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

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.

Study Overview

• Status: Recruiting
• Conditions: Cancer
• Intervention / Treatment: Radiation: Radical Image-Guided Radiotherapy

• Study Type: Observational
• Enrollment (Anticipated): 310

Contacts and Locations

• Study Contact: Name: Meena Murthy; Phone Number: 349707 01223 349707; Email: meena.murthy@addenbrookes.nhs.uk
• Study Contact Backup: Name: CCTU Cancer; Phone Number: 216038 01223 216038; Email: cctuc@addenbrookes.nhs.uk

Study Locations

• United Kingdom
  • Cambridgeshire
    • Cambridge, Cambridgeshire, United Kingdom, CB2 0QQ
      • Recruiting
      • Cambridge University Hospitals NHS Foundation Trust
      • Contact: Amy Bates; Phone Number: 256296 01223 256296; Email: amy.bates@addenbrookes.nhs.uk
      • Principal Investigator: Raj Dr. Jena

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: N/A
• Genders 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.

Description

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.

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

Number of groups / cohorts

4

Cohorts and Interventions

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: CCTU- Cancer Theme
• Collaborators: University of Cambridge; Microsoft Research
• Investigators: Principal Investigator: 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

Study record dates

Study Major Dates

• Study Start (Actual): September 11, 2019
• Primary Completion (Anticipated): January 1, 2023
• Study Completion (Anticipated): January 1, 2028

Study Registration Dates

• First Submitted: August 15, 2019
• First Submitted That Met QC Criteria: August 16, 2019
• First Posted (Actual): August 19, 2019

Study Record Updates

• Last Update Posted (Actual): August 2, 2021
• Last Update Submitted That Met QC Criteria: July 26, 2021
• Last Verified: July 1, 2021

More Information

Terms related to this study

• Keywords: Adults; Radiotherapy; Lung; Brain; Prostate; Head & Neck; Image-guided
• Other Study ID Numbers: Hamlet.rt

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: Undecided

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No