ArticleFrontiers in digital health2022
Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation.
Article in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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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.
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Who cites it
81 citing papers in PubMed, 103 citations in OpenAlex.
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- Multi-omics approaches reveal erythroid progenitor cell in cancer: from passive bystander to active player.Oncogene · 2026Review
- Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.International journal of molecular sciences · 2026Review
- Digital Twin Technology In Radiology.Journal of imaging informatics in medicine · 2026Review
- Six artificial intelligence innovation strategies applied to autism spectrum disorder research: A narrative review.Pediatric investigation · 2026Review
- SparStVR - exploring sparse 3D histology data in virtual reality.Communications engineering · 2026Article
- Towards evolutionary guided precision medicine of acute myeloid leukemia and Fanconi anemia associated bone marrow failure.Stem cells translational medicine · 2026Article
- ShinyEvents: harmonizing longitudinal data for real-world survival estimation.NPJ precision oncology · 2026Article
- Drug-loaded nanoparticles for cancer therapy: A high-throughput multicellular agent-based modeling study.Journal of theoretical biology · 2026Article
- ZENN: A thermodynamics-inspired computational framework for heterogeneous data-driven modeling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A Large Concept Model for Mechanistic Simulation of Disease Trajectories: A Hypothesis-Generating Exemplar for Pediatric Acute Lymphoblastic Leukemia.Computational and structural biotechnology journal · 2026Article
- Targeting phosphofructokinase in cancer: integrating natural products for metabolic reprogramming and therapeutic innovation.Frontiers in pharmacology · 2026Review
- Shaping the future of multiple myeloma with artificial intelligence and digital twins: from concept to clinic.Frontiers in digital health · 2026Review
- Beyond static biomarkers: systems biology and AI for decoding cancer dynamics.Frontiers in systems biology · 2026Review
21 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
33 authors at 20 institutions in 2 countries.
Funding
Abstract
We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoire of observational data across multiple scales and modalities. In 2020, the US National Cancer Institute, and the US Department of Energy, through a trans-disciplinary research community at the intersection of advanced computing and cancer research, initiated team science collaborative projects to explore the development and implementation of predictive Cancer Patient Digital Twins. Several diverse pilot projects were launched to provide key insights into important features of this emerging landscape and to determine the requirements for the development and adoption of cancer patient digital twins. Projects included exploring approaches to using a large cohort of digital twins to perform deep phenotyping and plan treatments at the individual level, prototyping self-learning digital twin platforms, using adaptive digital twin approaches to monitor treatment response and resistance, developing methods to integrate and fuse data and observations across multiple scales, and personalizing treatment based on cancer type. Collectively these efforts have yielded increased insights into the opportunities and challenges facing cancer patient digital twin approaches and helped define a path forward. Given the rapidly growing interest in patient digital twins, this manuscript provides a valuable early progress report of several CPDT pilot projects commenced in common, their overall aims, early progress, lessons learned and future directions that will increasingly involve the broader research community.
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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.