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

Fudan University

Status: Completed

Conditions: The Patients With CRLM Who Benefit More From Bevacizumab

Treatments: Diagnostic Test: Deep radiomics-based fusion model

Study type: Observational

Funder types: Other

Identifiers

NCT06023173

DERBY

Details and patient eligibility

About

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.

Full 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.

Enrollment: 307 patients

Sex: All

Ages: 18 to 75 years old

Volunteers: No Healthy Volunteers

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.

Trial design

307 participants in 4 patient groups

Training Cohort

Description:

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

Treatment:

Diagnostic Test: Deep radiomics-based fusion model

Negative Validation Cohort

Description:

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.

Treatment:

Diagnostic Test: Deep radiomics-based fusion model

Internal Validation Cohort

Description:

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.

Treatment:

Diagnostic Test: Deep radiomics-based fusion model

External Validation Cohort

Description:

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

Treatment:

Diagnostic Test: Deep radiomics-based fusion model