Artificial intelligence to predict cancer recurrence after liver surgery for colorectal metastases
A Retrospective Observational Study to Use Artificial Intelligence for Prediction of Disease REcurrence of COlorectal Cancer Liver METastasis After Hepatic Resection
IRCCS San Raffaele · NCT07385521
This project will try using AI on clinical and imaging data to predict which patients with colorectal cancer liver metastases are likely to have the cancer come back after liver resection or ablation.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 1000 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | IRCCS San Raffaele (other) |
| Drugs / interventions | chemotherapy, radiation |
| Locations | 1 site (Milan) |
| Trial ID | NCT07385521 on ClinicalTrials.gov |
What this trial studies
This is an observational project that applies AI analysis to clinical, imaging, and pathology data from patients who underwent liver resection with or without thermal ablation for colorectal liver metastases. Eligible cases have pathologically confirmed colorectal liver metastases and at least six months of follow-up; the AI will be trained to identify patterns associated with post-treatment recurrence. The work aims to develop and validate predictive models rather than change patient treatment during the observation period. Data are collected and analyzed at the Radiology Department in Milan to produce risk scores that could later inform clinical decision-making.
Who should consider this trial
Good fit: Ideal candidates are patients with pathologically confirmed colorectal liver metastases who have undergone hepatic resection (with or without thermal ablation) and have at least six months of follow-up and no other active cancers.
Not a fit: Patients who did not have surgical resection or pathological confirmation, who have other concurrent malignancies, or who lack sufficient follow-up data are unlikely to benefit from the predictions generated by this project.
Why it matters
Potential benefit: If successful, the AI tool could help clinicians identify patients at higher risk of recurrence so follow-up and therapies can be tailored more precisely.
How similar studies have performed: Previous radiomics and machine-learning efforts in colorectal liver metastases have shown promising but variable results and generally require further external validation.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Pathologically confirmed diagnosis (at final pathology) of liver metastases from colon or rectal adenocarcinoma * \> 6 months of follow-up * no other concomitant neoplastic disease Exclusion Criteria: * All subjects receiving hepatic resection but not fulfilling the inclusion criteria
Where this trial is running
Milan
- Radiology Department — Milan, Italy (RECRUITING)
Study contacts
- Study coordinator: Francesco De Cobelli, MD
- Email: trialcliniciradiologia@hsr.it
- Phone: 390226432529
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Colorectal Liver Metastasis, Liver Resection, Hepatectomy, Liver Ablation, Colorectal liver metastases, Liver resection, Liver ablation, Machine learning