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Quantitative image analysis for evaluation of tumor response in clinical oncology

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2018

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Chinese Medical Association
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Cai, Wen-Li, and Guo-Bin Hong. 2018. “Quantitative image analysis for evaluation of tumor response in clinical oncology.” Chronic Diseases and Translational Medicine 4 (1): 18-28. doi:10.1016/j.cdtm.2018.01.002. http://dx.doi.org/10.1016/j.cdtm.2018.01.002.

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Abstract

The objective, accurate, and standardized evaluation of tumor response to treatment is an indispensable procedure in clinical oncology. Compared to manual measurement, computer-assisted linear measurement can significantly improve the accuracy and reproducibility of tumor burden quantification. For irregular-shaped and infiltrating or diffuse tumors, which are difficult to quantify by linear measurement, computer-assisted volumetric measurement may provide a more objective and sensitive quantification to evaluate tumor response to treatment than linear measurement does. In the evaluation of tumor response to novel oncologic treatments such as targeted therapy, changes in overall tumor size do not necessarily reflect tumor response to therapy due to the presence of internal necrosis or hemorrhages. This leads to a new generation of imaging biomarkers to evaluate tumor response by using texture analysis methods, also called radiomics. Computer-assisted texture analysis technology offers a more comprehensive and in-depth imaging biomarker to evaluate tumor response. The application of computer-assisted quantitative imaging analysis techniques not only reduces the inaccuracy and improves the reliability in tumor burden quantification, but facilitates the development of more comprehensive and intelligent approaches to evaluate treatment response, and hence promotes precision imaging in the evaluation of tumor response in clinical oncology. This article summarizes the state-of-the-art technical developments and clinical applications of quantitative imaging analysis in evaluation of tumor response in clinical oncology.

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Quantitative image analysis, Tumor imaging biomarker, Response evaluation

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