# Image-based Survival Analysis for Lung Cancer Patients using CNNs

arXiv: https://arxiv.org/abs/1808.09679 · HTML: https://mlsanity.com/paper/1808.09679/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2018.

## Built on by (most cited first, 11 of 11)

- [Deep Learning for Survival Analysis: A Review](https://mlsanity.com/paper/2305.14961/) (2023)
- [A Deep Variational Approach to Clustering Survival Data](https://mlsanity.com/paper/2106.05763/) (2021)
- [DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis](https://mlsanity.com/paper/2202.07423/) (2022)
- [Survival Prediction in Lung Cancer through Multi-Modal Representation Learning](https://mlsanity.com/paper/2409.20179/) (2024)
- [Cross-Modality Masked Learning for Survival Prediction in ICI Treated NSCLC Patients](https://mlsanity.com/paper/2507.06994/) (2025)
- [Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment](https://mlsanity.com/paper/2508.02307/) (2025)
- [Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices](https://mlsanity.com/paper/2607.01001/) (2026)
- [RobSurv: Vector Quantization-Based Multi-Modal Learning for Robust Cancer Survival Prediction](https://mlsanity.com/paper/2505.02529/) (2025)
- [Spectral Survival Analysis](https://mlsanity.com/paper/2505.22641/) (2025)
- [Flexible Deep Neural Networks for Partially Linear Survival Data: Estimation and Survival Inference](https://mlsanity.com/paper/2512.10570/) (2025)
- [Structured Proxy Features for Multimodal NSCLC Survival Prediction from Pretreatment CT](https://mlsanity.com/paper/2608.00446/) (2026)

## Abstract (arXiv)

Traditional survival models such as the Cox proportional hazards model are typically based on scalar or categorical clinical features. With the advent of increasingly large image datasets, it has become feasible to incorporate quantitative image features into survival prediction. So far, this kind of analysis is mostly based on radiomics features, i.e. a fixed set of features that is mathematically defined a priori. To capture highly abstract information, it is desirable to learn the feature extraction using convolutional neural networks. However, for tomographic medical images, model training is difficult because on the one hand, only few samples of 3D image data fit into one batch at once and on the other hand, survival loss functions are essentially ordering measures that require large batch sizes. In this work, we show that by simplifying survival analysis to median survival classification, convolutional neural networks can be trained with small batch sizes and learn features that predict survival equally well as end-to-end hazard prediction networks. Our approach outperforms the previous state of the art in a publicly available lung cancer dataset.