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

• ClinicalTrials.gov Identifier: NCT06023173
• Recruitment Status: Completed
• First Posted: September 5, 2023
• Last Update Posted: September 14, 2023
• Sponsor: Fudan University
• Information provided by (Responsible Party): Xu jianmin, Fudan University

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
The Patients With CRLM Who Benefit More From Bevacizumab	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, the investigators proposed a deep radiomics-based fusion model which demonstrates high accuracy in predicting the efficacy of bevacizumab in CRLM patients. Further, the investigators 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: October 1, 2013
• Actual Primary Completion Date: January 1, 2023
• Actual Study Completion Date: January 1, 2023

Groups and Cohorts

Group/Cohort	Intervention/treatment
Training Cohort; This cohort was derived from Arm A (treated with FOLFOX + bevacizumab) of the BECOME 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 Name: deep learning model
Negative Validation Cohort; The cohort was derived from Arm B (treated with FOLFOX) of the BECOME 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 Name: 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 Name: 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 Name: deep learning model

Outcome Measures

Primary Outcome Measures :

1. ORR [ Time Frame: 2013.10.1-2023.1.1 ]
   Objective response rate of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX
2. PFS [ Time Frame: 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 [ Time Frame: 2013.10.1-2023.1.1 ]
   Overall survival of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX

Eligibility Criteria

• Ages Eligible for Study: 18 Years to 75 Years (Adult, Older Adult)
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No
• Sampling Method: Non-Probability Sample

Study Population

In this multicenter cohort study, the investigators collected 307 patients with colorectal cancer liver metastases. The training cohort and negative validation cohort were derived from the BECOME study (NCT01972490), for whom baseline PET/CT images were available. The internal validation cohort was derived from consecutive metastastic colorectal cancer patients of the multi-disciplinary team (MDT) at Zhongshan Hospital (ZSH), share the same MDT, surgical team, and PET/CT imaging equipment with training cohort, from 01 January 2018 to 31 December 2018. The external validation cohort came from the MDT of Zhongshan Hospital - Xiamen and the First Hospital of Wenzhou Medical University, from 01 January 2020 to 31 December 2020

Criteria

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

China

Department of General Surgery, Zhongshan Hospital, Fudan University

Shanghai, China

Sponsors and Collaborators

Fudan University

Investigators

• Principal Investigator: Jianmin Xu, MD; Fudan University

More Information

• Responsible Party: Xu jianmin, Head of Colorectal Surgery, Fudan University
• ClinicalTrials.gov Identifier: NCT06023173
• Other Study ID Numbers: DERBY
• First Posted: September 5, 2023
• Last Update Posted: September 14, 2023
• Last Verified: August 2023

Individual Participant Data (IPD) Sharing Statement:

• 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; Neoplastic Processes; Neoplasms; Pathologic Processes