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Deep Radiomics-based Fusion Model Predicting Bevacizumab Treatment Response and Outcome in Patients With Colorectal Liver Metastases

Sponsor
Fudan University (Other)
Overall Status
Completed
CT.gov ID
NCT06023173
Collaborator
(none)
307
Enrollment
1
Location
111
Actual Duration (Months)
2.8
Patients Per Site Per Month

Study Details

Study Description

Brief Summary

This multi-modal deep radiomics model, using PET/CT, clinical and histopathological data, was able to identify patients with bevacizumab-sensitive unresectable colorectal cancer liver metastases, providing a favorable approach for precise patient treatment.

Condition or Disease Intervention/Treatment Phase
  • Diagnostic Test: Deep radiomics-based fusion model

Detailed Description

Accurately predicting tumor response to targeted therapies is essential for guiding personalized conversion therapy in patients with unresectable colorectal cancer liver metastases (CRLM). Currently, tumor response evaluation criteria are based on assessments made after at least 2-months treatment. Consequently, there is a compelling need to develop baseline tools that can be used to guide therapy selection. Herein, we proposed a deep radiomics-based fusion model which demonstrates high accuracy in predicting the efficacy of bevacizumab in CRLM patients. Further, we observed a significant and positive association between the predicted-responders and longer progression-free survival as well as longer overall survival in CRLM patients treated with bevacizumab. Moreover, the model exhibits high negative prediction value, indicating its potential to accurately identify individuals who are unresponsive to bevacizumab. Thus, our model provides a valuable baseline method for specifically identifying bevacizumab-sensitive CRLM patients, which is offering a clinically convenient approach to guide precise patient treatment.

Study Design

Study Type:
Observational
Actual Enrollment :
307 participants
Observational Model:
Cohort
Time Perspective:
Retrospective
Official Title:
Deep Radiomics-based Fusion Model Predicting Bevacizumab Treatment Response and Outcome in Patients With Colorectal Liver Metastases: a Multicenter Cohort Study
Actual Study Start Date :
Oct 1, 2013
Actual Primary Completion Date :
Jan 1, 2023
Actual Study Completion Date :
Jan 1, 2023

Arms and Interventions

Arm Intervention/Treatment
Training Cohort

This cohort was derived from Arm A (treated with FOLFOX + bevacizumab) of the BEOME studyand was used for model construction.

Diagnostic Test: Deep radiomics-based fusion model
This multi-modal deep radiomics model, using PET/CT, clinical and histopathological data, was able to identify patients with bevacizumab-sensitive CRLM, providing a favorable approach for precise patient treatment.
Other Names:
  • deep learning model

    		•
    Negative Validation Cohort

    The cohort was derived from Arm B (treated with FOLFOX) of the BEOME study , which demonstrated that the model specifically predicted the efficacy of bevacizumab.

    Diagnostic Test: Deep radiomics-based fusion model
    This multi-modal deep radiomics model, using PET/CT, clinical and histopathological data, was able to identify patients with bevacizumab-sensitive CRLM, providing a favorable approach for precise patient treatment.
    Other Names:
  • deep learning model

    		•
    Internal Validation Cohort

    The cohort was derived from an independent Zhongshan Hospital cohort with the same treatment team and imaging instrumentation as the BECOME study, differing only in patient period, and was used for internal validation of the model.

    Diagnostic Test: Deep radiomics-based fusion model
    This multi-modal deep radiomics model, using PET/CT, clinical and histopathological data, was able to identify patients with bevacizumab-sensitive CRLM, providing a favorable approach for precise patient treatment.
    Other Names:
  • deep learning model

    		•
    External Validation Cohort

    The cohort was obtained from the Zhongshan Hospital - Xiamenand the First Affiliated Hospital of Wenzhou Medical University for external validation of the model.

    Diagnostic Test: Deep radiomics-based fusion model
    This multi-modal deep radiomics model, using PET/CT, clinical and histopathological data, was able to identify patients with bevacizumab-sensitive CRLM, providing a favorable approach for precise patient treatment.
    Other Names:
  • deep learning model

    		•

    Outcome Measures

    Primary Outcome Measures

    1. ORR [2013.10.1-2023.1.1]

      Objective response rate of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX

    2. PFS [2013.10.1-2023.1.1]

      Progression-free survival of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX

    Secondary Outcome Measures

    1. OS [2013.10.1-2023.1.1]

      Overall survival of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    18 Years to 75 Years
    Sexes Eligible for Study:
    All
    Accepts Healthy Volunteers:
    No
    Inclusion Criteria:

    1. Age ≥ 18 years and ≤75 years;

    2. Patients were histologically confirmed for colorectal adenocarcinoma with unresectable liver-limited or liver-dominant metastases

    3. PET/CT at baseline were available

    4. First line treated with FOLFOX+ bevacizumab.

    Exclusion Criteria:

    1. Resectable liver metastases;

    2. Wide-type KRAS/NRAS;

    3. No measurable liver metastasis;

    4. No efficacy assessment;

    5. No follow-up information.

    Contacts and Locations

    Locations

    Site City State Country Postal Code
    1 Department of General Surgery, Zhongshan Hospital, Fudan University Shanghai China

    Sponsors and Collaborators

    • Fudan University

    Investigators

    • Principal Investigator: Jianmin Xu, MD, Fudan University

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    Xu jianmin, Head of Colorectal Surgery, Fudan University
    ClinicalTrials.gov Identifier:
    NCT06023173
    Other Study ID Numbers:
    • DERBY
    First Posted:
    Sep 5, 2023
    Last Update Posted:
    Sep 5, 2023
    Last Verified:
    Aug 1, 2023
    Individual Participant Data (IPD) Sharing Statement:
    No
    Plan to Share IPD:
    No
    Studies a U.S. FDA-regulated Drug Product:
    No
    Studies a U.S. FDA-regulated Device Product:
    No
    Additional relevant MeSH terms:
    Neoplasm Metastasis
    Liver Neoplasms

    Study Results

    No Results Posted as of Sep 5, 2023