NCT06657027

Artificial Intelligence-Guided Radiotherapy Planning for Glioblastoma

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
• Phase: —
• Study type: Observational
• Enrollment: 40 (estimated)
• Sponsor: Hospital del Rio Hortega · Academic / Other
• Sex: All
• Age: 15 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Glioblastoma

Timeline

• Start date: 2025-01-01
• Primary completion: 2026-12-31
• Completion: 2027-06-30
• First posted: 2024-10-24
• Last updated: 2024-10-24