CancerHamlet rtNCT04060706
Heuristics, Algorithms and Machine Learning: Evaluation & Testing in Radiation Therapy
An observational study of Cancer, run by CCTU- Cancer Theme at 1 site in United Kingdom.
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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cancer
- What is being tested?
- Radical Image-Guided Radiotherapy
- Type of treatment
- Surgery or procedure
- How many people
- 310 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 11 Sep 2019to 1 Jan 2028
- How long will I take part?
- 5 Years
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United Kingdom1 site, 1 recruiting
- Recruiting
Cambridge University Hospitals NHS Foundation Trust
Cambridge, Cambridgeshire, CB2 0QQ
Amy Bates, 01223 256296 ext. 256296, amy.bates@addenbrookes.nhs.uk
Raj Dr. Jena
About this study
Official title: Hamlet-RT: Heuristics, Algorithms and Machine Learning: Evaluation & Testing in Radiation Therapy
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
- Being tested
- Radical Image-Guided Radiotherapy
- Keywords
- Radiotherapy, Image-guided, Head & Neck, Brain, Lung, Prostate, Adults
- Medical subject headings
- Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Radical Image-Guided RadiotherapyRadiation
Questionnaires administered will monitor the clinical toxicity experienced by each patient up to 5 years post radiotherapy
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: 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. Receives: 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. Receives: Radiation: Radical Image-Guided Radiotherapy |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 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
Measured: 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
Measured: 8 years from FPFV
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Aug 2019
- Study start11 Sep 2019
- Primary completion1 Jan 2023 (estimated)
- Study completion1 Jan 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 310 participants (estimated)
- Target duration
- 5 Years
Sponsor and investigators
- Lead sponsor
- CCTU- Cancer Theme
- Sponsor type
- University, hospital or other
- Collaborators
- University of Cambridge, Microsoft Research
- Responsible party
- Sponsor-investigator, CCTU- Cancer Theme, Cambridge University Hospitals NHS Foundation Trust
- Investigators
- Raj Dr. Jena, Principal investigator, Cambridge University Hospitals NHS Foundation Trust & the University of CambridgeSuzanne Miller, Principal investigator, Cambridge University Hospitals NHS Foundation TrustAmy Bates, Principal investigator, Cambridge University Hospitals NHS Foundation Trust
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04060706
- Sponsor's protocol ID
- Hamlet.rt
- Acronym
- Hamlet rt
- Status last verified
- Jul 2021
- First submitted
- 15 Aug 2019
- Last updated
- 2 Aug 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 2 Aug 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Hamlet-RT: Heuristics, Algorithms and Machine Learning: Evaluation & Testing in Radiation Therapy
- ClinicalTrials.gov ID
- NCT04060706
- Acronym / sponsor ID
- Hamlet rt / Hamlet.rt
- Conditions
- Cancer
- Keywords
- Radiotherapy, Image-guided, Head & Neck, Brain, Lung, Prostate, Adults
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 310 participants (estimated)
- Lead sponsor
- CCTU- Cancer ThemeUniversity, hospital or other
- Collaborators
- University of Cambridge, Microsoft Research
- Study start
- 11 Sep 2019 (actual)
- Primary completion
- 1 Jan 2023 (estimated)
- Study completion
- 1 Jan 2028 (estimated)
- First posted
- 19 Aug 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 5 Years
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Prostate Cancer | – | Radiation: Radical Image-Guided RadiotherapyAdults 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. |
| Head & Neck Cancer | – | Radiation: Radical Image-Guided RadiotherapyAdults 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. |
| Central Nervous System Tumours | – | Radiation: Radical Image-Guided RadiotherapyAdults 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. |
| Lung Cancer | – | Radiation: Radical Image-Guided RadiotherapyAdults 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (8 listed)
- 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
Key exclusion criteria (1 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Radical Image-Guided RadiotherapyQuestionnaires administered will monitor the clinical toxicity experienced by each patient up to 5 years post radiotherapy | Radiation | Central Nervous System Tumours, Head & Neck Cancer, Lung Cancer, Prostate Cancer |
Primary outcome
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
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
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Aug 2021.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Raj Dr. Jena principal investigator
Cambridge University Hospitals NHS Foundation Trust & the University of Cambridge
Suzanne Miller principal investigator
Cambridge University Hospitals NHS Foundation Trust
Amy Bates principal investigator
Cambridge University Hospitals NHS Foundation Trust
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cancer, run by CCTU- Cancer Theme at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 2 Aug 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Meena Murthy (01223 349707 ext. 349707, meena.murthy@addenbrookes.nhs.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Machine Learning Modelling (time frame: 8 years from FPFV). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Aug 2021.