Ignite Creation Date:
2024-10-26 @ 3:43 PM
Last Modification Date:
2024-10-26 @ 3:43 PM
Study NCT ID:
NCT06657027
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-10-15
Brief Title:
Artificial Intelligence-Guided Radiotherapy Planning for Glioblastoma
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Evaluation of the Efficacy and Safety of Personalized Radiotherapy Guided by Predictive Models of Tumor Infiltration Combining Artificial Intelligence and Multiparametric MRI in Glioblastomas
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
ARTPLAN-GLIO
Brief Summary:
The ARTPLAN-GLIO study aims to evaluate the feasibility and effectiveness of integrating artificial intelligence in personalized radiotherapy planning for glioblastomas On the basis of previous work by our group where a predictive model was developed from radiological characteristics extracted from MR images this project will evaluate the use of tumor infiltration probability maps in radiotherapy planning
Currently radiotherapy treatment uses margins defined by population studies without considering the individual characteristics of the patients Although 80 of recurrences occur in peritumoral areas close to the surgical margins treatment volumes are not customized owing to the lack of techniques that distinguish between edema and infiltrated tumor tissue
Our recurrence probability maps address this limitation and could improve radiation planning In this study the volumes and doses of radiotherapy were adjusted according to the predictions of the model with a focus on high-risk areas to optimize local control and reduce toxicity in healthy tissues
Survival results will be compared between patients treated with personalized AI-guided radiotherapy and a historical cohort with standard treatment In addition the safety of the approach will be evaluated by adverse event analysis Finally an accessible online platform with the potential to transform glioblastoma treatment and improve patient survival will be developed to implement this predictive model
Currently radiotherapy treatment uses margins defined by population studies without considering the individual characteristics of the patients Although 80 of recurrences occur in peritumoral areas close to the surgical margins treatment volumes are not customized owing to the lack of techniques that distinguish between edema and infiltrated tumor tissue
Our recurrence probability maps address this limitation and could improve radiation planning In this study the volumes and doses of radiotherapy were adjusted according to the predictions of the model with a focus on high-risk areas to optimize local control and reduce toxicity in healthy tissues
Survival results will be compared between patients treated with personalized AI-guided radiotherapy and a historical cohort with standard treatment In addition the safety of the approach will be evaluated by adverse event analysis Finally an accessible online platform with the potential to transform glioblastoma treatment and improve patient survival will be developed to implement this predictive model
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None