The Use of Artificial Intelligence for the Prediction of Recurrence After Resection of Colorectal Liver Metastases

NCT07385521 | Recruiting

• 1 other identifier: AI-RECOLMET
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief Summary

Colorectal cancer is the third most common cancer worldwide and the fourth most common cause of cancer-related death. Survival is primarily determined by stage of disease and the presence of metastases. The combination of chemotherapy and liver resection remains the treatment option with the highest survival benefit for patients with liver metastases from colorectal cancer, with surgery still being the only recognized potential curative treatment; surgical locoregional treatment can also be combined with thermal ablation to enhance the possibility of complete liver clearance. Despite significant improvements in prognosis, a large proportion of patients (almost half) will still experience recurrence following treatment. There is a clinical need to identify a priori patients who are different likely to develop disease recurrence after locoregional treatment (liver resection ± thermal ablation) and to respond differently to chemotherapy, in order to refine risk-based allocation of treatments and resources. Widespread digitalization of healthcare generates a large amount of data, and together with today accessible high-performance computing, artificial intelligence technologies can be applied to overcome the current limitations in estimating colorectal cancer liver metastases recurrence and response to locoregional and chemotherapy treatments, thus achieving better treatment allocation than current practice. All radiomic features can also help in training the neural network aimed at detecting liver metastases before they become visually detectable by the radiologist. Therefore, this study aims to evaluate whether a multifactorial machine learning model (including clinical and radiomic) can identify patients with colorectal cancer liver metastases with a high risk of progression after chemotherapy and recurrence after liver resection

Conditions

Colorectal Liver Metastasis (CRLM); Liver Resection; Hepatectomy; Liver Ablation

Keywords

Colorectal liver metastases; Liver resection; Hepatectomy; Liver ablation; Machine learning (ML); Artificial Intelligence (AI)

Outcome Measures

Primary Outcomes (1)

• Development of an ML algorithm predicting which individuals diagnosed with CRLM are most likely to experience early recurrence of disease after liver resection.

  The primary endpoints of this clinical study are the sensitivity, specificity, and area under the Receiver Operating Characteristic (AUC-ROC) curve of the machine learning models in predicting oncological outcomes: early recurrence based on clinical and radiological features.

  6 months post-intervention

Secondary Outcomes (2)

• Development of an ML algorithm predicting which individuals diagnosed with CRLM are most likely to experience early recurrence of disease after liver resection

  Through study completion, an average of 18 months

• Development of a ML algorithm predicting which individuals diagnosed with CRLM are most likely to experience response of disease to neoadjuvant systemic chemotherapy

  Through study completion, an average of 18 months

Study Arms (1)

Patients with CRLM treated with liver resection (with or without liver ablation)

Patients with colorectal cancer liver metastases receiving liver resection (with or without liver ablation) with or without perioperative (pre- , post- or pre-post-) systemic chemotherapy.

Other: AI-analysis

Interventions

AI-analysis OTHER

The study will investigate machine learning models to predict recurrence after liver resection for CRLM

Patients with CRLM treated with liver resection (with or without liver ablation)

Eligibility Criteria

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

Study Population

Patients with colorectal liver metastases

You may qualify if:

• Pathologically confirmed diagnosis (at final pathology) of liver metastases from colon or rectal adenocarcinoma
• \> 6 months of follow-up
• no other concomitant neoplastic disease

Sponsors & Collaborators

• Francesco De Cobelli: lead

Study Sites (1)

Radiology Department

Milan, 20123, Italy

RECRUITING

Central Study Contacts

Francesco De Cobelli, MD

CONTACT

390226432529; trialcliniciradiologia@hsr.it

Stephanie Steidler, PhD

CONTACT

390226432529; trialcliniciradiologia@hsr.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: MD, Director Radiology Department, IRCCS Ospedale San Raffaele

Study Record Dates

• First Submitted: January 22, 2026
• First Posted: February 4, 2026
• Study Start: February 19, 2025
• Primary Completion: February 19, 2026
• Study Completion (Estimated): February 19, 2027
• Last Updated: February 4, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)