Cancer
Conditions
Keywords
Radiotherapy, Image-guided, Head & Neck, Brain, Lung, Prostate, Adults
Brief summary
The Hamlet.rt study is a prospective data collection and patient questionnaire study for patients undergoing image-guided radiotherapy with curative intent. The aim of the study is to use novel machine learning and mathematical techniques to build a model that can predict the risk of significant side effects from radiotherapy treatment for an individual patient: using calculations of normal tissue dose from radiotherapy treatment planning and patient baseline characteristics derived from image and non-image data, continuously updated as the patient is reviewed both during and after treatment. A secondary goal of the project is to facilitate research in machine learning and medical image processing for radiation therapy through the creation of a discoverable and shared data resource for research use.
Interventions
RADIATIONRadical Image-Guided Radiotherapy
Questionnaires administered will monitor the clinical toxicity experienced by each patient up to 5 years post radiotherapy
Sponsors
University of Cambridge
Microsoft Research
CCTU- Cancer Theme
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Inclusion criteria
* Participant is willing and able to give informed consent for participation in the study * Male or Female * Aged 18 years or older * Diagnosed with primary prostate cancer, head and neck cancer, lung cancer, or brain tumour * Treated with curative intent * Suitable for radical image guided radiotherapy * WHO ECOG performance status 0 or 1 * Expected survival of 18 months or more
Exclusion criteria
* Participant is not willing or able to complete the protocol-stated requirements of the study, e.g. accessing & completing web-based long-term follow-up questionnaires.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Machine Learning Modelling | 8 years from FPFV | Characterise machine learning models for the four disease sites. Developing machine learning algorithms for autosegmentation of normal tissue anatomy, and to extend machine learning algorithms to identify and segment normal tissue structures in cone beam CT images, and to utilise the ML segmentations to evaluate image signatures correlated with treatment toxicity |
| Predictive Modelling | 8 years from FPFV | Predict performance matches with published techniques. Combining the machine learning models in outcome 1, with pre-treatment assessment data and on-treatment quantitative assessments in outcome 3 for the construction and evaluation of a predictive mathematical model |
| Clinical Toxicity Evaluation | 8 years from FPFV | Evaluation of the clinical toxicity experienced by each patient up to 5 years post radiotherapy to inform the predictive models in outcome 2 |
Countries
United Kingdom
Contacts
Primary ContactMeena Murthy
Backup ContactCCTU Cancer
Outcome results
None listed