Understanding the PULSAR Effect in Combined Radiotherapy and Immunotherapy through Attention Mechanisms with a Transformer Model
This work addresses cancer treatment optimization for medical researchers, but it is incremental as it applies an existing method (transformer attention) to a new biomedical dataset.
The study tackled the problem of understanding interactions in combined PULSAR radiotherapy and PD-L1 blockade immunotherapy using a transformer model, resulting in semi-quantitative prediction of tumor volume trends and identification of potential causal relationships.
PULSAR (personalized, ultra-fractionated stereotactic adaptive radiotherapy) is the adaptation of stereotactic ablative radiotherapy towards personalized cancer management. For the first time, we applied a transformer-based attention mechanism to investigate the underlying interactions between combined PULSAR and PD-L1 blockade immunotherapy based on a murine cancer model (Lewis Lung Carcinoma, LLC). The proposed approach is able to predict the trend of tumor volume change semi-quantitatively, and excels in identifying the potential causal relationships through both self-attention and cross-attention scores.