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

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.

Inclusion criteria

• Age ≥ 18 years and ≤75 years;
• Patients were histologically confirmed for colorectal adenocarcinoma with unresectable liver-limited or liver-dominant metastases
• PET/CT at baseline were available
• First line treated with FOLFOX+ bevacizumab.

Exclusion criteria

• Resectable liver metastases;
• Wide-type KRAS/NRAS;
• No measurable liver metastasis;
• No efficacy assessment;
• No follow-up information.