ArticleCancer research2017
Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis.
Article in Cancer research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
64 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review.Frontiers in oncology · 2025Pooled it
- Unlocking the prognostic power of pathomics in bladder cancer: a machine learning odyssey across multiple centers.BMC medical imaging · 2026Article
- Machine Learning-Based Pathomics Signature in Predicting MSH2 Expression and Prognosis in Gastric Cancer.Clinical and translational gastroenterology · 2026Article
- Melan-Dx: a knowledge-enhanced vision-language framework improves differential diagnosis of melanocytic neoplasm pathology.NPJ digital medicine · 2026Article
- AI in Cancer Prognosis: A Systematic Review of Multimodal Models Combining Pathology Images and High-Throughput Omics.Cancer informatics · 2026Article
- Multi-task adaptive deep sparse canonical correlation analysis for multi-omics cancer survival prediction.PloS one · 2026Article
- Multimodal deep learning for cancer prognosis prediction with clinical information prompts integration.NPJ digital medicine · 2025Article
- Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology.Cancer research · 2025Article
- Pathology-interpretable radiomic model for predicting clinical outcome in patients with osteosarcoma: a retrospective, multicenter study.BMC medicine · 2025Article
- A pathologist-AI collaboration framework for enhancing diagnostic accuracies and efficiencies.Nature biomedical engineering · 2025Article
- The Role of Maintaining Nutritional Adequacy Status and Physical Activity in Onco-Nephrology: Not a Myth Anymore, but a Reality.Nutrients · 2025Observational
- Integration of histopathological image features and multi-dimensional omics data in predicting molecular features and survival in glioblastoma.Frontiers in medicine · 2025Article
- Identification of gastric cancer subtypes based on disulfidptosis-related genes: GPC3 as a novel biomarker for prognosis prediction.Discover oncology · 2024Article
- A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.Nature cancer · 2024Article
- Article
- Machine learning-based pathomics signature of histology slides as a novel prognostic indicator in primary central nervous system lymphoma.Journal of neuro-oncology · 2024Article
- Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview.Journal of translational medicine · 2024Review
- Article
- Machine-learning methods based on the texture and non-texture features of MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer.Translational cancer research · 2023Article
- Improving Pancreatic Cyst Management: Artificial Intelligence-Powered Prediction of Advanced Neoplasms through Endoscopic Ultrasound-Guided Confocal Endomicroscopy.Biomimetics (Basel, Switzerland) · 2023Review
4 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (
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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.