A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study

Researchers from Satsuma Lab have introduced a hybrid approach combining Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to predict long-term survival in a Lung Cancer Screening (LCS) study. It was demonstrated that incorporating the patient’s imaging follow-up history can lead to improvement in survival prediction. Delineating subjects at increased risk of cardiorespiratory mortality can alert clinicians to request further more detailed functional or imaging studies to improve the assessment of cardiorespiratory disease burden. Such strategies may uncover unsuspected and under-recognised pathologies thereby potentially reducing patient morbidity.

Further details can be found in Lu et al., 2023.