Multimodal Deep Learning Signature for Evaluation of Response to Bevacizumab in Patient With Colorectal Cancer Liver Metastasis

Sponsor
Fudan University
Study ID
NCT05354674
Status
Not Yet Recruiting

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Conditions

Eligibility Criteria

Sex
ALL
Age
18 Years - 79 Years
Healthy Volunteers
Not accepted

Interventions

  • Bevacizumab — DRUG
    AEM A：The specialist's decision to add bevacizumab to chemotherapy will be based on their own judgment ARM B：The patient's PET/CT imaging, pathology, and clinical information were input into the signature, and the FOLFOX+Bevacizumab regimen was selected when the output label was 1. FOLFOXIRI chemotherapy regimen was selected when the output label was 0

Study Details

Establishment and validation of the deep learning signature of bevacizumab efficacy in initially unresectable CRLM patients

Key Dates

First listed
Apr 29, 2022
Start date
Sep 1, 2023
Status verified
Jan 2023
Primary completion
Jul 1, 2025
Completion
Jul 1, 2028

Study Design

Enrollment
302 participants (estimated)

Arms

  • Arm: ARM A
    Chemotherapy regimens are determined based on the clinical experience of specialists
  • Arm: ARM B
    Chemotherapy regimens are determined based on the multimodal deep learning signature

Primary Outcome Measure

response rate [ Time Frame: 6 months ]

Central Contacts

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