Evidence mapPaperPMID 37592292Full record

ReviewJournal of biological engineering2023

Cancer-on-chip: a 3D model for the study of the tumor microenvironment.

Elisa Cauli, Michela Anna Polidoro, Simona Marzorati, Claudio Bernardi, Marco Rasponi, Ana Lleo

Open access · goldAbstract readReview
In one paragraph

Review in Journal of biological engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
3.8field-weighted citation impact, top 6% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 35 citations in OpenAlex.

  1. Article
  2. Review
  3. Deep Learning-Powered Scalable Cancer Organ Chip for Cancer Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Elisa Cauli *Department of Electronics, Information and Bioengineering, Politecnico Di Milano, Milan, Italy. elisa.cauli@polimi.it.
Michela Anna Polidoro *Hepatobiliary Immunopathology Laboratory, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.
Simona MarzoratiHepatobiliary Immunopathology Laboratory, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.
Claudio BernardiAccelera Srl, Nerviano, Milan, Italy.
Marco RasponiDepartment of Electronics, Information and Bioengineering, Politecnico Di Milano, Milan, Italy.
Ana LleoDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
IRCCS Humanitas Research Hospital · ITPolitecnico di Milano · ITNerviano Medical Sciences · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The approval of anticancer therapeutic strategies is still slowed down by the lack of models able to faithfully reproduce in vivo cancer physiology. On one hand, the conventional in vitro models fail to recapitulate the organ and tissue structures, the fluid flows, and the mechanical stimuli characterizing the human body compartments. On the other hand, in vivo animal models cannot reproduce the typical human tumor microenvironment, essential to study cancer behavior and progression. This study reviews the cancer-on-chips as one of the most promising tools to model and investigate the tumor microenvironment and metastasis. We also described how cancer-on-chip devices have been developed and implemented to study the most common primary cancers and their metastatic sites. Pros and cons of this technology are then discussed highlighting the future challenges to close the gap between the pre-clinical and clinical studies and accelerate the approval of new anticancer therapies in humans.

Indexed as

Cancer-on-chipMetastasisMicrofluidicsOrgan-on-chipPre-clinical modelsTumor microenvironment

Identifiers

PMID37592292
PMCPMC10436436
OpenAlexW4385968414

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.