ReviewNature reviews. Cancer2025
Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer.
Review 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 28 papers, 1 of them a synthesis that pooled it.
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
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Novel diagnostic and therapeutic strategies based on PANoptosis for hepatocellular carcinoma.Cancer biology & medicine · 2025Pooled it
- Design parameter effects on controlled drug delivery through implantable hydrogels.Drug delivery and translational research · 2026Article
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- Hydrogel-Mediated Delivery of Drug Nanocarriers in the Oedematous Postoperative Brain.Pharmaceutical research · 2026Article
- AI-orchestrated design-build-test-learn is the future of mammalian biodesign.Nature biomedical engineering · 2026Article
- Mapping radiosensitivity in glioblastoma using MR elastography and biomechanical modeling.Scientific reports · 2026Article
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Invest where impact begins: recommendations from Breast Cancer Research Foundation Early Career Investigator Working Group (Part 1 of 2).NPJ breast cancer · 2026Article
- Therapeutic Spp1 silencing in TREM2Nature cardiovascular research · 2026Article
- 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
- Article
- Improving severity grading of chemotherapy-induced myelosuppression in AML via data-driven and model-based deep learning.NPJ systems biology and applications · 2026Article
- ViVo: A Temporal Modeling Framework That Boosts Statistical Power and Minimizes Animal Usage.Journal of medicinal chemistry · 2026Article
- From images to physics-based computational models to digital twins: a framework for personalized cancer therapies.Frontiers in radiology · 2026Article
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Toward Safe and Effective Gene Therapy: Non-Viral Nanostructured Delivery Systems.International journal of nanomedicine · 2026Review
- AI-engineered multifunctional nanoplatforms: synergistically bridging precision diagnosis and intelligent therapy in next-generation oncology.Journal of nanobiotechnology · 2025Review
- Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making.Cancer research · 2025Review
- Artificial intelligence based quantification of T lymphocyte infiltrate predicts prognosis in high grade breast cancer using deep learning and statistical validation.Discover oncology · 2025Article
- Antibody-drug conjugates in cancer therapy: current landscape, challenges, and future directions.Molecular cancer · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Mathematical modelling has proven to be a valuable tool in predicting the delivery and efficacy of molecular, antibody-based, nano and cellular therapy in solid tumours. Mathematical models based on our understanding of the biological processes at subcellular, cellular and tissue level are known as mechanistic models that, in turn, are divided into continuous and discrete models. Continuous models are further divided into lumped parameter models - for describing the temporal distribution of medicine in tumours and normal organs - and distributed parameter models - for studying the spatiotemporal distribution of therapy in tumours. Discrete models capture interactions at the cellular and subcellular levels. Collectively, these models are useful for optimizing the delivery and efficacy of molecular, nanoscale and cellular therapy in tumours by incorporating the biological characteristics of tumours, the physicochemical properties of drugs, the interactions among drugs, cancer cells and various components of the tumour microenvironment, and for enabling patient-specific predictions when combined with medical imaging. Artificial intelligence-based methods, such as machine learning, have ushered in a new era in oncology. These data-driven approaches complement mechanistic models and have immense potential for improving cancer detection, treatment and drug discovery. Here we review these diverse approaches and suggest ways to combine mechanistic and artificial intelligence-based models to further improve patient treatment outcomes.
Indexed as
Identifiers
What 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.