Artificial Intelligence-Guided Radiotherapy Planning for Glioblastoma (ARTPLAN-GLIO)

Hospital del Rio Hortega

Status: Not yet enrolling

Conditions: Glioblastoma

Study type: Observational

Funder types: Other

Identifiers

NCT06657027

PI-24-563-H

Details and patient eligibility

About

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.

Enrollment: 40 estimated patients

Sex: All

Ages: 15+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Patients with a recent diagnosis of IDH wild-type glioblastoma, grade 4 according to the Central Nervous System Tumors classification of the World Health Organization of 2021.

• Ability to undergo MRI studies.

• Performance status with Karnofsky Performance Status (KPS) ≥ 60.

• Life expectancy ≥ 12 weeks.

• Laboratory results within the following ranges, obtained in the 14 days prior to enrollment:

  • Leucocitos ≥ 3,000/µL.
  • Absolute neutrophils ≥ 1,500/µL.
  • Plaquetas ≥ 75,000/µL.
  • Hemoglobin ≥ 9.0 g/dL (transfusion is allowed to reach the minimum level).
  • Glutamic-oxaloacetic transaminase (SGOT) ≤ 2 times the upper limit of normal.
  • Bilirubin ≤ 2 times the upper limit of normal.
  • Creatinina ≤ 1.5 mg/dL.

• Women of childbearing age must present a negative pregnancy test ≤ 14 days prior to enrollment.

• Ability to understand and sign the informed consent.

• Willingness to refrain from other cytotoxic or noncytotoxic therapies against the tumor during the protocol.

Exclusion criteria

• Presence of pacemakers, neurostimulators, cochlear implants, metal in ocular structures, or work history that compromise safety in MRI.
• Significant medical illnesses that may compromise tolerance to treatment, at the discretion of the investigator.
• History of invasive cancer in the last 3 years, with few exceptions.
• Active infections or serious intercurrent illnesses.
• Previous treatments with cytotoxic, noncytotoxic, experimental agents, or cranial radiation therapy.
• Maximum radiation target volume (GTV3) greater than 65 cc.

Trial design

40 participants in 1 patient group

AI-Guided Radiotherapy Cohort

Description:

This cohort includes patients with newly diagnosed IDH wild-type glioblastoma, grade 4, according to the 2021 WHO classification of Central Nervous System Tumors. Patients in this group will undergo personalized radiotherapy guided by artificial intelligence (AI) and multiparametric MRI, using predictive models to adjust treatment volumes and doses according to areas of tumor infiltration. The AI model, developed from radiomic characteristics of postoperative MRI, predicts tumor recurrence and infiltration, enabling targeted dose escalation to high-risk areas while minimizing radiation exposure to healthy tissues.