[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100367164":3},{"organization":4,"outcomesModule":7,"designInfo":20,"detailedDescription":19,"studyPopulation":23,"armGroups":24,"interventions":42,"overallOfficials":48,"centralContacts":57,"locations":69,"responsibleParty":89,"collaborators":93,"id":99,"slug":100,"hasResults":101,"nctId":102,"briefTitle":103,"officialTitle":104,"acronym":105,"eligibilityCriteria":106,"healthyVolunteers":101,"sex":107,"minAge":108,"maxAge":19,"enrollmentInfo":109,"targetDuration":112,"studyType":113,"phases":19,"briefSummary":114,"conditions":115,"keywords":117,"overallStatus":71,"whyStopped":19,"lastUpdateSubmitDate":125,"lastUpdatePostDateStruct":126,"startDateStruct":129,"completionDateStruct":131,"leadSponsor":133,"locationsCount":134},{"fullName":5,"class":6},"Cambridge University Hospitals NHS Foundation Trust","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":19,"otherOutcomes":19},[9,13,16],{"measure":10,"description":11,"timeFrame":12},"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",{"measure":14,"description":15,"timeFrame":12},"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",{"measure":17,"description":18,"timeFrame":12},"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",null,{"allocation":19,"interventionModel":19,"interventionModelDescription":19,"primaryPurpose":19,"observationalModel":21,"timePerspective":22,"maskingInfo":19},"COHORT","PROSPECTIVE","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.",[25,30,34,38],{"label":26,"type":19,"description":27,"interventionNames":28},"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.",[29],"Radiation: Radical Image-Guided Radiotherapy",{"label":31,"type":19,"description":32,"interventionNames":33},"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.",[29],{"label":35,"type":19,"description":36,"interventionNames":37},"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.",[29],{"label":39,"type":19,"description":40,"interventionNames":41},"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.",[29],[43],{"type":44,"name":45,"description":46,"armGroupLabels":47,"otherNames":19},"RADIATION","Radical Image-Guided Radiotherapy","Questionnaires administered will monitor the clinical toxicity experienced by each patient up to 5 years post radiotherapy",[35,31,39,26],[49,53,55],{"name":50,"affiliation":51,"role":52},"Raj Dr. Jena","Cambridge University Hospitals NHS Foundation Trust & the University of Cambridge","PRINCIPAL_INVESTIGATOR",{"name":54,"affiliation":5,"role":52},"Suzanne Miller",{"name":56,"affiliation":5,"role":52},"Amy Bates",[58,64],{"name":59,"role":60,"phone":61,"phoneExt":62,"email":63},"Meena Murthy","CONTACT","01223 349707","349707","meena.murthy@addenbrookes.nhs.uk",{"name":65,"role":60,"phone":66,"phoneExt":67,"email":68},"CCTU Cancer","01223 216038","216038","cctuc@addenbrookes.nhs.uk",[70],{"facility":5,"status":71,"city":72,"state":73,"zip":74,"country":75,"countryCode":76,"cosmosGeoPoint":77,"geoPoint":82,"contacts":83},"RECRUITING","Cambridge","Cambridgeshire","CB2 0QQ","United Kingdom","UK",{"type":78,"coordinates":79},"Point",[80,81],0.11667,52.2,{"lat":81,"lon":80},[84,88],{"name":56,"role":60,"phone":85,"phoneExt":86,"email":87},"01223 256296","256296","amy.bates@addenbrookes.nhs.uk",{"name":50,"role":52,"phone":19,"phoneExt":19,"email":19},{"type":90,"investigatorFullName":91,"investigatorTitle":92,"investigatorAffiliation":5,"oldNameTitle":19,"oldOrganization":19},"SPONSOR_INVESTIGATOR","CCTU- Cancer Theme","Dr. Raj Jena, Chief Investigator",[94,96],{"name":95,"class":6},"University of Cambridge",{"name":97,"class":98},"Microsoft Research","INDUSTRY","100367164","heuristics-algorithms-and-machine-learning-evaluation--testing-in-radiation-therapy-100367164",false,"NCT04060706","Heuristics, Algorithms and Machine Learning: Evaluation & Testing in Radiation Therapy","Hamlet-RT: Heuristics, Algorithms and Machine Learning: Evaluation & Testing in Radiation Therapy","Hamlet rt","Inclusion Criteria:\n\n* Participant is willing and able to give informed consent for participation in the study\n* Male or Female\n* Aged 18 years or older\n* Diagnosed with primary prostate cancer, head and neck cancer, lung cancer, or brain tumour\n* Treated with curative intent\n* Suitable for radical image guided radiotherapy\n* WHO ECOG performance status 0 or 1\n* Expected survival of 18 months or more\n\nExclusion Criteria:\n\n* 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.","ALL","18 Years",{"count":110,"type":111},310,"ESTIMATED","5 Years","OBSERVATIONAL","The Hamlet.rt study is a prospective data collection and patient questionnaire study for patients undergoing image-guided radiotherapy with curative intent.\n\nThe 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.\n\nA 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.",[116],"Cancer",[118,119,120,121,122,123,124],"Radiotherapy","Image-guided","Head & Neck","Brain","Lung","Prostate","Adults","2021-07-26",{"date":127,"type":128},"2021-08-02","ACTUAL",{"date":130,"type":128},"2019-09-11",{"date":132,"type":111},"2028-01-01",{"name":91,"class":6},1]