Rectal Cancer
Conditions
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.
Detailed description
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.
Interventions
OTHERArtificial Intelligence
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
Sponsors
Università Politecnica delle Marche
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
(Justification in parenthesis): * Age ≥18 years (RC and EC are primarily adult-onset cancers, and adult inclusion aligns with ethical biospecimen collection and consent processes.) * 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.) * Optional Inclusion: Access to tumor tissue (archival or fresh) for multi-omic profiling (Supports deep integrative biomarker discovery.)
Exclusion criteria
* 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.)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Primary outcomes | 24 months | 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) |
Contacts
Primary ContactMonica Ortenzi, PhD
Outcome results
None listed