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

• URL: http://arxiv.org/abs/2303.10789v1
• Date: Sun, 19 Mar 2023 23:00:41 GMT
• Title: A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study
• Authors: Yaozhi Lu, Shahab Aslani, An Zhao, Ahmed Shahin, David Barber, Mark Emberton, Daniel C. Alexander, Joseph Jacob
• Abstract summary: We present a hybrid CNN-RNN approach to investigate long-term survival of subjects in a lung cancer screening study. The models were trained on subjects who underwent cardiovascular and respiratory deaths. The Cox neural network can achieve an IPCW C-index of 0.75 on the internal dataset and 0.69 on an external dataset.
• Score: 17.26942882598847
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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 imaging features associated with long-term survival can help focus preventative interventions appropriately, particularly for under-recognised pathologies thereby potentially reducing patient morbidity.

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