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

May 22, 2023 updated by: Xu jianmin, Fudan University

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

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

Study Overview

Status

Not yet recruiting

Intervention / Treatment

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.

Study Type

Interventional

Enrollment (Anticipated)

100

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

      • Shanghai, China, 200032
        • Zhongshan hosptial, Fudan University
    • Shanghai
      • Shanghai, Shanghai, China, 200032
        • Zhongshan Hospital, Fudan University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  1. Age 18-75 years;
  2. Histologically proven colorectal adenocarcinoma;
  3. Simultaneous liver-limited metastases;
  4. Initially unresectable liver metastases determined by a local MDT;
  5. Life expectancy of > 6 months;
  6. RAS and BRAF V600E wild-type;
  7. ECOG 0-1;
  8. Available CT imaging before treatment.

Exclusion Criteria:

  1. Previous systemic treatment for metastatic disease;
  2. Previous surgery for metastatic disease;
  3. Extrahepatic metastases;
  4. Unresectable primary tumor;
  5. Major cardiovascular events (myocardial infarction, severe/unstable angina, congestive heart failure, CVA) within 12 months before randomisation;
  6. Acute or subacute intestinal obstruction;
  7. Second primary malignancy within the past 5 years;
  8. Drug or alcohol abuse;
  9. No legal capacity or limited legal capacity;
  10. Uncontrolled hypertension, or unsatisfactory blood pressure control with ≥3 antihypertensive drugs;

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

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

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Active Comparator: ARM A
Chemotherapy regimens are determined based on the clinical experience of specialists

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

Experimental: ARM B
Chemotherapy regimens are determined based on the multimodal deep learning signature

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

What is the study measuring?

Primary Outcome Measures

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

Secondary Outcome Measures

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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Investigators

  • Principal Investigator: Jianmin Xu, MD, Fudan University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

June 1, 2023

Primary Completion (Anticipated)

June 1, 2024

Study Completion (Anticipated)

December 31, 2024

Study Registration Dates

First Submitted

February 17, 2023

First Submitted That Met QC Criteria

May 22, 2023

First Posted (Actual)

May 24, 2023

Study Record Updates

Last Update Posted (Actual)

May 24, 2023

Last Update Submitted That Met QC Criteria

May 22, 2023

Last Verified

June 1, 2022

More Information

Terms related to this study

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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