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3D- and 4D-Printed Nano-Enabled Soft Robots for Cancer Diagnosis and Therapy: Materials, Manufacturing, Image Guidance, and Translational Validation.

Created on 26 Oct 2026

Authors

Ye Ri Han, Sang Bong Lee

Published in

Biosensors. Volume 16. Issue 10. Sep 29, 2026. Epub Sep 29, 2026.

Abstract

Three-dimensional (3D) and four-dimensional (4D) printing expand the design space for nano-enabled soft robots intended for cancer diagnosis and therapy. This structured narrative review separates three evidence classes: core systems satisfying all scope criteria, adjacent technologies satisfying only part of the definition, and enabling technologies not yet integrated as oncology soft robots. The revised evidence set contains 12 core reports, 7 adjacent comparators and 68 enabling methods, standards or background reports. Study-level analysis using independent manufacturing, biological, workflow and safety (M/B/W/S) axes shows that the core literature is dominated by single-platform, in vitro therapeutic demonstrations with absent or short-term safety evidence (S0-S1). No core study established cancer-specific diagnostic sensitivity or specificity, no human validation was identified, and only two systems were classified as genuine or candidate 4D platforms under a strict programmed-transformation definition. Hydrogel, magnetic, photothermal, conductive, imaging-active and melanin-based materials are evaluated together with printability, dose, localization, sterilization, retrieval, degradation and nanoparticle fate. Translation requires reproducible manufacture, quantitative application endpoints, exposure-matched safety testing and comparison with established oncology workflows. The principal gap is not the availability of additional functions, but the lack of integrated evidence connecting controlled manufacture to clinically meaningful diagnosis or therapy. These findings apply only to the studies identified within the predefined search and eligibility framework and do not establish the absence of relevant evidence elsewhere.

PMID:
42930093
Bibliographic data and abstract were imported from PubMed on 26 Oct 2026.

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