Ignite Creation Date:
2024-05-06 @ 8:16 PM
Last Modification Date:
2024-10-26 @ 3:24 PM
Study NCT ID:
NCT06317948
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2024-03-20
First Post:
2024-03-12
Brief Title:
Improving the Quality of Radiotherapy by Multi-Institution Knowledge-Based Planning Optimization Models Acronym MIKAPOCo Multi-Institutional Knowledge-based Approach in Plan Optimization for the Community
Sponsor:
IRCCS Ospedale San Raffaele
Organization:
IRCCS Ospedale San Raffaele
Study Overview
Official Title:
Improving the Quality of Radiotherapy by Multi-Institution Knowledge-Based Planning Optimization Models
Status:
ENROLLING_BY_INVITATION
Status Verified Date:
2024-03
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:
MIKAPOCo
Brief Summary:
Investigators central hypothesis is that it is possible to create libraries of consistent Knowledge-Based plan-models derived from large Institutional experiences These libraries can be used to guide automated RT planning and serve as tools to assist centers for plan quality assurance QA and plan prediction
Quantifying Inter-institute variability of RT planning and building libraries of interchangeable and validated multi-Institutional KB plan prediction models is expected to impact on the quality of planning at the national level The project has the potential of facilitating the introduction of AI approaches in plan optimization thus reducing intra and inter-Institute planning variability Improving plan quality is expected to translate into better outcome after RT in terms of local control and even more of side effects and Quality of life Positive impact is also expected in patient selection for advanced techniques in plan audit and plan optimization in clinical trials in technology comparison and cost-benefit analyses as well as in the RT educational field
Quantifying Inter-institute variability of RT planning and building libraries of interchangeable and validated multi-Institutional KB plan prediction models is expected to impact on the quality of planning at the national level The project has the potential of facilitating the introduction of AI approaches in plan optimization thus reducing intra and inter-Institute planning variability Improving plan quality is expected to translate into better outcome after RT in terms of local control and even more of side effects and Quality of life Positive impact is also expected in patient selection for advanced techniques in plan audit and plan optimization in clinical trials in technology comparison and cost-benefit analyses as well as in the RT educational field
Detailed Description:
Major aims
1 To create libraries of consistently generated KB models for patients treated with RT for breast and prostate cancer and for selected stereotactic-body RT SBRT applications based on the experience of many Italian Institutions to quantify planning inter-institute variability in homogeneous classes of patients
2 To group models based on their characteristics and interchangeability To assess groups of highly interchangeable models to be considered for multi-institutional dose-volume histogram DVH prediction purposes
1 To create libraries of consistently generated KB models for patients treated with RT for breast and prostate cancer and for selected stereotactic-body RT SBRT applications based on the experience of many Italian Institutions to quantify planning inter-institute variability in homogeneous classes of patients
2 To group models based on their characteristics and interchangeability To assess groups of highly interchangeable models to be considered for multi-institutional dose-volume histogram DVH prediction purposes
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