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

Deep Radiomics-based Fusion Model Predicting Bevacizumab Treatment Response and Outcome in Patients With Colorectal Liver Metastases: a Multicenter Cohort Study

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.

Study Overview

• Status: Completed
• Conditions: The Patients With CRLM Who Benefit More From Bevacizumab
• Intervention / Treatment: 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 Type: Observational
• Enrollment (Actual): 307

Contacts and Locations

Study Locations

• China
  • Shanghai, China
    • Department of General Surgery, Zhongshan Hospital, Fudan University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• 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

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

4

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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
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 Names: 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 Names: deep learning model

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
ORR	Objective response rate of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX	2013.10.1-2023.1.1
PFS	Progression-free survival of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX	2013.10.1-2023.1.1

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
OS	Overall survival of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX	2013.10.1-2023.1.1

Collaborators and Investigators

• Sponsor: Fudan University
• Investigators: Principal Investigator: Jianmin Xu, MD, Fudan University

Study record dates

Study Major Dates

• Study Start (Actual): October 1, 2013
• Primary Completion (Actual): January 1, 2023
• Study Completion (Actual): January 1, 2023

Study Registration Dates

• First Submitted: August 29, 2023
• First Submitted That Met QC Criteria: August 29, 2023
• First Posted (Actual): September 5, 2023

Study Record Updates

• Last Update Posted (Actual): September 14, 2023
• Last Update Submitted That Met QC Criteria: September 13, 2023
• Last Verified: August 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Neoplasms; Neoplastic Processes; Neoplasm Metastasis
• Other Study ID Numbers: DERBY

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No