ReviewAPL bioengineering2025
Microfluidic lung cancer models: Bridging clinical treatment strategies and tumor microenvironment recapitulation.
Review in APL bioengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Microfluidic chips for decoding cancer-immune crosstalk in immunotherapy.Frontiers in immunology · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer accounting for a majority of cases. Despite advances in targeted therapies and immunotherapy, challenges such as tumor heterogeneity, resistance mechanisms, and limited preclinical models hinder treatment efficacy. Traditional cancer models, including 2D cell cultures and animal models, often fail to accurately replicate the lung's complex architecture, microenvironment, and biomechanical cues, leading to poor predictive performance in drug development. Microfluidic-based organ-on-a-chip technology offers a promising alternative by integrating human-derived cells with precisely controlled perfusion, mechanical cues, and tumor-stroma interactions in physiologically relevant 3D models. These platforms enable the study of lung cancer biology, drug responses, and patient-specific therapeutic outcomes with improved accuracy. In this review, we discuss recent advancements in microfluidic systems for recapitulating normal lung physiology and 3D lung cancer microenvironment, covering various microfluidic platforms with applications in disease modeling and drug testing. Unlike other review articles, we bring first-hand insights from clinicians about the current treatment practice for lung cancer and the clinical utilities of lung cancer-on-a-chip models, which bioengineers have been seeking. We also highlight the translational potential of these systems in personalized oncology and the need for interdisciplinary collaborations, particularly with clinicians, to enhance their clinical impact.
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.