Abstract
INTRODUCTION: Bevacizumab, an angiogenesis inhibitor, is commonly used alongside chemotherapy for metastatic colorectal cancer (mCCR). While inducing necrosis in tumours, bevacizumab may also lead to atrophy in tumour-free organs. Artificial intelligence (AI) models offer user-friendly methods for measuring organ volumes. This study explores the relationship between bevacizumab-induced atrophy using AI-assisted volume measurement in tumour-free organs and treatment efficacy. METHODS: This multicenter retrospective study includes patients from the PRODIGE 9 and PRODIGE 20 trials. Organ atrophy was assessed by evaluating volume changes from diagnosis to two months after treatment initiation in patients receiving bevacizumab compared to those who did not. Statistical analyses were performed using the Wilcoxon test, with correlations between volumetric changes. Overall and progression-free survival were assessed using log-rank tests and Cox regression models. RESULTS: Among the 214 patients included, 192 received bevacizumab. Both liver and spleen volumes were measured using a deep learning-based AI model and manual measurements. AI-generated volume measurements showed a strong correlation with manual measurements (Pearson coefficient > 0.8). Bevacizumab-treated patients exhibited significant atrophy of non-tumoural liver volume (p = 0.0378), while no significant changes were observed in tumour or spleen volumes in either group. Survival analyses revealed that patients with a smaller decrease in non-tumoural liver volume had improved overall survival (p = 0.016), although this association became non-significant after adjusting for age, sex, and tumour volume at diagnosis (p = 0.25). CONCLUSION: Our findings support the feasibility and reliability of AI in organ volume measurement. While bevacizumab exposure was linked to non-tumoural liver atrophy, its impact on survival remains inconclusive after adjustment. These results pave the way for further research into bevacizumab-induced organ atrophy and the potential of AI in personalizing oncology treatments.