Head and neck cancer as a model for advances in imaging prognosis, early assessment, and posttherapy evaluation
Cancer Journal, ISSN: 1528-9117, Vol: 17, Issue: 3, Page: 159-165
2011
- 10Citations
- 14Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations10
- Citation Indexes10
- 10
- CrossRef9
- Captures14
- Readers14
- 14
Review Description
Novel noninvasive functional imaging methods are necessary to predict therapeutic outcome and thereby improve the ability to properly select patients for treatment with both conventional and targeted therapies, to better evaluate therapeutic effectiveness during the early phases of treatment, and to enhance a priori risk assessment for treatment induced toxicity. Functional metabolic imaging typically involves pretreatment baseline magnetic resonance imaging (MRI) and/or positron emission tomographic (PET) scans and performance of subsequent scans during and/or after treatment. Imaging parameter changes are routinely attributed to the intervening therapy and clinical outcomes subsequently correlated with these changes. The physiologic parameter(s) that best correlate with clinical outcome and the relative utility of MRI versus PET are unknown, however. Furthermore, tumor vascular physiology and metabolic parameters are heterogeneous and dynamic processes. Large daily fluctuations often occur in the absence of treatment. The magnitude of this temporal variability is not established for MRI or for PET. Routine and meaningful clinical application of functional imaging requires understanding and quantification of the intrinsic variability of the underlying biologic processes and a demonstration that treatment-induced changes exceed intrinsic temporal variation. © 2011 Lippincott Williams & Wilkins, Inc.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=79958819674&origin=inward; http://dx.doi.org/10.1097/ppo.0b013e31821e8a09; http://www.ncbi.nlm.nih.gov/pubmed/21610469; http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage&an=00130404-201105000-00004; https://journals.lww.com/00130404-201105000-00004; https://dx.doi.org/10.1097/ppo.0b013e31821e8a09; https://insights.ovid.com/ShowUpgradeBrowserMessage
Ovid Technologies (Wolters Kluwer Health)
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