Year: 2023
Type: article
Abstract: In this study, we present a hybrid CNN-RNN approach to investigate long-term survival of subjects in a lung cancer screening study. Subjects who died of cardiovascular and respiratory causes were identified whereby the CNN model was used to capture imaging features in the CT scans and the RNN model was used to investigate time series and thus global information. To account for heterogeneity in patients' follow-up times, two different variants of LSTM models were evaluated, each incorporating different strategies to address irregularities in follow-up time. The models were trained on subjects who underwent cardiovascular and respiratory deaths and a control cohort matched to participant age, gender, and smoking history. The combined model can achieve an AUC of 0.76 which outperforms humans at cardiovascular mortality prediction. The corresponding F1 and Matthews Correlation Coefficient are 0.63 and 0.42 respectively. The generalisability of the model is further validated on an 'external' cohort. The same models were applied to survival analysis with the Cox Proportional Hazard model. It was demonstrated that incorporating the follow-up history can lead to improvement in survival prediction. The Cox neural network can achieve an IPCW C-index of 0.75 on the internal dataset and 0.69 on an external dataset. 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.
Source: Heliyon
Institution: University College London
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,100
Funders: Oregon Health and Science University, Wellcome Trust, EU Joint Programme – Neurodegenerative Disease Research, UK Research and Innovation, Cancer Research UK, National Institute for Health and Care Research, Royal Academy of Engineering, University of Cambridge, University College London, University of Manchester, University College London Hospitals NHS Foundation Trust, Knight Cancer Institute, Oregon Health and Science University, Medical Research Council, Engineering and Physical Sciences Research Council, UCLH Biomedical Research Centre, University College London Hospitals Biomedical Research Centre, National Cancer Institute
Awards: G1019074163, G1833914674, Learning MRI and histology image mappings for cancer diagnosis and prognosis, G206799163, G2810537366, Medical image computing for next-generation healthcare technology, G3191056609, I-AIM: Individualised Artificial Intelligence for Medicine, G339059223, Assessing Placental Structure and Function by Unified Fluid Mechanical Modelling and in-vivo MRI, G3555719874, Real-Time Ultrasound Guided Abdominal Interventions Without a Tracking Device, G3953918217, G3958697817, G454368420, G4669776581, CCP in Synergistic Reconstruction for Biomedical Imaging, G5402890377, G5412564710, G5619513383, JPND: Stratification of presymptomatic amyotrophic lateral sclerosis: the development of novel imaging biomarkers, G6080853620, Computational Modelling and Inference of Neurodegenerative Disease Propagation, G6127694706, G6479250449, G6806397390, Context Aware Augmented Reality for Endonasal Endoscopic Surgery, G7838069348, G7860295317, G8057587666, Computer-based analysis of early fibrosing lung disease, Computational Modelling and Inference of Neurodegenerative Disease Propagation, Enabling the Development and Application of Artificial Intelligence in the NHS, Enabling Clinical Decisions From Low-power MRI In Developing Nations Through Image Quality Transfer, G8807293646, G8849905504, G995500112
OpenAlex ID: W4385543554