Clinical trial · Observational
Artificial Intelligence in CNS Radiation Oncology
Artificial Intelligence in Radiation Oncology for CNS Tumors
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Radiotherapy involves the use of high-energy X-rays, which can be used to stop the growth of tumor cells. Radiotherapy constitutes an essential avenue in the treatment of brain tumors. The modern techniques of radiotherapy involve radiation planning techniques guided by computer algorithms aimed to deliver high doses of radiation to the areas of brain with tumors and limit the doses to surrounding normal structures. Artificial intelligence uses advanced analytical processes aided by computational analysis, which can be undertaken on the medical images, and radiation planning process. We plan to use artificial intelligence techniques to automatically delineate areas of the brain with tumor and other normal structures as identified from images. Also, we will use artificial intelligence on the radiation dose images and other images done for radiation treatment to classify tumors with good or bad prognoses, identify patients developing radiation complications, and detect responses after treatment.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| CNS Tumor | Central Nervous System Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| The images (CT, MRI, PET) used for RT planning, mid-treatment imaging as part of IGRT or disease evaluation, and response assessment/ surveillance post-RT | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Autosegmentation of organs at risk and target volumes.
- timeFrame
- 5 years
- description
- The agreement between manual segmentation and automated segmentation using an artificial intelligence-based model will be assessed using Dice coefficient of similarity.
Secondary outcomes (3)
- measure
- Survival Analysis and Toxicity Estimation
- timeFrame
- 5 years
- description
- Quantitative image analysis from target volumes and organs at risk and correlation with survival and normal tissue complications. Survival analysis will be done using Kaplan Meier method and nomograms will be constructed from quantitative imaging features. Toxicity assessment will include the incidence of radionecrosis and the correlation of quantitative imaging markers will be done using regression models or machine learning algorithms.
- measure
- Dosiomic analysis
- timeFrame
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 1 Year
Show eligibility criteria text
Inclusion Criteria: • Patients with CNS tumors treated with radiation in TMC between January 2010 and December 2022. Exclusion Criteria: * RT treatment outside TMC. * Radiation planning not done in the treatment planning system (treated using clinical marking/ conventional simulator).
References
Publications (0)
Data not yet available