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Analysis of PET parameters predicting response to radiotherapy for myeloid sarcoma

  • Catholic Univ. of Korea Coll. Med.
  • The Catholic University of Korea Incheon St. Mary's Hospital

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Purpose Positron-emission tomography (PET)-CT has recently been used for diagnostic imaging and radiotherapy for myeloid sarcoma, but there is little research on predicting the response of radiotherapy. The aim of this study was to analyze the association between PET-CT variables and the response to radiotherapy in patients with myeloid sarcoma. Materials and methods This study was conducted in myeloid sarcoma patients who received radiotherapy and PET-CT before and after radiotherapy. The response to radiotherapy was evaluated based on the European Organization for Research and Treatment of Cancer PET response criteria, and binary regression analysis was performed to assess the factors predicting reductions in the maximum standardized uptake value (SUVmax). Results Twenty-seven sites in 12 patients were included in the study. Complete metabolic responses were seen in 24 patients after radiotherapy, a partial metabolic response in one, and progressive metabolic disease in two patients. The prescribed dose of more than 3000 cGy10 was significantly greater in the treatment control group (P = 0.024). In binary logistic regression analysis predicting reductions in the SUVmax of more than 70% after radiotherapy, the pretreatment SUVmax ( 7.5) and further chemotherapy after radiotherapy showed significant differences in univariate and multivariate analyses. Conclusion Good metabolic responses (complete or partial) to radiotherapy were achieved in 92.6% of the myeloid sarcoma patients. Radiation doses < 3000 cGy10 and increased SUVmax were related to treatment failure and high SUVmax before radiotherapy was a factor influencing SUVmax reduction. Further large-scale studies are needed.

Original languageEnglish
Article numbere0261550
JournalPLoS ONE
Volume16
Issue number12 December
DOIs
StatePublished - Dec 2021

Bibliographical note

Publisher Copyright:
Copyright: © 2021 Choi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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