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Heuristics, Algorithms and Machine Learning: Evaluation & Testing in Radiation Therapy (Hamlet rt)

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ClinicalTrials.gov Identifier: NCT04060706
Recruitment Status : Not yet recruiting
First Posted : August 19, 2019
Last Update Posted : August 19, 2019
Sponsor:
Collaborators:
University of Cambridge
Microsoft Research
Information provided by (Responsible Party):
CCTU- Cancer Theme, Cambridge University Hospitals NHS Foundation Trust

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.


Condition or disease Intervention/treatment
Cancer Radiation: Radical Image-Guided Radiotherapy

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Study Type : Observational [Patient Registry]
Estimated Enrollment : 310 participants
Observational Model: Cohort
Time Perspective: Prospective
Target Follow-Up Duration: 5 Years
Official Title: Hamlet-RT: Heuristics, Algorithms and Machine Learning: Evaluation & Testing in Radiation Therapy
Estimated Study Start Date : January 1, 2020
Estimated Primary Completion Date : January 1, 2023
Estimated Study Completion Date : January 1, 2028

Group/Cohort Intervention/treatment
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
Questionnaires administered will monitor the clinical toxicity experienced by each patient up to 5 years post 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
Questionnaires administered will monitor the clinical toxicity experienced by each patient up to 5 years post 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
Questionnaires administered will monitor the clinical toxicity experienced by each patient up to 5 years post 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
Questionnaires administered will monitor the clinical toxicity experienced by each patient up to 5 years post radiotherapy




Primary Outcome Measures :
  1. Machine Learning Modelling [ Time Frame: 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

  2. Predictive Modelling [ Time Frame: 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

  3. Clinical Toxicity Evaluation [ Time Frame: 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



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
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.
Criteria

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.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04060706


Contacts
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Contact: Richard Skells 01223 349707 ext 349707 richard.skells@addenbrookes.nhs.uk
Contact: CCTU Cancer 01223 216038 ext 216038 cctuc@addenbrookes.nhs.uk

Locations
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United Kingdom
Cambridge University Hospitals NHS Foundation Trust Not yet recruiting
Cambridge, Cambridgeshire, United Kingdom, CB2 0QQ
Contact: Amy Bates    01223 256296 ext 256296    amy.bates@addenbrookes.nhs.uk   
Principal Investigator: Raj Dr. Jena         
Sponsors and Collaborators
CCTU- Cancer Theme
University of Cambridge
Microsoft Research
Investigators
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Principal Investigator: Raj Dr. Jena Cambridge University Hospitals NHS Foundation Trust & the University of Cambridge

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Responsible Party: CCTU- Cancer Theme, Dr. Raj Jena, Chief Investigator, Cambridge University Hospitals NHS Foundation Trust
ClinicalTrials.gov Identifier: NCT04060706     History of Changes
Other Study ID Numbers: Hamlet.rt
First Posted: August 19, 2019    Key Record Dates
Last Update Posted: August 19, 2019
Last Verified: August 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by CCTU- Cancer Theme, Cambridge University Hospitals NHS Foundation Trust:
Radiotherapy
Image-guided
Head & Neck
Brain
Lung
Prostate
Adults