# Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer

arXiv: https://arxiv.org/abs/1901.01449 · HTML: https://mlsanity.com/paper/1901.01449/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2019.

## Built on by (most cited first, 8 of 8)

- [TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction](https://mlsanity.com/paper/2209.05036/) (2022)
- [A Federated Cox Model with Non-Proportional Hazards](https://mlsanity.com/paper/2207.05050/) (2022)
- [Deep Clustering Survival Machines with Interpretable Expert Distributions](https://mlsanity.com/paper/2301.11826/) (2023)
- [Learning Survival Distribution with Implicit Survival Function](https://mlsanity.com/paper/2305.14655/) (2023)
- [Confidence Intervals and Simultaneous Confidence Bands Based on Deep Learning](https://mlsanity.com/paper/2406.14009/) (2024)
- [Radiomics in Medical Imaging: Methods, Applications, and Challenges](https://mlsanity.com/paper/2602.00102/) (2026)
- [Graph-based multimodal multi-lesion DLBCL treatment response prediction from PET images](https://mlsanity.com/paper/2310.16863/) (2023)
- [Flexible Deep Neural Networks for Partially Linear Survival Data: Estimation and Survival Inference](https://mlsanity.com/paper/2512.10570/) (2025)

## Abstract (arXiv)

Recent radiomic studies have witnessed promising performance of deep learning techniques in learning radiomic features and fusing multimodal imaging data. Most existing deep learning based radiomic studies build predictive models in a setting of pattern classification, not appropriate for survival analysis studies where some data samples have incomplete observations. To improve existing survival analysis techniques whose performance is hinged on imaging features, we propose a deep learning method to build survival regression models by optimizing imaging features with deep convolutional neural networks (CNNs) in a proportional hazards model. To make the CNNs applicable to tumors with varied sizes, a spatial pyramid pooling strategy is adopted. Our method has been validated based on a simulated imaging dataset and a FDG-PET/CT dataset of rectal cancer patients treated for locally advanced rectal cancer. Compared with survival prediction models built upon hand-crafted radiomic features using Cox proportional hazards model and random survival forests, our method achieved competitive prediction performance.