Artificial Intelligence in CNS Radiation Oncology

ConditionCNS Tumor

Trial statusRecruiting

Trial phaseNot listed

Trial typeObservational

Biological sexAll

Age1+

SponsorTata Memorial Centre

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

None

Disqualifiers

RT treatment outside TMC.

Radiation planning not done in the treatment planning system (treated using clinical marking/ conventional simulator).

Trial population

With approximately 500-600 patients with CNS tumors treated with RT in TMC every year, we expect a ceiling of 6000 patients during 2010-2022, which will be the maximum number of patients used for the analysis.

Trial design

Design model

Case-only

Time perspective

Retrospective

Treatments tested in this trial

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
The radiation plans and dose-volume histogram will be obtained from TPS. All the images and radiation-related data will be downloaded from the PACS and TPS, applying anonymization filters. Clinical features (patient, disease, treatment-related characteristics, and outcomes) will be extracted by review of electronic medical records. Imaging pre-processing will be done, which will include skull stripping and registration across different modalities (e.g., MRI and CT) or different sequences(e.g., T1C, T2W, ADC) will be done using rigid or deformable algorithms as suited best for the modality

Treatment groups

No treatment groups listed

Trial outcomes

Primary outcomes

Autosegmentation of organs at risk and target volumes.

The agreement between manual segmentation and automated segmentation using an artificial intelligence-based model will be assessed using Dice coefficient of similarity.

Time frame

5 years

Secondary outcomes

Survival Analysis and Toxicity Estimation

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.

Time frame

5 years

Dosiomic analysis

Quantitative analysis of radiation dosimetric maps and correlation with survival and toxicity. The performance metrics of the radiomics model in the prediction of recurrence/ death or toxicity (necrosis) will be compared with ground truth using sensitivity, specificity, accuracy, area under curve.

Time frame

5 years

Response assessment following treatment

Development of an automated response assessment algorithm using the images (MRI/ CT/ PET scan) following radiation. The predicted response using machine learning algorithms will be compared with actual outcomes (response/ stable disease/ progressive disease) and performance measured in the form of sensitivity, specificity, accuracy, area under curve.

Time frame

5 years

Other outcomes

Automated radiation planning and evaluation

Application of artificial intelligence algorithms in the treatment planning system for automated radiation plan generation and evaluation. The plans generated using artificial intelligence algorithms will be manually evaluated by radiation oncologists/ physicists and reported with proportions/ratios.

Time frame

5 years

Data Banking

Development of a data bank to store clinical details, images, and radiation plans. The annotated clinical, imaging, and radiation data will be secured in a database (no formal assessment is planned for the efficacy of data storage). Standardization of clinical variables with automated data extraction from the medical records and PACS will be established.

Time frame

5 years

Natural language processing for data interpretation

Development of NLP algorithms for automated recording of data and interpretation. The performance of NLP algorithms in interpretation will be assessed manually (with actual data/ records) and reported with confusion matrices e.g., sensitivity, specificity

Time frame

5 years

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Tata Memorial Centre

Lead sponsor

Bhabha Atomic Research Centre

Collaborator

Source ClinicalTrials.gov

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