1. SAFE-AI ONCO-TRACK: Multimodal GenAI for Early Detection of Minimal Residual Disease and Recurrence in Gastrointestinal Oncology

NCT07189520 | Not Yet Recruiting DMC

• 2 other identifiers: SAFE-AI ONCO-TRACK2025-TOOL-01-03
• Study Type: observational
• Target: 700
• Locations: 0 countries
• Sites: N/A

Brief Summary

Current decision tools (TNM, MRI/PET, 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/tissue multi-omics with radiology and clinical trajectories to: (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. The 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/MDR expectations.

Conditions

Rectal Cancer

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (1)

AI cohort

Benchmark AI scoring vs expert raters (GEARS/OCHRA κ ≥0.75)• Assess performance gains after GenAI feedback (≥15% improvement)• Measure usability, cognitive load, and ecological footprint reduction

Other: Artificial Intelligence

Interventions

Artificial Intelligence OTHER

Benchmark AI scoring vs expert raters (GEARS/OCHRA κ ≥0.75)• Assess performance gains after GenAI feedback (≥15% improvement)• Measure usability, cognitive load, and ecological footprint reduction

AI cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Cancer patients

You may qualify if:

• Histologically confirmed diagnosis of rectal or esophageal cancer (Confirms clinical relevance and eligibility for standard treatment pathways.)
• Treatment plan includes surgical resection with curative intent (Ensures applicability to MRD and outcome prediction tasks.)
• Undergoing standard-of-care neo-adjuvant or perioperative therapy (Ensures data consistency and relevance to response modelling.)
• Ability and willingness to provide informed consent for biospecimen and clinical data use (Meets ethical requirements for participation.)
• Availability for longitudinal blood sampling at T0 (baseline), T1 (3 months post-treatment), and T2 (6 months post-treatment) (Critical for temporal biomarker analysis.)

You may not qualify if:

• Diagnosis of non-resectable or metastatic disease at enrollment (Excludes non-curative settings where the longitudinal biomarker protocol may not be feasible.)
• Emergency surgeries or treatment plans that deviate from standard protocols (To maintain data comparability.)
• Inability or refusal to provide informed consent (Essential for ethical compliance.)
• Failure to complete biospecimen donation or key follow-up timepoints (Maintains data integrity and model reliability.)

Sponsors & Collaborators

• Università Politecnica delle Marche: lead

Biospecimen

Retention: SAMPLES WITH DNA

consistent prognostic value for minimal residual disease (MRD) and recurrence risk. However, most available studies are heterogeneous in assays, sampling timepoints, and outcome definitions, and are predominantly retrospective or single-centre, which limits their generalizability and clinical utility.

MeSH Terms

Conditions

Rectal Neoplasms

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

Colorectal Neoplasms; Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Intestinal Diseases; Rectal Diseases

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Central Study Contacts

Monica Ortenzi, PhD

CONTACT

+393924770853; monica.ortenzi@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: September 16, 2025
• First Posted: September 24, 2025
• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): June 1, 2028
• Study Completion (Estimated): June 1, 2030
• Last Updated: September 24, 2025

Record last verified: 2025-09