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Clinical Use of Fluorodeoxyglucose F 18 Positron Emission Tomography for Detection of Renal Cell Carcinoma
Abstract
Purpose We evaluate the role of fluorodeoxyglucose F 18 positron emission tomography (PET) in patients with renal cell carcinoma (RCC) by retrospective review. To our knowledge this series is the largest reviewing the use of PET in patients with RCC. Materials and methods A total of 66 patients who underwent 90 PET scans for suspected or known RCC were identified. Dictated reports of PET, chest computerized tomography (CT), abdominal/pelvic CT and bone scan were examined with confirmation of results by histopathology or followup of at least 1 year. The accuracies of PET and conventional imaging modalities were compared. Results PET exhibited a sensitivity of 60% and specificity of 100% for primary RCC tumors (abdominal CT demonstrated 91.7% sensitivity and 100% specificity). For retroperitoneal lymph node metastases and/or renal bed recurrence, PET was 75.0% sensitive and 100.0% specific (92.6% sensitivity and 98.1% specificity for abdominal CT). PET had a sensitivity of 75.0% and a specificity of 97.1% for metastases to the lung parenchyma compared to 91.1% and 73.1%, respectively, for chest CT. PET had a sensitivity of 77.3% and specificity of 100.0% for bone metastases, compared to 93.8% and 87.2% for combined CT and bone scan. In 39 scans (32 patients) PET failed to detect RCC lesions identified by conventional imaging. Conclusions The role of fluorodeoxyglucose F 18 PET in the detection of RCC is limited by low sensitivity. With superior specificity PET may have a comple…
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“or benign lesions and well-differentiated malignant lesions with low [18F]FDG avidity [122,123,124,125].”
From Influences on PET Quantification and Interpretation (2022) · cites this paper as [123]
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Metadata
- Authors
- KANG, D. E., WHITE, R. L., ZUGER, J. H., SASSER, H. C., & TEIGLAND, C. M.
- Affiliations
- Carolinas Medical Center (US)
- ORCID
- WHITE, RICHARD L.
- Venue
- Journal of Urology
- Published
- 2004-05
- Volume
- 171
- Issue
- 5
- Pages
- 1806-1809
- ISSN
- 0022-5347 (ISSN-L)
- 1527-3792
- OpenAlex
- W2103792773
- Cited by
- 305
- References
- 14
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 4 · 2023: 7 · 2022: 14 · 2021: 6 · 2020: 6 · 2019: 16 · 2018: 14 · 2017: 13 · 2016: 19 · 2015: 17 · 2014: 15 · 2013: 16 · 2012: 20
- FWCI
- 14.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- The Journal of Urology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Oncology › Renal cell carcinoma treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- renal cell carcinoma, tumor detection, diagnostic accuracy, sensitivity and specificity, retroperitoneal lymph node metastasis, lung metastasis, bone metastasis, abdominal CT, chest CT, bone scintigraphy
- MeSH terms
- Adult, Aged, Carcinoma, Renal Cell, Female, Humans, Kidney Neoplasms, Male, Middle Aged, Retrospective Studies, Tomography, Emission-Computed, Radiopharmaceuticals, Fluorodeoxyglucose F18
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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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.
