ArticleNature reviews. Cancer2025
Application of digital twins for personalized oncology.
Article in Nature reviews. Cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- AI-based augmentation of oncology clinical trials.Nature reviews. Clinical oncology · 2026Review
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- Integrating ex vivo platforms with AI to guide glioblastoma treatment.Journal of neuro-oncology · 2026Review
- Zebrafish against cancer: towards clinical relevance.Disease models & mechanisms · 2026Review
- From intravascular imaging to adaptive vascular care: intelligent photonics and digital twins in panvascular disease.Light, science & applications · 2026Review
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- From static prediction to dynamic cancer trajectories: Virtual Human Twins for breast cancer decision support.PLOS digital health · 2026Article
- Translational barriers to digital twins in radiation oncology.Physics and imaging in radiation oncology · 2026Article
- Preclinical and Virtual Models of Mucosal Melanoma: Bridging Translational Gaps in a Rare and Lethal Cancer.Pigment cell & melanoma research · 2026Review
- Expanding horizons: integrating multichannel deep learning predictors into patient-specific digital twins for precision lung cancer management.International journal of surgery (London, England) · 2026Article
- [Digital twins in uro-oncology].Urologie (Heidelberg, Germany) · 2026Review
- Reimagining human-centric drug development with new approach methodologies.Science (New York, N.Y.) · 2026Review
- Context defines precision: rethinking KRAS inhibition in oncology.NPJ precision oncology · 2026Review
- MS360°: a conceptual digital-first, data-driven hybrid care framework for personalised multiple sclerosis management.NPJ digital medicine · 2026Review
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- Multilayer network approaches to omics data integration in digital twins for cancer research.Frontiers in systems biology · 2026Review
- Digital twins for personalized treatment in uro-oncology in the era of artificial intelligence.Nature reviews. Urology · 2026Review
- Shaping the future of multiple myeloma with artificial intelligence and digital twins: from concept to clinic.Frontiers in digital health · 2026Review
- Role of health digital twins in oncology drug development - a primer.Frontiers in oncology · 2026Review
- Analysing the Structural Identifiability and Observability of Mechanistic Models of Tumour Growth.Bioengineering (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Identifiers
40634598What OpenQuestion holds
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.