[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100607512":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":10,"centralContacts":18,"locations":10,"responsibleParty":24,"collaborators":10,"id":28,"slug":29,"hasResults":30,"nctId":31,"briefTitle":32,"officialTitle":33,"acronym":34,"eligibilityCriteria":35,"healthyVolunteers":30,"sex":36,"minAge":37,"maxAge":10,"enrollmentInfo":38,"targetDuration":10,"studyType":41,"phases":10,"briefSummary":42,"conditions":43,"keywords":10,"overallStatus":45,"whyStopped":10,"lastUpdateSubmitDate":46,"lastUpdatePostDateStruct":47,"startDateStruct":50,"completionDateStruct":52,"leadSponsor":54,"locationsCount":10},{"fullName":5,"class":6},"Università Politecnica delle Marche","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"AI cohort",null,"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",[13],"Other: Artificial Intelligence",[15],{"type":6,"name":16,"description":11,"armGroupLabels":17,"otherNames":10},"Artificial Intelligence",[9],[19],{"name":20,"role":21,"phone":22,"phoneExt":10,"email":23},"Monica Ortenzi, PhD","CONTACT","+393924770853","monica.ortenzi@gmail.com",{"type":25,"investigatorFullName":26,"investigatorTitle":27,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Monica Ortenzi","Assistant Professor","100607512","1-safe-ai-onco-track-multimodal-genai-for-early-detection-of-minimal-residual-disease-and-recurrence-in-gastrointestinal-oncology-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":39,"type":40},700,"ESTIMATED","OBSERVATIONAL","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.",[44],"Rectal Cancer","NOT_YET_RECRUITING","2025-09-16",{"date":48,"type":49},"2025-09-24","ACTUAL",{"date":51,"type":40},"2026-06-01",{"date":53,"type":40},"2030-06-01",{"name":5,"class":6}]