# CNN-based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging

arXiv: https://arxiv.org/abs/1906.10729 · HTML: https://mlsanity.com/paper/1906.10729/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [Deep Learning for Survival Analysis: A Review](https://mlsanity.com/paper/2305.14961/) (2023)
- [DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT](https://mlsanity.com/paper/2109.07711/) (2021)
- [AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images](https://mlsanity.com/paper/2305.09946/) (2023)
- [Survival-oriented embeddings for improving accessibility to complex data structures](https://mlsanity.com/paper/2110.11303/) (2021)
- [Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling](https://mlsanity.com/paper/2405.08780/) (2024)
- [Opportunistic Screening for Pancreatic Cancer using Computed Tomography Imaging and Radiology Reports](https://mlsanity.com/paper/2504.00232/) (2025)
- [Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer](https://mlsanity.com/paper/2509.23268/) (2025)

## Abstract (arXiv)

Cox proportional hazard model (CPH) is commonly used in clinical research for survival analysis. In quantitative medical imaging (radiomics) studies, CPH plays an important role in feature reduction and modeling. However, the underlying linear assumption of CPH model limits the prognostic performance. In addition, the multicollinearity of radiomic features and multiple testing problem further impedes the CPH models performance. In this work, using transfer learning, a convolutional neural network (CNN) based survival model was built and tested on preoperative CT images of resectable Pancreatic Ductal Adenocarcinoma (PDAC) patients. The proposed CNN-based survival model outperformed the traditional CPH-based radiomics approach in terms of concordance index by 22%, providing a better fit for patients' survival patterns. The proposed CNN-based survival model outperforms CPH-based radiomics pipeline in PDAC prognosis. This approach offers a better fit for survival patterns based on CT images and overcomes the limitations of conventional survival models.