ArticleProceedings of the National Academy of Sciences of the United States of America2018
Predicting cancer outcomes from histology and genomics using convolutional networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06717295 (The CCANED-CIPHER Study), which is not on this map. Cited by 469 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The CCANED-CIPHER Study: Early Cancer Detection and Treatment Response Monitoring Using AI-Based Platelet and Immune Cell Transcriptomic Profiling
Who cites it
469 citing papers in PubMed, 3 syntheses or guidelines pooled it, 1,034 citations in OpenAlex.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- AI predicting recurrence in non-muscle-invasive bladder cancer: systematic review with study strengths and weaknesses.Frontiers in oncology · 2024Pooled it
- Tumour-infiltrating lymphocytes: from prognosis to treatment selection.British journal of cancer · 2023Pooled it
- CLEAR-HPV: Interpretable concept discovery for human-papillomavirus-associated morphology in whole-slide histology.Patterns (New York, N.Y.) · 2026Article
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Deep Learning-Based Multimodal Fusion of Whole-Slide Images and RNA Sequencing Identifies Survival-Relevant Glioblastoma Clusters.Cancer medicine · 2026Article
- Review
- Development and multicohort external validation of a preoperative [18F]PSMA-1007 PET-derived deep learning score and multimodal model for predicting biochemical recurrence-free survival after radical prostatectomy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- End-to-end 2.5D multisequence-multichannel fusion model for preoperative survival prediction in glioma: a retrospective study.BMC medical imaging · 2026Article
- Treatment-aware deep learning enables counterfactual prediction of individual benefit from PARP inhibitors in ovarian cancer.NPJ digital medicine · 2026Article
- Evolution of artificial intelligence in breast cancer pathology: a bibliometric analysis.Discover oncology · 2026Article
- Robust artificial intelligence frameworks for lung cancer subtyping and malignancy detection on thoracic CT.Scientific reports · 2026Article
- Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma.Journal of ovarian research · 2026Article
- AI-genomics synergy for drug repurposing in breast cancer: an interpretability-driven framework.NPJ genomic medicine · 2026Review
- [Hierarchical attention-driven multiple instance learning for clear cell renal cell carcinoma grading and staging in digital pathology].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Traditional Chinese Medicine-Derived Active Ingredient and Formulation Therapy for Glioma: Multi-Target Mechanisms, Drug Delivery Systems, and Advances in Clinical Translational Research.Pharmaceuticals (Basel, Switzerland) · 2026Review
- A Synaptogenesis-Associated Histomorphologic Signature from H&E Whole-Slide Images Predicts Glioma Prognosis and IdentifiesInternational journal of molecular sciences · 2026Article
- Contrastive multimodal deep learning for survival prediction in grade 2/3 gliomas.JNCI cancer spectrum · 2026Article
- Radiogenomics deciphers tumor heterogeneity in hepatocellular carcinoma and combined hepatocellular-cholangiocarcinoma.Discover oncology · 2026Article
- Deep Learning Models: A Lens Worth Sharpening for Cancer Prognostication.Annals of surgical oncology · 2026Article
409 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how these survival convolutional neural networks (SCNNs) can integrate information from both histology images and genomic biomarkers into a single unified framework to predict time-to-event outcomes and show prediction accuracy that surpasses the current clinical paradigm for predicting the overall survival of patients diagnosed with glioma. We use statistical sampling techniques to address challenges in learning survival from histology images, including tumor heterogeneity and the need for large training cohorts. We also provide insights into the prediction mechanisms of SCNNs, using heat map visualization to show that SCNNs recognize important structures, like microvascular proliferation, that are related to prognosis and that are used by pathologists in grading. These results highlight the emerging role of deep learning in precision medicine and suggest an expanding utility for computational analysis of histology in the future practice of pathology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.