mlsanityData to 14 Oct 2026

Deep learning-based survival prediction for multiple cancer types using histopathology images

arXiv 1912.07354 ↗First posted 16 Dec 2019q-bio.QM cs.LG eess.IVcited by 47

Not on the map. Why?

Abstract (arXiv)

Prognostic information at diagnosis has important implications for cancer treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic insights, improving risk stratification remains an active research area. We developed a deep learning system (DLS) to predict disease specific survival across 10 cancer types from The Cancer Genome Atlas (TCGA). We used a weakly-supervised approach without pixel-level annotations, and tested three different survival loss functions. The DLS was developed using 9,086 slides from 3,664 cases and evaluated using 3,009 slides from 1,216 cases. In multivariable Cox regression analysis of the combined cohort including all 10 cancers, the DLS was significantly associated with disease specific survival (hazard ratio of 1.58, 95% CI 1.28-1.70, p<0.0001) after adjusting for cancer type, stage, age, and sex. In a per-cancer adjusted subanalysis, the DLS remained a significant predictor of survival in 5 of 10 cancer types. Compared to a baseline model including stage, age, and sex, the c-index of the model demonstrated an absolute 3.7% improvement (95% CI 1.0-6.5) in the combined cohort. Additionally, our models stratified patients within individual cancer stages, particularly stage II (p=0.025) and stage III (p<0.001). By developing and evaluating prognostic models across multiple cancer types, this work represents one of the most comprehensive studies exploring the direct prediction of clinical outcomes using deep learning and histopathology images. Our analysis demonstrates the potential for this approach to provide prognostic information in multiple cancer types, and even within specific pathologic stages. However, given the relatively small number of clinical events, we observed wide confidence intervals, suggesting that future work will benefit from larger datasets.

Key facts. Cited by 47 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Dec 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 8; 2023: 17; 2024: 9; 2025: 5; 2026: 2.

47 papers.

Sort:

  1. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 359
  2. 2023Artificial Intelligence for Digital and Computational Pathologycited by 108
  3. 2023Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Predictioncited by 92
  4. 2023Cross-Modal Translation and Alignment for Survival Analysiscited by 70
  5. 2024Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathologycited by 67
  6. 2022Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathologycited by 58
  7. 2024Multimodal Prototyping for cancer survival predictioncited by 47
  8. 2023A General-Purpose Self-Supervised Model for Computational Pathologycited by 42
  9. 2021Self-supervised driven consistency training for annotation efficient histopathology image analysiscited by 39
  10. 2023CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Countingcited by 33
  11. 2021MHAttnSurv: Multi-Head Attention for Survival Prediction Using Whole-Slide Pathology Imagescited by 15
  12. 2021Deep Probability Estimationcited by 14
35 more
  1. 2024Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysiscited by 13
  2. 2023Masked Pre-Training of Transformers for Histology Image Analysiscited by 12
  3. 2022A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Predictioncited by 11
  4. 2023Topology-Guided Multi-Class Cell Context Generation for Digital Pathologycited by 7
  5. 2024PATHS: A Hierarchical Transformer for Efficient Whole Slide Image Analysiscited by 7
  6. 2024Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalitiescited by 6
  7. 2022Interpretable Prediction of Lung Squamous Cell Carcinoma Recurrence With Self-supervised Learningcited by 5
  8. 2023Deep Biological Pathway Informed Pathology-Genomic Multimodal Survival Predictioncited by 5
  9. 2023Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Imagescited by 5
  10. 2024Histopathological Image Classification with Cell Morphology Aware Deep Neural Networkscited by 5
  11. 2024NCT-CRC-HE: Not All Histopathological Datasets Are Equally Usefulcited by 5
  12. 2023Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosiscited by 4
  13. 2021Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learningcited by 3
  14. 2022Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unlabeled, unannotated pathology slidescited by 3
  15. 2022Weighted Concordance Index Loss-based Multimodal Survival Modeling for Radiation Encephalopathy Assessment in Nasopharyngeal Carcinoma Radiotherapycited by 3
  16. 2023Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathologycited by 3
  17. 2022MPVNN: Mutated Pathway Visible Neural Network Architecture for Interpretable Prediction of Cancer-specific Survival Riskcited by 2
  18. 2022Transformer-based Personalized Attention Mechanism for Medical Images with Clinical Recordscited by 2
  19. 2023Transformer-based end-to-end classification of variable-length volumetric datacited by 2
  20. 2025Multimodal Cancer Modeling in the Age of Foundation Model Embeddingscited by 2
  21. 2025PEaRL: Pathway-Enhanced Representation Learning for Gene and Pathway Expression Prediction from Histologycited by 2
  22. 2026GrapHist: Graph Self-Supervised Learning for Histopathologycited by 2
  23. 2021L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy na"ive breast cancer patientscited by 1
  24. 2021Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural Networkcited by 1
  25. 2024HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysiscited by 1
  26. 2024Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCTcited by 1
  27. 2025A Graph-Based Framework for Interpretable Whole Slide Image Analysiscited by 1
  28. 2025Cross-Cancer Knowledge Transfer in WSI-based Prognosis Predictioncited by 1
  29. 2023Case-Base Neural Networks: survival analysis with time-varying, higher-order interactionscited by 0
  30. 2023The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detectioncited by 0
  31. 2023Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathologycited by 0
  32. 2023Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentationcited by 0
  33. 2023Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival predictioncited by 0
  34. 2025Staining normalization in histopathology: Method benchmarking using multicenter datasetcited by 0
  35. 2026Biological Spatial Priors Regularize Foundation Model Representations for Cross-Site MSI Generalization in Colorectal Cancercited by 0