Glioblastoma
Conditions
Keywords
radiotherapy, oncology, artificial intelligence, glioblastoma, personalized radiotherapy, precision medicine
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.
Interventions
None listed
Sponsors
Hospital del Rio Hortega
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
15 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Feasibility of AI-Guided Radiotherapy for Glioblastoma | 12 months after the start of radiotherapy for the last enrolled patient. | The primary outcome of the study is to assess the feasibility of integrating an AI-based predictive model into radiotherapy planning for patients with glioblastoma. The model uses radiomic features derived from multiparametric MRI to generate tumor infiltration probability maps, which guide the personalized adjustment of treatment volumes and doses. Feasibility will be determined by evaluating the successful integration of the AI model into clinical practice, the precision of the model in identifying areas of tumor infiltration, and the ability to implement personalized treatment plans in a routine clinical setting. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Progression-Free Survival (PFS) at 1 Year | 12 months after the start of radiotherapy for each patient. | This outcome evaluates whether patients treated with personalized AI-guided radiotherapy experience improved progression-free survival (PFS) at one year compared to a historical control group treated with standard radiotherapy. PFS is defined as the time from the start of radiotherapy to either the first documented disease progression or death from any cause. The AI-guided approach uses tumor infiltration probability maps to target high-risk areas, aiming to delay or prevent local recurrence. |
| Overall Survival (OS) | 24 months after the start of radiotherapy for each patient. | This outcome measures the overall survival (OS) of patients treated with AI-guided personalized radiotherapy for glioblastoma. OS is defined as the time from the start of radiotherapy to death from any cause. The study aims to assess whether personalized radiotherapy, guided by AI-driven tumor infiltration probability maps, improves survival outcomes compared to standard radiotherapy. This will be evaluated by comparing OS in the AI-guided group with a historical control group treated with standard radiotherapy protocols. |
| Quality of Life | 12 months after the start of radiotherapy for each patient. | This outcome evaluates the differences in quality of life (QoL) between patients treated with AI-guided radiotherapy based on multiparametric MRI and those treated with standard radiotherapy (historical controls). Quality of life will be evaluated using the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). |
Contacts
Primary ContactSantiago Cepeda Principal Investigator, MD., PhD
Backup ContactOlga Esteban Co-PI, MD
Outcome results
None listed