Multi-Modality Imaging for Prediction of Tumor Control Following Radiotherapy
Image-Guided High-Precision Radiotherapy, Page: 271-283
2022
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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.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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
- Captures2
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Book Chapter Description
Multi-modality imaging techniques, such as hybrid systems combining positron emission tomography (PET) and magnetic resonance imaging (MRI) or computed tomography (CT), provide various possibilities to acquire quantitative imaging biomarkers of tumors. Several studies have shown the potential of such quantitative imaging biomarkers to predict tumor control following radiotherapy. In this chapter, an overview on technical requirements of multi-modality imaging systems, which need to be fulfilled to qualify potential biomarkers for response modeling, is given. Furthermore, examples of quantitative imaging biomarkers from different imaging modalities, which were shown in recent studies to be predictive for tumor control after radiotherapy, are discussed. The ultimate goal of training prediction models based on multi-modality imaging parameters is a potential future use of such models to steer personalized radiotherapy concepts using individualized prescriptions for, e.g., dose painting.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85169381910&origin=inward; http://dx.doi.org/10.1007/978-3-031-08601-4_12; https://link.springer.com/10.1007/978-3-031-08601-4_12; https://dx.doi.org/10.1007/978-3-031-08601-4_12; https://link.springer.com/chapter/10.1007/978-3-031-08601-4_12
Springer Science and Business Media LLC
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