[18F]-FDG PET and MRI radiomic signatures to predict the risk and the location of tumor recurrence after re-irradiation in head and neck cancer - Archive ouverte HAL Access content directly
Journal Articles European Journal of Nuclear Medicine and Molecular Imaging Year : 2022

[18F]-FDG PET and MRI radiomic signatures to predict the risk and the location of tumor recurrence after re-irradiation in head and neck cancer

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Abstract

Purpose: To evaluate whether radiomics from [18F]-FDG PET and/or MRI before re-irradiation (reRT) of recurrent head and neck cancer (HNC) could predict the occurrence and the location "in-field" or "outside" of a second locoregional recurrence (LR). Methods: Among the 55 patients re-irradiated at curative intend for HNC from 2012 to 2019, 48 had an MRI and/or PET before the start of the reRT. Thirty-nine radiomic features (RF) were extracted from the reirradiated GTV (rGTV) using LIFEx software. Student t-tests and Spearman Correlation Coefficient were used to select the RF that best separate patients who recurred from those who did not, and "in-field" from "outside" recurrences. Principal component analysis involving these features only was used to create a prediction model. Leave-oneout cross-validation was performed to evaluate the models. Results: After a median follow-up of 17 months, 40/55 patients had developed a second LR, including 18 "in-field" and 22 "outside" recurrences. From pre-reRT MRI, a model based on three RF (GLSZM_SZHGLE, GLSZM_LGLZE and skewness) predicted whether patients would recur with a balanced accuracy (BA) of 83.5%. Another model from pre-reRT MRI based on three other RF (GLSZM_ LZHGE, NGLDM_Busyness and GLZLM_SZE) predicted whether patients would recur "in-field" or "outside" with a BA of 78.5%. From pre-reRT PET, a model based on four RF (Kurtosis, SUVbwmin, GLCM_Correlation and GLCM_Contrast) predicted the LR location with a BA of 84.5%. Conclusion: RF characterizing tumor heterogeneity extracted from pre-reRT PET and MRI predicted whether patients would recur, and whether they would recur "in-field" or "outside".
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inserm-03872913 , version 1 (26-11-2022)

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Arnaud Beddok, Fanny Orlhac, Valentin Calugaru, Laurence Champion, Catherine Ala Eddine, et al.. [18F]-FDG PET and MRI radiomic signatures to predict the risk and the location of tumor recurrence after re-irradiation in head and neck cancer. European Journal of Nuclear Medicine and Molecular Imaging, 2022, Online ahead of print. ⟨10.1007/s00259-022-06000-7⟩. ⟨inserm-03872913⟩
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