ReviewCommunications biology2026
Next-generation osteosarcoma models for precision medicine.
Review in Communications biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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0 citing papers in PubMed.
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteosarcoma (OS) is a highly aggressive bone malignancy that predominantly affects adolescents and young adults. Despite the progress in conventional treatment approaches, such as surgery and chemotherapy, patient outcomes remain poor due to OS metastatic potential, chemoresistance and frequent recurrence. Although recent advancements in novel therapeutic strategies, such as molecular inhibitors, gene-based interventions, alongside immuno- and radiotherapies, have emerged in recent years, OS is still not well understood due to its complexity and heterogeneity. Tissue engineering and predictive preclinical models offer a good toolbox to study OS and produce patient-tailored therapeutic protocols. This review discusses the recent development of tissue engineering-based strategies and OS mimicking preclinical models like 3D in vitro models, organ-on-chip technologies, and predictive computational (in silico) models, ranging from mechanistic (white-box) to data-driven (black-box) and hybrid (grey-box) approaches. Based on these recent developments, researchers can better replicate the native OS tumour microenvironment, which opens the doors to a more representative high-throughput drug screening tools and patient-tailored treatment strategies.
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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.