Ignite Creation Date:
2024-07-17 @ 10:56 AM
Last Modification Date:
2024-10-26 @ 3:32 PM
Study NCT ID:
NCT06468332
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-06-21
First Post:
2024-06-10
Brief Title:
Artificial Intelligence-based Platform Integrating Pathologic Imaging and Molecular Profiles of Prostate Cancer
Sponsor:
Azienda Ospedaliero Universitaria Maggiore della Carita
Organization:
Azienda Ospedaliero Universitaria Maggiore della Carita
Study Overview
Official Title:
Development of Artificial Intelligence-based Multiplex Network for Individualized Risk Stratification of Prostate Cancer
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-06
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:
PRISM-AI
Brief Summary:
The goal of this observational study is to use an artificial intelligence-based platform integrating clinical pathologic imaging genomic and transcriptomic profiles of prostate cancer in order to outperform currently available risk-stratification tools Thus could lead to a better risk assessment of prostate cancer progression and recurrence
A key challenge in managing non-metastatic Prostate Cancer is identifying and distinguishing between men that are likely to progress to clinically significant disease and those whose disease is likely to remain indolent for the remainder of their lifetime aiming to offer invasive treatment only to patients harboring a disease which would affect cancer specific survival
In the context of a multidisciplinary team of urologists and digital health experts a two-phases study has been designed A retrospective cohort of 200 radical prostatectomy patients will be identified within three participating clinical centres Clinical pathology MRI data will be collected and stored in an appropriate anonymised online platform Whole exome sequences DNAseq will be analyzed for each patients total samples200 and transcriptome analyses RNAseq for both cancer and non-cancer tissues total samples400 In parallel the recruitment of a prospective cohort of 200 biopsy-proven newly PCa patients will start For these patients blood and urine samples will be also collected Data will be collected and genetic analyses total samples1000 will be performed as in the retrospective phase Patients will be treated and followed according to best clinical practice
Expected Results The retrospective phase would allow to identify genes pathological features and MRI imaging features that can correlate with PCa biology in order to create and train the AI-based algorithm The prospective phase will allow the validation of the prognostic tool the definition of a novel risk grouping and the evaluation of the prognostic role of biofluid analysis
A key challenge in managing non-metastatic Prostate Cancer is identifying and distinguishing between men that are likely to progress to clinically significant disease and those whose disease is likely to remain indolent for the remainder of their lifetime aiming to offer invasive treatment only to patients harboring a disease which would affect cancer specific survival
In the context of a multidisciplinary team of urologists and digital health experts a two-phases study has been designed A retrospective cohort of 200 radical prostatectomy patients will be identified within three participating clinical centres Clinical pathology MRI data will be collected and stored in an appropriate anonymised online platform Whole exome sequences DNAseq will be analyzed for each patients total samples200 and transcriptome analyses RNAseq for both cancer and non-cancer tissues total samples400 In parallel the recruitment of a prospective cohort of 200 biopsy-proven newly PCa patients will start For these patients blood and urine samples will be also collected Data will be collected and genetic analyses total samples1000 will be performed as in the retrospective phase Patients will be treated and followed according to best clinical practice
Expected Results The retrospective phase would allow to identify genes pathological features and MRI imaging features that can correlate with PCa biology in order to create and train the AI-based algorithm The prospective phase will allow the validation of the prognostic tool the definition of a novel risk grouping and the evaluation of the prognostic role of biofluid analysis
Detailed Description:
None
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?:
None
Is an FDA AA801 Violation?:
None