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Cited by 337

The DOI matches its Crossref record; 106 of the 106 references it cites were found in a registry or an index.

Molecular imaging for cancer diagnosis and surgery

Open access
Hussain, Timon, Nguyen, Quyen T.·2014·Advanced Drug Delivery Reviews

Abstract

Novel molecular imaging techniques have the potential to significantly enhance the diagnostic and therapeutic approaches for cancer treatment. For solid tumors in particular, novel molecular enhancers for imaging modalities such as US, CT, MRI and PET may facilitate earlier and more accurate diagnosis and staging which are prerequisites for successful surgical therapy. Enzymatically activatable “smart” molecular MRI probes seem particularly promising because of their potential to image tumors before and after surgical removal without re-administration of the probe to evaluate completeness of surgical resection. Furthermore, the use of “smart” MR probes as part of screening programs may enable detection of small tumors throughout the body in at-risk patient populations. Dual labeling of molecular MR probes with fluorescent dyes can add real time intraoperative guidance facilitating complete tumor resection and preservation of important structures. A truly theranostic approach with the further addition of therapeutic agents to the molecular probe for adjuvant therapy is conceivable for the future.

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Metadata

Authors
Hussain, T., & Nguyen, Q. T.
Venue
Advanced Drug Delivery Reviews
Publisher
Elsevier BV
Published
2014-02
Volume
66
Pages
90-100
ISSN
0169-409X (ISSN-L)
OpenAlex
W2087594141
Cited by
337
References
106
FWCI
1.67
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Molecular imaging for cancer diagnosis and surgery. https://app.cochat.ai/papers/10.1016/j.addr.2013.09.007
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Hussain, T., & Nguyen, Q. T. (2014). Molecular imaging for cancer diagnosis and surgery. Advanced Drug Delivery Reviews. https://doi.org/10.1016/j.addr.2013.09.007

Data sources

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.