ReviewInternational journal of molecular sciences2026
Mechano-Organ-on-Chip for Cancer Research.
Review in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Integration of Artificial Intelligence and Microfluidics for Drug Delivery Applications.Micromachines · 2026Review
- Organoid technology in cancer research.Molecular biomedicine · 2026Review
- Human iPSC-derived and conventional cancer models in precision oncology: advancing patient-specific therapies from bench to bedside.Journal of experimental & clinical cancer research : CR · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mechano-Organ-on-Chip (Mechano-OoC) platforms are emerging as powerful microphysiological systems that place mechanical cues at the center of tumor modeling, providing a scalable and human-relevant approach to recapitulate the biophysical complexity of the tumor microenvironment. Mechanical factors such as matrix stiffness, viscoelasticity, solid stress, interstitial flow, confinement, and shear critically regulate cancer progression, metastasis, immune interactions, and treatment response, yet remain poorly captured by conventional in vitro models and are often studied separately in tumor-on-chip and mechanobiology research. In this review, we summarize recent advances in mechano-OoC technologies for cancer research, highlighting strategies that integrate engineered mechanical cues with microfluidics, tunable extracellular matrices, vascular and stromal interfaces, and dynamic loading to model tumor invasion, vascular transport, immune trafficking, and drug delivery. We also discuss emerging approaches for real-time, multimodal readouts, including sensor-integrated platforms and artificial intelligence-assisted data analysis, and outline key challenges that limit translation, such as device complexity, limited throughput, insufficient standardization, and inadequate validation against in vivo and clinical data. By organizing progress across platform engineering, sensing and readout, data standardization, and AI-driven analytics, this review provides a unified framework for advancing mechanobiology-aware tumor models and guiding the development of predictive preclinical platforms for precision cancer therapy.
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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.