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

arXiv: https://arxiv.org/abs/2303.10789 · HTML: https://mlsanity.com/paper/2303.10789/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network](https://mlsanity.com/paper/1711.08324/) (2017)
- [Longitudinal detection of radiological abnormalities with time-modulated LSTM](https://mlsanity.com/paper/1807.06144/) (2018)
- [Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection](https://mlsanity.com/paper/1909.05321/) (2019)
- [Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography](https://mlsanity.com/paper/2008.06997/) (2020)
- [TransforMesh: A Transformer Network for Longitudinal modeling of Anatomical Meshes](https://mlsanity.com/paper/2109.00532/) (2021)
- [Knowledge-based Analysis for Mortality Prediction from CT Images](https://mlsanity.com/paper/1902.07687/) (2019)
- [Direct Prediction of Cardiovascular Mortality from Low-dose Chest CT using Deep Learning](https://mlsanity.com/paper/1810.02277/) (2018)
- [Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data](https://mlsanity.com/paper/2203.11391/) (2022)

## Built on by (most cited first, 4 of 4)

- [Time-driven Survival Analysis from FDG-PET/CT in Non-Small Cell Lung Cancer](https://mlsanity.com/paper/2604.06885/) (2026)
- [CenTime: Event-Conditional Modelling of Censoring in Survival Analysis](https://mlsanity.com/paper/2309.03851/) (2023)
- [Classification based deep learning models for lung cancer and disease using medical images](https://mlsanity.com/paper/2507.01279/) (2025)
- [Deep Learning for Longitudinal Medical Imaging: A Scoping Review](https://mlsanity.com/paper/2610.08838/) (2026)

## Abstract (arXiv)

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.