NCT Number: NCT06363435
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).
Interested in participating?
Request Info20 year–120 year
Male
Observational
Skåne University Hospital, Lund, Sweden
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.
The 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.
Patients 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).
The 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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
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
Time frame: 5-year follow-up
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
Time frame: 5 years
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
Time frame: 5 years
Evaluate how automatically derived number of tumours/metastases predict OS throught Cox regression analysis
Time frame: 5 years
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)
Time frame: 5 years
The automatically derived meausurements of total tumour burden (cm3) will be compared to manually derived measurements by using Bland-Altman analysis and correlation analysis.
Contact information is provided by the study sponsor or research team.
Elin Tragardh
Other
The Prognostic Value of AI-based Measurements of Tumour Burden in PSMA PET-CT in Patients With Prostate Cancer
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