studyFirstSubmitDate': '2024-03-29', 'studyFirstSubmitQcDate': '2024-04-09', 'lastUpdatePostDateStruct': {'date': '2024-04-12', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-04-12', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2031-03', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Tumour burden (cm3) in relation to overall survival', 'timeFrame': '5-year follow-up', 'description': 'Evaluate how the total tumour burden (cm3) predicts overall survival (OS). The total tumour burden will automatically be calculated by the AI-based method and will through Cox regression analysis be related to OS'}], 'secondaryOutcomes': [{'measure': 'Tumour burden (cm3) in relation to biochemical recurrence', 'timeFrame': '5 years', 'description': 'Evaluate how the total tumour burden (cm3) predicts time to biochemical recurrence. The total tumour burden will automatically be calculated by the AI-based method and will through Cox regression analysis be related to time to biochemical recurrence. This analysis will be performed in patients performing the PET examination due to initial staging of high-risk prostate cancer'}, {'measure': 'Number of tumours/metastases in relation to OS', 'timeFrame': '5 years', 'description': 'Evaluate how automatically derived number of tumours/metastases predict OS throught Cox regression analysis'}, {'measure': 'Comparing two different segmentation methods in relation to OS', 'timeFrame': '5 years', 'description': 'Evaluate which of two different segmentation methods (50% of SUVmax and SUV threshold of 4) of total tumour burden is best for predicting outcome 1 (overall survival)'}, {'measure': 'Comparing total tumour burden (cm3) measured manually and by the AI-based mehtod', 'timeFrame': '5 years', 'description': 'The automatically derived meausurements of total tumour burden (cm3) will be compared to manually derived measurements by using Bland-Altman analysis and correlation analysis.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['artificial intelligence', '18F-PSMA-1007'], 'conditions': ['Prostate Cancer']}, 'descriptionModule': {'briefSummary': 'The primary aim of the present study is to evaluate how automatically calculated (by an AI-based method) tumour burden, measured as tumour volume (TV) and as tumour uptake (TU: TV x SUVmean) in the prostate/prostate bed, pelvic lymph nodes, distant lymph nodes, bone and as the total tumour burden predicts overall survival (OS) in patients with prostate cancer (newly diagnosed and patients with biochemical recurrence).', 'detailedDescription': 'In Sweden, prostate cancer is diagnosed in 10,000 men annually and the mortality rate of 2,400 is among the highest worldwide. Some prostate cancers are at high risk of metastatic progression to lethal disease and require correct staging or detection of recurrence and multidisciplinary treatments.\n\nThe investigators have developed an AI-based method to detect and quantify tumours and metastases in 18F-PSMA-1007 PET-CT scans in patients with prostate cancer. The method can find tumours in the prostate and metastases in pelvic lymph nodes, distant lymph nodes and in bone, both in patients referred to the PET-CT scan for primary staging of high-risk prostate cancer for secondary staging due to recurrence.\n\nPatients referred to clinically indicated PSMA PET-CT due to either initial staging of primary high-risk prostate cancer or due to biochemical recurrence will be eligible for inclusion. The AI-based method will automatically calculate TV, TU and number of suspected lesions and this information will be stored in a database. The values will after a 5 year follow-up period be analysed with regard to overall survival (OS) and progression-free survival (PFS).\n\nThe primary aim of the present study is to evaluate how tumour burden, measured as TV and as tumour uptake (TU: TV x SUVmean) in the prostate/prostate bed, pelvic lymph nodes, distant lymph nodes, bone and as the total tumour burden predicts overall survival (OS) in patients with prostate cancer (newly diagnosed and patients with biochemical recurrence). A secondary aim is to evaluate how the AI-derived measurements predict time to biochemical recurrence in a sub-cohort of patients with newly diagnosed high-risk prostate cancer. Tertiary aims are to evaluate the difference in TV and TU measured with two different segmentation methods (a threshold of 50% of SUVmax in each lesion and a threshold of SUV 4) in relation to OS and biochemical PFS. The impact of the number of automatically calculated suspected lesions will also be investigated regarding OS and biochemical PFS as well as to the difference in tumour burden measured with AI and manually.'}, 'eligibilityModule': {'sex': 'MALE', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '120 Years', 'minimumAge': '20 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Patients who undergo PSMA PET-CT scans due to primary staging of high-risk prostate cancer or due to secondary staging due to biochemical recurrance of prostate cancer', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Patients referred to a clinically indicated 18F-PSMA-1007 PET-CT scan at Skåne University Hospital, Lund or Malmö, Sweden\n\nExclusion Criteria:\n\n* Patients under 20 years old'}, 'identificationModule': {'nctId': 'NCT06363435', 'briefTitle': 'AI-based Measurements of Tumour Burden in PSMA PET-CT', 'organization': {'class': 'OTHER', 'fullName': 'Skane University Hospital'}, 'officialTitle': 'The Prognostic Value of AI-based Measurements of Tumour Burden in PSMA PET-CT in Patients With Prostate Cancer', 'orgStudyIdInfo': {'id': '#2022-01302-02-PSMA'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Patients with prostate cancer', 'description': 'Patients referred to clinically indicated PSMA PET-CT due to initial or secondary staging of prostate cancer', 'interventionNames': ['Device: AI-based detection and quantification of suspected tumour/metastases in PSMA PET/CT scans']}], 'interventions': [{'name': 'AI-based detection and quantification of suspected tumour/metastases in PSMA PET/CT scans', 'type': 'DEVICE', 'description': 'Tumour burden will be automatically calculated and stored in a database. The result of the AI-based measurements will not involve the handling of the patients', 'armGroupLabels': ['Patients with prostate cancer']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Lund', 'status': 'RECRUITING', 'country': 'Sweden', 'contacts': [{'name': 'Elin Tragardh, Prof', 'role': 'CONTACT', 'email': 'elin.tragardh@skane.se', 'phone': '+4640338724'}], 'facility': 'Skåne University Hospital', 'geoPoint': {'lat': 55.70584, 'lon': 13.19321}}, {'city': 'Malmo', 'status': 'RECRUITING', 'country': 'Sweden', 'contacts': [{'name': 'Elin Tragardh, Prof', 'role': 'CONTACT', 'email': 'elin.tragardh@skane.se', 'phone': '+4640338724'}], 'facility': 'Skåne university hospital', 'geoPoint': {'lat': 55.60587, 'lon': 13.00073}}], 'centralContacts': [{'name': 'Elin Tragardh, Prof', 'role': 'CONTACT', 'email': 'elin.tragardh@skane.se', 'phone': '+4640338724'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Elin Tragardh', 'class': 'OTHER'}, 'collaborators': [{'name': 'Lund University', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Elin Tragardh', 'investigatorAffiliation': 'Skane University Hospital'}}}}