Clinical trial NCT06657027: Artificial Intelligence-Guided Radiotherapy Planning for Glioblastoma
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
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Glioblastoma
Keywords: radiotherapy
oncology
artificial intelligence
glioblastoma
personalized radiotherapy
precision medicine
Lead sponsor: Hospital del Rio Hortega
Sponsor class: OTHER
First posted: 2024-10-24
Enrollment: 40
Sex: ALL
Minimum age (years): 15
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The study population consists of adult patients with newly diagnosed IDH wild-type glioblastoma, grade 4, as classified by the World Health Organization (WHO) 2021 Central Nervous System Tumor guidelines. Eligible participants must have undergone maximum safe tumor resection and be scheduled to receive radiotherapy.

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.


Eligibility criteria: 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.

Primary outcomes:
measure: Feasibility of AI-Guided Radiotherapy for Glioblastoma
timeFrame: 12 months after the start of radiotherapy for the last enrolled patient.
Secondary outcomes:
measure: Progression-Free Survival (PFS) at 1 Year
timeFrame: 12 months after the start of radiotherapy for each patient.
measure: Overall Survival (OS)
timeFrame: 24 months after the start of radiotherapy for each patient.
measure: Quality of Life
timeFrame: 12 months after the start of radiotherapy for each patient.
Other outcomes:
Locations:
Study officials:
References:
citation: Cepeda S, Luppino LT, Perez-Nunez A, Solheim O, Garcia-Garcia S, Velasco-Casares M, Karlberg A, Eikenes L, Sarabia R, Arrese I, Zamora T, Gonzalez P, Jimenez-Roldan L, Kuttner S. Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI. Cancers (Basel). 2023 Mar 22;15(6):1894. doi: 10.3390/cancers15061894.