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

NCT04060706 | Recruiting

• 1 other identifier: Hamlet.rt
• Study Type: observational
• Target: 310
• Locations: 1 country
• Sites: 1

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.

Conditions

Cancer

Keywords

Radiotherapy; Image-guided; Head & Neck; Brain; Lung; Prostate; Adults

Outcome Measures

Primary Outcomes (3)

• 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

• 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

• 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

Study Arms (4)

Prostate Cancer

Adults suitable for radical image-guided radiotherapy for their Prostate cancer, approximately 170 patients Components from RTOG, LENT SOM(A), RMH symptom scale and UCLA PCI (prostate cancer index) questionnaires will be used.

Radiation: Radical Image-Guided Radiotherapy

Head & Neck Cancer

Adults suitable for radical image-guided radiotherapy for their Head \& Neck cancer, approximately 140 patients. Components from CTCAE v3, LENT SOM(A), EORTC QLQ H+N35 \& Modified xerostomia questionnaires will be used.

Radiation: Radical Image-Guided Radiotherapy

Central Nervous System Tumours

Adults suitable for radical image-guided radiotherapy for their CNS tumour, as many patients recruited as possible. Components from RTOG, LENT SOM(A), Folstein mini mental state examination \& Generalised activites of daily living scale (G-ADL) questionnaires will be used.

Radiation: Radical Image-Guided Radiotherapy

Lung Cancer

Adults suitable for radical image-guided radiotherapy for their Lung cancer, as many patients recruited as possible. Components from RTOG \& LENT SOM(A) questionnaires will be used.

Radiation: Radical Image-Guided Radiotherapy

Interventions

Radical Image-Guided Radiotherapy RADIATION

Questionnaires administered will monitor the clinical toxicity experienced by each patient up to 5 years post radiotherapy

Central Nervous System Tumours; Head & Neck Cancer; Lung Cancer; Prostate Cancer

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adults suitable for radical image-guided radiotherapy with Prostate, Head \& Neck, Brain, or Lung Cancer. The variation in conditions is based on the requirements of Machine Learning algorithms requiring high levels of clinical applicability, which depends on the quality and quantity of the input data available. The input data set therefore should adequately encompass the variation in anatomy encountered in the population.

You may qualify if:

• 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

You may not qualify if:

• 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.

Sponsors & Collaborators

• CCTU- Cancer Theme: lead
• University of Cambridge: collaborator
• Microsoft Research: collaborator

Study Sites (1)

Cambridge University Hospitals NHS Foundation Trust

Cambridge, Cambridgeshire, CB2 0QQ, United Kingdom

RECRUITING

MeSH Terms

Conditions

Neoplasms

Study Officials

• Raj Dr. Jena

  Cambridge University Hospitals NHS Foundation Trust & the University of Cambridge

  PRINCIPAL INVESTIGATOR

• Suzanne Miller

  Cambridge University Hospitals NHS Foundation Trust

  PRINCIPAL INVESTIGATOR

• Amy Bates

  Cambridge University Hospitals NHS Foundation Trust

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Meena Murthy

CONTACT

01223 349707; meena.murthy@addenbrookes.nhs.uk

CCTU Cancer

CONTACT

01223 216038; cctuc@addenbrookes.nhs.uk

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 5 Years
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Dr. Raj Jena, Chief Investigator

Study Record Dates

• First Submitted: August 15, 2019
• First Posted: August 19, 2019
• Study Start: September 11, 2019
• Primary Completion: January 1, 2023
• Study Completion (Estimated): January 1, 2028
• Last Updated: August 2, 2021

Record last verified: 2021-07

Locations

GB (1)