Prostate Cancer
Conditions
Keywords
artificial intelligence, 18F-PSMA-1007
Brief summary
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).
Detailed description
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.
Interventions
DEVICEAI-based detection and quantification of suspected tumour/metastases in PSMA PET/CT scans
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
Sponsors
Elin Tragardh
Lund University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
MALE
Age
20 Years to 120 Years
Healthy volunteers
No
Inclusion criteria
* Patients referred to a clinically indicated 18F-PSMA-1007 PET-CT scan at Skåne University Hospital, Lund or Malmö, Sweden
Exclusion criteria
* Patients under 20 years old
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Tumour burden (cm3) in relation to overall survival | 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Tumour burden (cm3) in relation to biochemical recurrence | 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 |
| Number of tumours/metastases in relation to OS | 5 years | Evaluate how automatically derived number of tumours/metastases predict OS throught Cox regression analysis |
| Comparing two different segmentation methods in relation to OS | 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) |
| Comparing total tumour burden (cm3) measured manually and by the AI-based mehtod | 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. |
Countries
Sweden
Contacts
CONTACTElin Tragardh, Prof
Outcome results
None listed