aceDeep Learning and Engineered Biomaterials‐Enhanced Imaging‐Guided Cancer Immunotherapy
ABSTRACT The clinical success of immunotherapies is hindered by the absence of robust methodologies for the early and accurate assessment of antitumor immune responses. Integrating engineered biomaterials with artificial intelligence (AI)‐driven molecular imaging technology offers a powerful approach for predicting immune efficacy. However, its clinical translation is significantly hindered by several critical limitations, including the complex and costly synthesis of multifunctional imaging probes, insufficient…
Combining engineered imaging biomaterials with deep learning image analysis is described as a platform for non-invasive monitoring and response prediction in cancer immunotherapy.
Clinical translation is held back by costly probe synthesis, limited tissue penetration, poor signal-to-noise ratios, toxicity risks, and deep learning models that generalize poorly across patients.
The source says clinical translation is significantly hindered by probe cost and complexity, tissue penetration, signal-to-noise problems, toxicity risks, and limited generalizability of deep learning models across diverse patients.
Evidence
- Peer-reviewedAdvanced Healthcare Materials2026-10-18
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Truvace Impact Record TRV-2026-1895, v1: “Deep Learning and Engineered Biomaterials‐Enhanced Imaging‐Guided Cancer Immunotherapy.” Truvace, 2026-10-19. /record/TRV-2026-1895 (accessed at citation time). sha256 56a80ab71cbf5842…
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