ArticleNature communications2025
Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- An RNase domain-dependent microRNA detection with DNA-spiked nanocage for accurate cancer diagnosis.Nature communications · 2026Article
- A unified vision-language model for precision oncology and biomarker prediction in neuroblastoma.Nature communications · 2026Article
- The seventh kidney cancer research summit: progress in accelerating cures.The oncologist · 2026Article
- The Role of AI in Clinical Trial Design and Scientific Writing.Cardiovascular and interventional radiology · 2026Review
- The role of AI in oncology: present applications and future horizons.NPJ precision oncology · 2026Review
- A Deep Learning-Based Multimodal Clinico-Histology-Genomic Prognostic Model in Prostate Cancer.Annals of surgical oncology · 2026Article
- Determinants of pancreatic tropism in metastatic renal cell carcinoma.JCI insight · 2026Article
- Cactus Thorn-Inspired Janus Nanofiber Membranes as a Water Diode for Light-Enhanced Diabetic Wound Healing.Nano-micro letters · 2026Article
- Deciphering the mechanistic landscape of immune checkpoint blockade in ccRCC: from molecular drivers to therapeutic responses.Frontiers in immunology · 2026Review
- Multimodal AI in precision medicine: linking omics, imaging and clinical decisions.American journal of clinical and experimental immunology · 2026Article
- AI-enabled virtual spatial proteomics from histopathology for interpretable biomarker discovery in lung cancer.Nature medicine · 2026Article
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 2025Review
- Radiologic, Pathologic, and Deep Learning Predictors of Response to Immune Checkpoint Blockade in Renal Cell Carcinoma Patients Undergoing Post-Treatment Nephrectomy.medRxiv : the preprint server for health sciences · 2025Article
- MorphoITH: a framework for deconvolving intra-tumor heterogeneity using tissue morphology.Genome medicine · 2025Article
- Artificial intelligence-driven pathomics in hepatocellular carcinoma: current developments, challenges and perspectives.Discover oncology · 2025Review
- Article
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Authors and funding
15 authors.
Funding
Abstract
Anti-angiogenic (AA) therapy is a cornerstone of metastatic clear cell renal cell carcinoma (ccRCC) treatment, but not everyone responds, and predictive biomarkers are lacking. CD31, a marker of vasculature, is insufficient, and the Angioscore, an RNA-based angiogenesis quantification method, is costly, associated with delays, difficult to standardize, and does not account for tumor heterogeneity. Here, we developed an interpretable deep learning (DL) model that predicts the Angioscore directly from ubiquitous histopathology slides yielding a visual vascular network (H&E DL Angio). H&E DL Angio achieves a strong correlation with the Angioscore across multiple cohorts (spearman correlations of 0.77 and 0.73). Using this approach, we found that angiogenesis inversely correlates with grade and stage and is associated with driver mutation status. Importantly, DL Angio expediently predicts AA response in both a real-world and IMmotion150 trial cohorts, out-performing CD31, and closely approximating the Angioscore (c-index 0.66 vs 0.67) at a fraction of the cost.
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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 Socratic graph.