[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100607512":3},{"organization":4,"outcomesModule":7,"designInfo":14,"detailedDescription":17,"studyPopulation":18,"armGroups":19,"interventions":25,"overallOfficials":13,"centralContacts":29,"locations":13,"responsibleParty":35,"collaborators":13,"id":39,"slug":13,"hasResults":40,"nctId":41,"briefTitle":42,"officialTitle":43,"acronym":44,"eligibilityCriteria":45,"healthyVolunteers":40,"sex":46,"minAge":47,"maxAge":13,"enrollmentInfo":48,"targetDuration":13,"studyType":51,"phases":13,"briefSummary":17,"conditions":52,"keywords":13,"overallStatus":54,"whyStopped":13,"lastUpdateSubmitDate":55,"lastUpdatePostDateStruct":56,"startDateStruct":59,"completionDateStruct":61,"leadSponsor":63,"locationsCount":13},{"fullName":5,"class":6},"Università Politecnica delle Marche","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":13},[9],{"measure":10,"description":11,"timeFrame":12},"Primary outcomes","Primary Objective A: Establish a generative AI-powered simulation ecosystem (SAFE-AI) for biomarker discovery, risk stratification, and safety testing in oncology through integration of synthetic data, 3D tumour models, and multi-omics datasets. (Threshold: AUC ≥0.80 (95% CI ±0.05) for 12-mo recurrence prediction; Model calibration slope ≥0.90)","24 months",null,{"allocation":13,"interventionModel":13,"interventionModelDescription":13,"primaryPurpose":13,"observationalModel":15,"timePerspective":16,"maskingInfo":13},"COHORT","PROSPECTIVE","Current decision tools (TNM, MRI\u002FPET, CEA, and other serum markers, as well as single-marker genomics) are insufficiently predictive of responders, fail to detect early MRD in many cases, and rarely connect molecular biology to dynamic perioperative data. SAFE-AI will build and validate multimodal, explainable GenAI models that fuse liquid\u002Ftissue multi-omics with radiology and clinical trajectories to:\n\n(i) detect MRD earlier, (ii) improve recurrence-risk calibration, and (iii) support non-invasive \"virtual biopsy\"-inferring tissue-level features from blood profiles, and vice-versa, to mitigate missing-modality gaps. This is grounded in the strong mechanistic premise that integrating heterogeneous molecular signals with imaging captures tumour-host biology more completely than single-modality assays, enabling actionable, calibrated risk estimates for rectal and oesophageal cancer.\n\nThe clinical hypothesis is that such integrated models can improve recurrence prediction by at least 20% over guideline baselines, with transparent uncertainty and bias monitoring to meet EU AI Act\u002FMDR expectations.","Cancer patients",[20],{"label":21,"type":13,"description":22,"interventionNames":23},"AI cohort","Benchmark AI scoring vs expert raters (GEARS\u002FOCHRA κ ≥0.75)• Assess performance gains after GenAI feedback (≥15% improvement)• Measure usability, cognitive load, and ecological footprint reduction",[24],"Other: Artificial Intelligence",[26],{"type":6,"name":27,"description":22,"armGroupLabels":28,"otherNames":13},"Artificial Intelligence",[21],[30],{"name":31,"role":32,"phone":33,"phoneExt":13,"email":34},"Monica Ortenzi, PhD","CONTACT","+393924770853","monica.ortenzi@gmail.com",{"type":36,"investigatorFullName":37,"investigatorTitle":38,"investigatorAffiliation":5,"oldNameTitle":13,"oldOrganization":13},"PRINCIPAL_INVESTIGATOR","Monica Ortenzi","Assistant Professor","100607512",false,"NCT07189520","1. SAFE-AI ONCO-TRACK: Multimodal GenAI for Early Detection of Minimal Residual Disease and Recurrence in Gastrointestinal Oncology","SAFE-AI ONCO-TRACK: Multimodal GenAI for Early Detection of Minimal Residual Disease and Recurrence in Gastrointestinal Oncology","ONCO-TRACK","Inclusion Criteria (Justification in parenthesis):\n\n* Age ≥18 years (RC and EC are primarily adult-onset cancers, and adult inclusion aligns with ethical biospecimen collection and consent processes.)\n* Histologically confirmed diagnosis of rectal or esophageal cancer (Confirms clinical relevance and eligibility for standard treatment pathways.)\n* Treatment plan includes surgical resection with curative intent (Ensures applicability to MRD and outcome prediction tasks.)\n* Undergoing standard-of-care neo-adjuvant or perioperative therapy (Ensures data consistency and relevance to response modelling.)\n* Ability and willingness to provide informed consent for biospecimen and clinical data use (Meets ethical requirements for participation.)\n* Availability for longitudinal blood sampling at T0 (baseline), T1 (3 months post-treatment), and T2 (6 months post-treatment) (Critical for temporal biomarker analysis.)\n* Optional Inclusion: Access to tumor tissue (archival or fresh) for multi-omic profiling (Supports deep integrative biomarker discovery.)\n\nExclusion Criteria:\n\n* Diagnosis of non-resectable or metastatic disease at enrollment (Excludes non-curative settings where the longitudinal biomarker protocol may not be feasible.)\n* Emergency surgeries or treatment plans that deviate from standard protocols (To maintain data comparability.)\n* Inability or refusal to provide informed consent (Essential for ethical compliance.)\n* Failure to complete biospecimen donation or key follow-up timepoints (Maintains data integrity and model reliability.)","ALL","18 Years",{"count":49,"type":50},700,"ESTIMATED","OBSERVATIONAL",[53],"Rectal Cancer","NOT_YET_RECRUITING","2025-09-16",{"date":57,"type":58},"2025-09-24","ACTUAL",{"date":60,"type":50},"2026-06-01",{"date":62,"type":50},"2030-06-01",{"name":5,"class":6}]