NCT05873972

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

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

• Status: Unknown
• Phase: N/A
• Study type: Interventional
• Enrollment: 100 (estimated)
• Sponsor: Fudan University · Academic / Other
• Sex: All
• Age: 18 Years – 75 Years
• Healthy volunteers: Not accepted

Summary

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

Detailed description

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.

Conditions

• Colorectal Cancer Liver Metastases

Interventions

Type	Name	Description
DRUG	Cetuximab	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

Timeline

• Start date: 2023-06-01
• Primary completion: 2024-06-01
• Completion: 2024-12-31
• First posted: 2023-05-24
• Last updated: 2023-05-24

Locations

2 sites across 1 country: China