ReviewAdvanced healthcare materials2026
Engineering Complexity: Advances in 3D Breast Cancer Models for Precision Oncology.
Review in Advanced healthcare materials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- 3D Bioprinting for Tumor Microenvironment Reconstruction: Advances, Challenges, and Future Perspectives.Biotechnology journal · 2026Review
- Tumor microenvironment and key signaling pathways in breast cancer progression and therapy resistance: A review.Biomolecules & biomedicine · 2026Review
- Bioinks in systems biology: integrating biomaterial design with cellular network modelling for predictive biofabrication.Frontiers in bioengineering and biotechnology · 2026Review
- Engineering Complexity: Advances in 3D Breast Cancer Models for Precision Oncology.Advanced healthcare materials · 2026Review
- Constructing an ultrasound-assisted organoid model for tumor management.Discover oncology · 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
2 authors.
Funding
Abstract
Engineered in vitro cancer models are essential tools in cancer research, offering controlled microenvironments to study tumor biology and develop personalized therapies. Breast cancer, known for its complexity and high cellular heterogeneity, poses significant challenges in treatment. To address this, patient-derived in vitro breast cancer models are being used to better predict individual drug responses and guide therapy selection. Recent bioengineering efforts have focused on replicating key features of breast cancer in vitro, such as angiogenesis, tumor cell invasion, heterogeneity, interactions with the tumor microenvironment, and metastatic behavior. Technologies including organoids, microfluidic platforms, and 3D bioprinting technologies have enabled the creation of more biologically and physiologically relevant models. Each system offers specific strengths and limitations, and choosing the right model depends on the research question and desired complexity. This review highlights the current progress in engineering in vitro breast cancer models, comparing their capabilities and discussing innovations aimed at improving their biological accuracy and clinical relevance. Ongoing challenges and future directions are also addressed, with a focus on enhancing model fidelity to native breast cancer.
Indexed as
Identifiers
What Socratic 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 Socratic graph.