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Deep Learning-Based Long Term Mortality Prediction in the National Lung Screening Trial.
Lu, Yaozhi; Aslani, Shahab; Emberton, Mark; Alexander, Daniel C; Jacob, Joseph.
Afiliação
  • Lu Y; Centre for Medical Image Computing, University College London, London WC1V 6LJ, U.K.
  • Aslani S; Department of Computer Science, University College London, London WC1E 6BT, U.K.
  • Emberton M; Centre for Medical Image Computing, University College London, London WC1V 6LJ, U.K.
  • Alexander DC; Department of Respiratory Medicine, University College London, London WC1E 6BT, U.K.
  • Jacob J; Division of Surgery and Interventional Science, University College London, London W1W 7TS, U.K.
IEEE Access ; 10: 34369-34378, 2022.
Article em En | MEDLINE | ID: mdl-37810591
In this study, the long-term mortality in the National Lung Screening Trial (NLST) was investigated using a deep learning-based method. Binary classification of the non-lung-cancer mortality (i.e. cardiovascular and respiratory mortality) was performed using neural network models centered around a 3D-ResNet. The models were trained on a participant age, gender, and smoking history matched cohort. Utilising both the 3D CT scan and clinical information, the models can achieve an AUC of 0.73 which outperforms humans at cardiovascular mortality prediction. The corresponding F1 and Matthews Correlation Coefficient are 0.60 and 0.38 respectively. By interpreting the trained models with 3D saliency maps, we examined the features on the CT scans that correspond to the mortality signal. By extracting information from 3D CT volumes, we can highlight regions in the thorax region that contribute to mortality that might be overlooked by the clinicians. Therefore, this can help focus preventative interventions appropriately, particularly for under-recognised pathologies and thereby reducing patient morbidity.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: IEEE Access Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: IEEE Access Ano de publicação: 2022 Tipo de documento: Article