Heuristics, Algorithms and Machine Learning: Evaluation & Testing in Radiation Therapy

Cancer Clinical Trial

— Hamlet rt  

Official title:

Hamlet-RT: Heuristics, Algorithms and Machine Learning: Evaluation & Testing in Radiation Therapy

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04060706
• Other study ID #: Hamlet.rt
• Status: Recruiting
• Start date: September 11, 2019
• Est. completion date: January 1, 2028

Study information

• Verified date: July 2021
• Source: Cambridge University Hospitals NHS Foundation Trust
• Contact: Meena Murthy
• Phone: 01223 349707
• Email: meena.murthy[@]addenbrookes.nhs.uk
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 310
• Est. completion date: January 1, 2028
• Est. primary completion date: January 1, 2023
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Cancer

Intervention

Radiation:
• Radical Image-Guided Radiotherapy
Questionnaires administered will monitor the clinical toxicity experienced by each patient up to 5 years post radiotherapy

Locations

Country	Name	City	State
United Kingdom	Cambridge University Hospitals NHS Foundation Trust	Cambridge	Cambridgeshire

Sponsors (3)

Lead Sponsor	Collaborator
CCTU- Cancer Theme	Microsoft Research, University of Cambridge

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Machine Learning Modelling	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	8 years from FPFV
Primary	Predictive Modelling	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	8 years from FPFV
Primary	Clinical Toxicity Evaluation	Evaluation of the clinical toxicity experienced by each patient up to 5 years post radiotherapy to inform the predictive models in outcome 2	8 years from FPFV