protocolSection identificationModule nctId: NCT06329648, orgStudyIdInfo id: DECIDE, briefTitle: Distributed Learning of Edic and CardIac Dose Effects in Lung Cancer, acronym: DECIDE, statusModule overallStatus: NOT_YET_RECRUITING, startDateStruct date: 2024-06-01, primaryCompletionDateStruct date: 2025-12-31, completionDateStruct date: 2028-06-01, studyFirstPostDateStruct date: 2024-03-26, lastUpdatePostDateStruct date: 2024-03-26, sponsorCollaboratorsModule leadSponsor name: The Netherlands Cancer Institute, class: OTHER, descriptionModule briefSummary: Cardiac dose was not a major concern in lung radiotherapy patients until the results of the RTOG (Radiation Therapy Oncology Group) 0617 trial, which showed an association between cardiac dose and survival. Since then, many papers have studied the association between cardiac (substructure) dose and either survival or cardiac toxicity. Ideally, cardiac toxicity would be separated from survival. However, scoring cardiac toxicity prospectively was not standard practice, and retrospective scoring is challenging because of the overlap of cardiac toxicity symptoms and lung cancer (treatment) symptoms. Therefore in real world cohorts, cardiac toxicity is usually not scored properly and most larger studies pragmatically consider overall survival as primary endpoint, and the relation between cardiac dose and cardiac toxicity is not well established for lung cancer patients.Cardiac toxicity might not be the only factor in decreased survival; toxicity of the immune system might be a competing risk or a major contributing factor, where dose to the heart is a surrogate for dose to blood. Dose to the immune system is defined as EDIC (Effective Dose to circulating Immune Cells), comprising heart dose, lung dose and body dose combined. As EDIC dose and cardiac dose partly overlap, a large cohort with substantial variation will be required to disentangle the two effects. Such vast amounts of routine care data are immediately available in many radiotherapy centers all over the world. The problem we face is not the lack of routine care data, but making such data available for analysis. DECIDE adopts a federated learning approach, which implies that data does not have to be centralized within a single institution to be fit for use. We aim to include an unprecedentedly large-scale cohort of 20,000 patients.In this proposal, we need to add on scientific and technological innovations that exploit the existing federated learning framework to scale up to supporting \>25 simultaneously connected partners. We will be training (generalized) linear epidemiological models as well as new computer vision-based models for outcome predictions. As cause-specific survival (cardiac toxicity or immune toxicity) is unavailable or unreliable in major studies, we will use the more pragmatic endpoint of survival. By elucidating the clinical contributions of whole heart dose, cardiac substructure dose and EDIC dose in combination with known clinical risk factors, the desired impact is to change clinical practice for lung cancer radiotherapy and improve survival., conditionsModule conditions: NSCLC, designModule studyType: OBSERVATIONAL, designInfo observationalModel: COHORT, timePerspective: RETROSPECTIVE, enrollmentInfo count: 20000, type: ESTIMATED, outcomesModule primaryOutcomes measure: Optimize EDIC dose, secondaryOutcomes measure: cardiac toxicity, eligibilityModule sex: ALL, stdAges: CHILD, stdAges: ADULT, stdAges: OLDER_ADULT, contactsLocationsModule, hasResults: False