Multimodal model fuses radiology-report semantics and host-response lab biomarkers to predict lung cancer survival, reporting C-indices of 0.920 (train) and 0.849 (test) in a retrospective two-center cohort of 574 patients.
Uncertainty-aware automatic TNM staging classification for [18F]FDG PET-CT reports for lung cancer utilising transformer-based language models and multi- task learning
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Radiology-Report Semantic Modelling and Host-Response Laboratory Biomarkers for Multimodal Survival Prediction in Lung Cancer
Multimodal model fuses radiology-report semantics and host-response lab biomarkers to predict lung cancer survival, reporting C-indices of 0.920 (train) and 0.849 (test) in a retrospective two-center cohort of 574 patients.