Multimodal Model for Efficacy Prediction Cetuximab in Colorectal Cancer Liver Metastasis Patient

Study Overview
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Brief Summary
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Establishment and validation of the deep learning model of Cetuximab efficacy in simultaneous RAS wild unresectable CRLM patients

Detailed Description
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Ras wild unresectable CRLM patients with primary tumor resection followed by Cetuximab in combination with chemotherapy were included in this study. The tumor response was assessed by local MDT group. Based on tumor response, almost 100 CRLM patients were classified into two groups (Clinician drived regimen vs Multi-omics model drived regimen). They will be the prospective cohort to validate our deep learning model for predicting Cetuximab efficacy.

Official Title
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Multimodal Deep Learning Radiomic Nomogram for Evaluation of Response to Cetuximab in Patient With Colorectal Cancer Liver Metastasis

Conditions
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Colorectal Cancer Liver Metastases

Intervention / Treatment
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* Drug: Cetuximab


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Age 18-75 years; Histologically proven colorectal adenocarcinoma; Simultaneous liver-limited metastases; Initially unresectable liver metastases determined by a local MDT; Life expectancy of > 6 months; RAS and BRAF V600E wild-type; ECOG 0-1; Available CT imaging before treatment. Exclusion Criteria: Previous systemic treatment for metastatic disease; Previous surgery for metastatic disease; Extrahepatic metastases; Unresectable primary tumor; Major cardiovascular events (myocardial infarction, severe/unstable angina, congestive heart failure, CVA) within 12 months before randomisation; Acute or subacute intestinal obstruction; Second primary malignancy within the past 5 years; Drug or alcohol abuse; No legal capacity or limited legal capacity; Uncontrolled hypertension, or unsatisfactory blood pressure control with ≥3 antihypertensive drugs;

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 75 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Treatment
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: ARM A<br>Chemotherapy regimens are determined based on the clinical experience of specialists | Drug: Cetuximab<br>* AEM A：The specialist's decision to add cetuximab to chemotherapy will be based on their own judgment ARM B：The patient's CT imaging,genetic mutation information were input into the signature, and the FOLFOX+cetuximab regimen was selected when the output label was 1. FOLFOX+bevacizumab chemotherapy regimen was selected when the output label was 0<br>|
| Experimental: ARM B<br>Chemotherapy regimens are determined based on the multimodal deep learning signature | Drug: Cetuximab<br>* AEM A：The specialist's decision to add cetuximab to chemotherapy will be based on their own judgment ARM B：The patient's CT imaging,genetic mutation information were input into the signature, and the FOLFOX+cetuximab regimen was selected when the output label was 1. FOLFOX+bevacizumab chemotherapy regimen was selected when the output label was 0<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| response rate | response rate will be assessed by local MDT | 6 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| progression free survival | progression free survival will be assessed by local MDT every two months during treatment, and telephone follow-up every three month after treatment | 3 years | 
| overall survival | overall survival will be assessed by researchers every two months during treatment, and telephone follow-up every three month after treatment | 3 years |