Ignite Creation Date:
2024-05-06 @ 8:23 PM
Last Modification Date:
2024-10-26 @ 3:26 PM
Study NCT ID:
NCT06363435
Status:
RECRUITING
Last Update Posted:
2024-04-12
First Post:
2024-03-29
Brief Title:
AI-based Measurements of Tumour Burden in PSMA PET-CT
Sponsor:
Elin Tragardh
Organization:
Skane University Hospital
Study Overview
Official Title:
The Prognostic Value of AI-based Measurements of Tumour Burden in PSMA PET-CT in Patients With Prostate Cancer
Status:
RECRUITING
Status Verified Date:
2024-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 prostateprostate 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 10000 men annually and the mortality rate of 2400 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 prostateprostate 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
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 prostateprostate 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?:
None