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Cited by 368

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

Tumor Ablation: Common Modalities and General Practices

Open access
Knavel, Erica M., Brace, Christopher L.·2013·Techniques in Vascular and Interventional Radiology

Abstract

Tumor ablation is a minimally invasive technique that is commonly used in the treatment of tumors of the liver, kidney, bone, and lung. During tumor ablation, thermal energy is used to heat or cool tissue to cytotoxic levels (less than −40°C or more than 60°C). An additional technique is being developed that targets the permeability of the cell membrane and is ostensibly nonthermal. Within the classification of tumor ablation, there are several modalities used worldwide: radiofrequency, microwave, laser, high-intensity focused ultrasound, cryoablation, and irreversible electroporation. Each technique, although similar in purpose, has specific and optimal indications. This review serves to discuss general principles and technique, reviews each modality, and discusses modality selection.

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Crossref · 10.1053/j.tvir.2013.08.002

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Metadata

Authors
Knavel, E. M., & Brace, C. L.
Venue
Techniques in Vascular and Interventional Radiology
Publisher
Elsevier BV
Published
2013-12
Volume
16
Issue
4
Pages
192-200
ISSN
1089-2516 (ISSN-L)
OpenAlex
W1972861065
Cited by
368
References
36
FWCI
7.16
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Tumor Ablation: Common Modalities and General Practices. https://app.cochat.ai/papers/10.1053/j.tvir.2013.08.002
The paper
Knavel, E. M., & Brace, C. L. (2013). Tumor Ablation: Common Modalities and General Practices. Techniques in Vascular and Interventional Radiology. https://doi.org/10.1053/j.tvir.2013.08.002

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.