This study aims to build a multimodal model based on CT images and liquid biopsy to accurately predict the survival of lung cancer patients. Data of 500 lung cancer patients were collected, CT images were standardized and 120 imaging features were innovatively extracted, and advanced second-generation sequencing technology was used to detect liquid biopsy indicators such as ctDNA. A fusion strategy based on the attention mechanism was used to construct a multimodal model, and performance was evaluated after training and optimization. The results showed that the model had an accuracy of 85% in predicting lung cancer survival, with a consistency index (C-index) of 0.82. It performed well in different stages of lung cancer, and the accuracy was improved by 15% compared with the single-modal model. Although the multimodal model is affected by data heterogeneity, it shows great potential in the personalized treatment of lung cancer. In the future, it is necessary to expand the sample size and multi-center verification to further improve the model performance.
Liyan Zhong (Mon,) studied this question.