The Use of Artificial Intelligence for the Prediction of Recurrence After Resection of Colorectal Liver Metastases
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorFrancesco De Cobelli
About this trial
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
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Pathologically confirmed diagnosis (at final pathology) of liver metastases from colon or rectal adenocarcinoma
> 6 months of follow-up
no other concomitant neoplastic disease
Disqualifiers
All subjects receiving hepatic resection but not fulfilling the inclusion criteria
Trial population
Patients with colorectal liver metastases
Trial design
Design model
Cohort
Time perspective
Retrospective
Treatments tested in this trial
AI-analysis
Other interventionThe study will investigate machine learning models to predict recurrence after liver resection for CRLM
Treatment groups
1,000 Participants
are divided into 1 treatment groupGroup A: Patients with CRLM treated with liver resection (with or without liver ablation)1 intervention
Trial outcomes
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.
Time frame
6 months post-interventionSecondary 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
Sensitivity, specificity, and area under the Receiver Operating Characteristic (AUC-ROC) curve of the machine learning models in predicting time to recurrence, disease free survival (DFS), progression free survival (PFS), recurrence free survival (RFS), overall survival (OS), based on clinical and radiological features
Time frame
Through study completion, an average of 18 months2
Development of a ML algorithm predicting which individuals diagnosed with CRLM are most likely to experience response of disease to neoadjuvant systemic chemotherapy
Sensitivity, specificity, and area under the Receiver Operating Characteristic (AUC-ROC) curve of the machine learning models in predicting PFS and OS based on the pre-treatment contrast-enhanced CT or MRI scan
Time frame
Through study completion, an average of 18 monthsSponsors and contacts
Click on the lead sponsor to view all of their trials.
Francesco De Cobelli
Lead sponsor
IRCCS San Raffaele
Sponsor institution
Source ClinicalTrials.gov