ArticleRadiologie (Heidelberg, Germany)2026
[Imaging-based response criteria in oncology : Assessment of response to treatment in solid tumors and lymphomas].
Article in Radiologie (Heidelberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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4 authors.
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Abstract
The imaging-based assessment of response to treatment is essential for treatment planning and outcome prediction in oncology. Standardized classification systems enable reproducible follow-up assessments and improve comparability in the clinical practice and research. Depending on the tumor type and treatment approach different evaluation criteria and imaging modalities are applied. Metabolic information plays a central role in the assessment of hematological diseases and is gaining relevance in the assessment of solid tumors, which have previously been evaluated primarily based on their size. Additionally, the mechanism of action of the respective treatment should be considered, such as an expected size reduction after chemotherapy, altered contrast agent uptake after locoregional therapy or atypical response patterns, such as pseudoprogression after immunomodulatory therapy, which make repeated follow-up imaging mandatory. This article provides a structured overview of clinically relevant imaging-based response criteria and their specific applications.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.